And then yeah, start expanding the front end, make it usable, make it look beautiful and


Udacity Deep Learning Nanodegree Language Translation Project Submission | 100 Days of Code 10

hopefully offer some value to people.


But that's all I've been up to today.


I'm about to record a podcast.


Not entirely sure what I'm just yet, but you'll be able to find it on my YouTube channel or


my podcast channel.


I'll link both in the description.


And yeah, I think this might be the last clip for this vlog.


So I think it's the end of vlog nine.


And yeah, we'll catch you next week.


Next week, I'm still working on Udacity deep learning course.


What's up y'all?


Day 44 of the 100 days of code series.


As you just saw, I just finished my most recent machine learning assignment.


I think it's week eight, assignment seven.


Still learning MATLAB, it's all new and that sort of stuff.


I much prefer Python, even though I'm still not that great at Python, but it's just my


language of choice for the time being.


And to be honest, I'm just not a fan of the MATLAB interface.


And plus, it's a paid license, so it's not open source.


So if you wanted to start using MATLAB now, you'd have to pay for it unless you have an


education license, which is what I have for 12 weeks, I get it for free through the Coursera


machine learning course.


But I think that's the majority of the code I'm going to do for today.


I want to do some planning on my own data science master's degree that I'm going to


create.


So if I make any progress on that, I'll let you know, of course, because over the next


six months, I want to do some sort of data science master's degree moving towards the


field of AI and machine learning.


But otherwise, I might do some Python review.


I haven't done a review in a while, so that'll be later this afternoon.


But we'll catch you in the next clip.


What's going on y'all, day 47 of the 100 days of code series.


Sorry I haven't filmed the last two days.


I spent Sunday, well it is Tuesday today, I spent Monday doing some writing, no sorry,


Sunday doing some writing.


I've been dedicating a lot more time to writing lately.


And the last two days, well yesterday, I was just really deep in doing this Udacity Project


4, which is building your own language translator from English to French, but only on a very


small amount of words.


If you wanted to do the whole vocabulary, you would need a lot of compute power, which


I don't have at the moment.


But good news, it is 425pm.


And I just submitted my project.


So hopefully it goes all good.


I would have spent about at least, I don't know, six to eight hours worth of work on


it today.


I started work at about maybe 10am, probably a bit earlier.


And then the same again yesterday, it takes me a lot longer to do these projects than


the average person because I'm still learning, I'm still in such a basic state of all this.


I've got a lot to learn.


I've got a very uphill battle over the next six months.


Oh, well, longer than that.


It's a continual learning process.


But I'm excited.


That's gonna be it for today in terms of code.


I just built a language translator.


As you can see, I'll show you what the output was.


Where is it?


We translated this sentence here.


He saw an old yellow truck to French.


There we go.


Not 100% correct.


But that's all right.


Better than zero.


It knows more French than I do right now.


So that's really cool.


Tomorrow I'm going to be learning Python on Treehouse.


SimVLM explained | What the paper doesn’t tell you

Hi, it's official!


The Google Brain team joined the pre-trained vision and language models business.


Their new model called SimVLM uses a simple training procedure and promises generative


capabilities in generalization and zero-shot behavior when it comes to tasks combining


images and text so you can ask the model things by just alluding to what can be seen in an


image, like where to observe this animal and the model just answers to this open-endedly.


If you're curious about what the authors say about how SimVLM works, about what they


don't say and how this compares to previous vision and language models, grab a cup of


coffee and join us for this AI Coffee Break.


We have known Google research from text-only models like T5 or vision-only models like


VIT, but now with SimVLM they propose a visual language model.


Well, prior literature called these things vision and language models, but sure, they


are Google, they are allowed to be visionary, right?


If you're not yet familiar with vision and language transformers and want to get the


prerequisite knowledge for this video, make sure to check out at least this one previous


video where we explain how this is done.


Or rather was done, because this paper here criticizes that the first vision and language


models did not process the raw image, but features extracted by an object detection


backbone like the FasterRCNN.


And then of course the problem is that the transformer sees the image through the looking


glass of the FasterRCNN and you know, if that looking glass is cracked or blurry, that might


bottleneck the whole procedure.


But some of the latest architectures like CLIP or VILT do process the image end-to-end,


but the authors of SimVLM decided against highlighting this too much and rather do not


mention this at all.


In any case, SimVLM gets rid of the FasterRCNN backbone and looks at the image like VIT does,


or almost, because they find that the linear projection of VIT should be rather replaced


by three blocks of ResNet to extract image patch vectors.


So sure, unlike most of the previous work on vision and language models, these weights


are learned and updated.


The text is tokenized and represented through word vectors, and the image vectors and text


vectors alike, all endowed with positional embeddings, are all blended and mixed together


by a transformer like they wouldn't even be part of different modalities.


But remember, the only difference to previous work is that the image vectors are now extracted


by a ResNet that trains with the whole transformer, while the original vision and language models,


but not the most recent ones, used a whole pre-trained but then fixed FasterRCNN backbone


for this.


Okay, cool.


Moving on to the next contribution of the paper, the authors further highlight shortcomings


of existing work in that it uses multitask learning by formulating multiple losses, like


masked language modeling, where words are masked and the model now informed also with


the image has to predict what a masked word should be, or the task of image sentence alignment,


or masked region classification and others.


But SimVLM does not need all this.


It can get away with one single objective, which is the so-called prefix language modeling


objective.


Most vision and language models out there use the masked language modeling loss, where


a BERT-based vision and language transformer processes text following the idea of a denoising


autoencoder.


The original sequence is altered by randomly masking some tokens and the model must predict


what these masked tokens had originally contained.


But these masked language modeling based models are not really language models in the classical


sense because they can't really compute the joint probability of the tokens in a sentence


by autoregressively factorizing the probability.


You know, that is when you are applying the chain rule over and over.


And SimVLM wants to be like a language model because they have the advantage that they


can open-endedly generate text, while they rely on context and not on fine-tuning for


zero-shot capabilities as seen for example with GPT-3, where one can show some examples


as context at the start of the sequence and GPT-3 can continue it by picking up the pattern.


But now SimVLM wants to generate text also given image tokens as context.


So the authors use something similar to the autoregressive modeling the GPT family does,


prefix language modeling, where the autoregressive factorization does not happen on the whole


sequence but only at the end of it.


So the image for example acts as a prefix or context and then the autoregressive factorization


from left to right happens only on the text.


Of course this prefix can extend on the text tokens too, if the goal is for the model to


continue a piece of text that has been already written.


With this, SimVLM processes the prefix much like a masked language model, bidirectionally,


but can perform unidirectional open-ended text generation on the rest, like a real language


model.


And this is cool, because this is how SimVLM can do things like this and complete a sequence


of an image and a question by answering that question.


What I understand so far from the paper is that this autoregressiveness delivers these


great zero-shot abilities seen here, because SimVLM is like GPT-3 but now with text and


image.


But Ms. Coffee Bean, this makes no sense, because GPT-2 is autoregressive in the same


way GPT-3 is, and while GPT-3 has zero-shot and few-shot capabilities, GPT-2 doesn't


really have these impressive features.


Then what is the secret ingredient here for SimVLM?


What did we miss?


Let's take a step back.


What we understand from this paper so far is that all you need, sorry for this, I cannot


actually hear all you need anymore, so all you need, but wish you didn't need, is a


vision and language transformer that processes the image without a huge and stiff preprocessing


step beforehand and a model that is generative, at least on the text part.


With this, SimVLM beats state-of-the-art on standard vision and language tasks like VQA,


NLVR2, SNL-IVE image captioning.


Wow, right?


It makes us wonder why nobody came up with this simple idea before.


Ms. Coffee Bean, why are you stopping me?


Did I miss something?


Oh, yes, I did, the data.


I mean, of course, SimVLM must be pre-trained on lots and lots of data first, like every


other model beforehand too.


So this data is, we cite, a large-scale, weakly labeled data set, which has better potential


towards zero-shot generalization.


What does this last part even mean?


I don't know.


Let's go to this dedicated data section.


So they are using, we cite, a large-scale noisy image text data, which has better potential


for zero-shot generalization.


Specifically, we use the image and alt text pairs introduced in GIA and collaborators


in 2021, which are crawled from the web with minimal post-processing.


Ah, so these are images and captions.


Certainly, not like the 330,000 MS Cocoa image text pairs, because MS Cocoa has images and


well-written crowdsourced captions.


Maybe it's something more like the 3.3 million sized conceptual captions data set that contains


images and alt text captions, but curated.


Or maybe something as large-scale as OpenAI's crawled image and alt text captions it used


for Clip, right?


So the simple architecture of SimVLM and this data section completely explain the superior


results of SimVLM over everything else they compare to.


Ms. Coffee Bean, why are you poking me?


You tell me that I should look for the amount of data that this mysterious GIA and collaborators


data set contains?


Okay, sure, I mean, it will be something similar to these numbers of the related work, right?


What?


You are telling me that we are speaking about over 1 billion image text pairs?


But Ms. Coffee Bean, the number billion or any number indicating the amount of data samples


is not anywhere in the SimVLM paper, I had to check another one.


And large-scale does not necessarily mean 1 billion, hundreds of millions were still


considered large-scale the last I checked.


Okay, so if the amount of used data is not a thing to put in the abstract and everywhere


else in the paper, I don't know what is.


So you're telling me that they are training on 1 billion data samples and beat the state


of the art, but don't even mention the data amount and advertise the architectural simplicity


of end-to-end image processing and that they need just one loss instead?


But it is exactly this, the data aspect that is the clue to everything.


We need to start from the billion images and then explain what we can do with that.


If we have 10,000 more data samples to go than Alex Mert had for pre-training, then


of course we do not need as many losses and the heavy multitask learning procedure that


Alex Mert needs.


Alex Mert has to compensate the lack of data with more thorough supervision, so losses.


If we have 1000 times more data than Wilbert had during pre-training, then of course we


have more freedom to learn our own image representations flexibly instead of employing the inductive


biases of other architectures like the FastRAR-CNN and not even train them further.


Ms. Coffee Bean, get me out of here, I am too mad.


What I mean to say by this?


I hope to see a better framing of the advances of SimVLM in a future version of this paper.


I hope to see more emphasis on the enormous importance of the data.


Choosing architectural details, inserting or getting rid of inductive biases heavily


relies on the amount of available training data.


It's not like nobody before reading this paper never thought about removing the FastRAR-CNN


backbone or getting rid of all the losses making optimization overly complicated in


most vision and language models.


But the authors of SimVLM thought about this and could afford doing this and did all the


heavy lifting to get all the tiny details right, so that this beast can train in parallel


over many GPUs and converge.


They have done a great job and they have written a great paper.


But it becomes clearer and clearer how hard it is to keep making good science in machine


learning.


In a scientific experiment, in order to show the gains of a component with respect to the


state of the art, that component and that component only should be changed and the rest


should be kept fixed.


In this case, it is nice to see this simpler architecture with no object detection backbone


and the simplified training procedure, but this is also combined with bringing up a lot


more data than the related work had and we do not see at all how these choices behave


in the smaller data regimes previous architectures had.


Especially because nobody else has the 1 billion image text dataset the authors here has, to


conduct the missing experiments showing how the proposed architecture behaves with respect


to increasing amounts of data, it is even more of the authors' job to conduct these


comparisons and show where the performance gains in SimVLM really come from.


So I'm just calling for more honesty when highlighting the paper contributions and where


the gains come from.


That's all.


In any case, it's an archived version of the paper anyways, where maybe getting the


first version out was a rush to mark the territory or who knows, I'm sure we will see an improved


version soon.


Thanks for listening until the end, even if it got really ranty.


Now get out of here and read the paper and the blog post linked below in the description


yourself.


They are so clearly written and visualized.


See you next time.


It's my birthday! Come and ask me anything! (livestream party)

Excellent connection actually.


We're going live. We're going to start dancing.


Dancing, romancing. It says we're live.


Beautiful. Hello everyone.


So I'm very excited. First of all,


we're celebrating. We hit, the channel recently hit


14,000 subscribers.


13,000 as well.


I want to celebrate both the subscriber count


not that that means too much to me but I love you all.


But mainly here just to chat with you.


I want to spend some time on my birthday just having a


live stream birthday party. And you know what?


I got a little cake here. Can you see it?


There we go. So this is from


this is from the local cafe and


we went down there because it's Father's Day as well so happy Father's Day


to all the fathers out there. And the local


cafe, I go there a lot to do writing and get coffee


and whatnot and they said when we had breakfast


and they're like would you like a piece of brownie for your birthday?


And I'm like of course I do.


So we're going to start off the stream with a little bit of


birthday cake.


Is this going to light?


Hey!


Don't play with fire inside kids. I do not condone this.


If you're watching this live stream and you like


sparklers and you like cake well be careful


when you're using it inside. That's why we're using a sparkler.


Hopefully I don't set off the smoke alarms inside.


Thank you Sid.


I really appreciate it. Thank you CryptoPedol.


Thank you GalaxyGwen. Thank you Ravi.


I appreciate it. Again if you're


here I'm happy that you're here.


If you want me to answer one of your questions feel free to ask me


a question. I'm going to hang around for about the next hour or so


and answer some questions while I eat this brownie.


The sparkler's almost out. Happy birthday to me


and drink this coffee.


So we're going to have a good day. And you know what's also


exciting? Where I live in the world


it's the first day of spring. So winter is over.


I really like the heat. I really like


summer. And so my birthday, 1st of September


marks the first day of spring in the southern


hemisphere. So I always felt special as a kid when that


happened. When it was the first day of spring


I'm like yes that means it's getting warmer and I'm


a special little boy because I'm born on the first day


of spring. But we're going to dig into this brownie.


Oh my goodness.


Oh I bet that brownie tastes amazing.


That is phenomenal. Phenomenal.


Thank you. Thank you for the


kind wishes everyone. I really appreciate it.


We have a question.


Hello. Thank you my friend. I really appreciate the birthday wishes. But you said I'm from Nigeria and I want to ask you if I can get you to tell me how I can start with deep learning with no programming experience and how I can get a job online that's going to provide money to buy a laptop.


OK. Well I can help with the first one.


So with no programming experience that's how I started right.


So about two, two and a half maybe.


Let's say two years ago the start of 2017.


Well maybe that's that's almost three years ago now because we're almost through 2019.


But you can get started with deep learning.


I would start learning Python programming.


Why. Because relatively I don't want to say easy because everything is going to get hard eventually.


But Python programming is in terms of programming languages it's probably one of the best to get started with.


Right. And a lot of machine learning a lot of deep learning is done. I do all of my deep learning machine learning work in Python.


So get started first of all using Python and then learn a bit of deep learning with Python.


And then the hard part then is being able to tell people or showcase to people your skill set.


Right. So how can you do that.


So if you've been learning some deep learning stuff or if you've been learning some Python stuff I would highly suggest you starting to share your work.


And how you can do that through GitHub through your own projects through blog posts.


And if you want to get in touch with with someone who's or a company that's offering a deep learning job.


I'm a big fan of going straight to the target. Right.


So bypass bypass the application or maybe don't bypass it but apply through the job website.


But also go direct find the one person who you should be talking to to get a role or to get hired or something like that.


So say for example when I went for when I wanted to get a job at Airbnb.


There was a cutback but I went straight on LinkedIn found out who the recruiters were and messaged them on LinkedIn asking for their advice.


How I would apply for the job. And then they said hey we've seen your experience.


Do you want to send us your resume. And I'm like yes that would be amazing.


I sent them my resume and then I got a phone call interview. I didn't end up getting the job.


But that is my advice in terms of going direct to the people who are going to offer you the job.


Right. So first of all build some skills. Second of all showcase those skills.


Third of all go straight to the target of people who are offering those jobs.


Now how do you do all the steps in between. I'm going to leave that to you to figure out because I think it'll be very valuable.


If you if you figure out these things on your own because you'll use those skills wherever you go.


Let me know by the way if the stream audio is great or the video is great because I'm kind of just doing this on the fly as we usually do.


And YouTube's given me a warning the stream's current bit rate 1691 is lower than the recommended bit rate.


So hmm. OK. Programming with Sid. Actually I'm going to take another bite of brownie.


So we're going to we're going to work this live stream. We'll go question brownie question brownie or we'll just do chatting.


By the way if you're new here welcome to the channel. I really appreciate it.


If you're joining in this is the first time you see me live. I do this every month.


So feel free to ask me a question whenever you want. I usually respond to email or YouTube comments.


And at the end of every month or the start of every month like today I do a live stream where we just hang out for an hour.


Sid. I have a question for you. How do you get so much done. Don't you get distracted when working at home.


How do you stay on the grind while avoiding wasting time on nonproductive things. Amazing question Sid.


So I haven't always been like this right. So I do get a lot of things done. You're right.


One of the main things. Let me show you what I use. You've probably seen this before.


I have a notepad. So this is usually every Sunday I'll write something down like this.


I'll start with the date. See there's a week. So this is the start of the week end of the week.


And then I'll have all these things here of what I want to do through the week.


So there's Monday Tuesday Wednesday Thursday Friday right. And so usually on a Sunday which is like today I'll plan out the week ahead and I'll go OK.


These are the things I want to get done in the week. And as you saw not everything gets done right.


So there's still there's still boxes that haven't been ticked on here. And so we go over here.


Birthday live stream was one of the ones I wanted to get done. OK. And so not all the time everything gets done.


But at the start of the week I will write down a list of things that I want to do throughout the week.


And at the start of each day I will do the same thing. So then I have another notepad.


So I have two main notepads at the moment. One is day to day and one is week to week.


So as you see top of the day and then I have a list of things.


And as you see not everything gets done right. But here I write the task of what I need to do.


And then down here I write some notes. So that is basically my planning in a nutshell.


I also have a Trello board. You may have seen that. I wonder if I can show you that.


Let me see. I also have a Trello board which I kind of keep some longer term projects going.


I want to just show you how I organize things. And again this is not a perfect system right. But it works for me.


Now how do I stay productive on all these things. The reason why I write things down is because I basically do everything on the list and nothing else.


And of course there will be times where I have different things that I have to do. So look at this. This is an absolute mess at the moment.


But you can see here I have on hold. I have to do. I have to do. I have these are the things that I am doing.


They are currently in progress. Too many things as always. That is my weakness. I have too many things going on all the time.


And then I have a list of things that I have done. So you can see I have done this. I have used this kind of schedule for May 2017. Over two years.


So what I will do if I have an idea I will put it in the to do. And then if I have something I want to do in the month.


And the fact that I did a four day fast. I am doing a few more other things here.


And I put whatever I am currently working on in the doing column. And then once it gets done it goes into the done column.


So for example I would make one here for September. This is going to be what I have done in September.


Which we will put here September live stream. Right. So once this live stream is over this will be. We will actually put this here.


And it gets a YouTube tag. I could probably make a full video on how I actually do this if you want to see.


But this is essentially my system at the moment. So my ideas go in this to do column.


My monthly things that I want to do this month go in the to do for the current month.


So this is going to be September. And then whatever I am doing say I want to do this will go in here.


So I am going to make a video on data science resources for beginners. That is a spoiler alert.


I will put that in the doing column. And then once it is done it will go in the done. So this card will actually probably be done this month.


Well it will be because that is what I want to do. But yeah that is kind of my system.


And as I said I wasn't always like this right. When I was in university I failed. Let me jump back to. There we go.


When I was in university right. I failed my first two years of university because I was very lazy.


And most of that was because I was studying something I wasn't really interested into.


And so the formula I have been following since failing university for two years.


I spent five years at university to do a three year degree.


Like my degree was only supposed to take three years. But I took five because I failed.


Right. Failed the first two years. I almost got kicked out of university because I was doing so poorly.


And I would get home. I would sit on the couch. I remember watching shows like Antique Roadshow and Ice Road Truckers and all those things on Discovery.


Just because I had no energy because I was using all my energy to study something I didn't want to.


So when I finally worked out that I could study the things that I was interested in.


I had all this energy to work on that. And so right now I'm really fascinated by machine learning.


Fascinated by writing. Fascinated by making videos. So all of my energy is dedicated towards that.


And it wasn't always like that as you saw. But it's kind of I've built that up over the last five or six years.


So it's taken a long time. But it was worth it because now I'm able to do things like writing a whole list down and working through it.


So if you could do one thing I would say try at the start of every day write down two to three things.


Doesn't have to be a massive list like my list is probably too long right. But start write down two to three things.


Doesn't have to be massive. Just two to three things and practice every day doing a task.


And again it doesn't have to be every day either. You could take take weekends off but just try that system.


Start small and then work out what works best for you. But if you want to see a video of how I actually plan things out like in depth.


Let me know and I can probably work on that for you.


Hopefully that answers it Sid. But if you have any more questions let me know.


Ravi says many more happy returns the day. Thank you very much. I really appreciate it.


We're going to take a sip of water and then have another bite of brownie. Birthday brownie.


This is a good brownie. I'm going to have to let the girls know they've done a phenomenal job making this brownie.


Where are we at? Ravi can you suggest some websites where you can learn competitive coding skills?


Well I've never done competitive coding. But where I practice my coding is on hacker rank.


And I think there's a programming cake or something like that. I'm pretty sure it's actually called programming cake.


No interview cake. If you look up interview cake. There you go.


So this is also another resource that will send you an email of different coding problems every day or every week.


Where you can practice your coding skills. So I would check out something like this or hacker rank.


At the end of the day competitive programming and coding is different to actual coding.


So in a competition you might have time constraints and you want to try and optimize everything.


And you're just building things and they're rough. But in real life you're going to take time building your projects.


You're going to be looking at stack overflow. There's not going to be a really set path of how to do things.


You're kind of making it up on the fly. So yeah practice competitive coding to get really good at coding and thinking out thinking fast.


But remember it will probably be different to things you're working on in a production environment.


But then again I've never done competitive programming.


George Hots is probably a really good person to look up to because he does a lot of YouTube live streams of coding sessions.


But it won't teach you competitive programming.


Galaxy Gwen asked what was my favorite birthday party?


Hmm wow. I try to have a little holiday every year for my birthday right. So it falls at the start of spring.


So the last three or four years we've been going to the beach. Me and my friend. I'll show you this beautiful photo.


We actually slept. He's got a van that you can sleep in the back of.


And so to celebrate the night before we stayed in his van and watched the sunrise.


So let me find this photo for you.


And I think look it's recency bias. So this one might be my favorite one yet.


And photos isn't loading because I think it's got OBS running.


My computer is just lagging.


My poor little MacBook Pro.


One second. I'll get this photo for you.


And if I can't well we shall continue the stream anyway.


Come on photos.


Maybe I'll stop recording.


Because it tries to stream and record at the same time.


OK photos isn't loading for some reason.


Anyway we'll do that another time.


Maybe I'll post it on my Twitter or something.


All right I need to come back to the live stream. Here we go.


But anyway OK so my favorite party.


I really like going going to the beach.


That is where I am most happy near water.


So I grew up near water.


If you walk this direction from where I live.


If you walk like five ten minutes that way you'll be at you'll be at the water.


And so I try to spend as much time as I can near the water.


I try to go for a walk every day.


And if I'm around people that I love and I'm near water well then it's a good day for me.


So that's that's how I like spending birthdays.


But as for party my 21st birthday was a pretty good party.


We had a bucking ball set up in the backyard and we had a giant party bus come and pick us up.


And we went and had a night on the town and it was just a really good time.


But at the end of the day I've worked out that the party isn't as good as the people that are there.


So as long as there's good people as long as you're surrounded by people you love and people that love you.


It's hard to be in a bad place and I'm very lucky to have friends like I do.


I'm very lucky to be able to to talk to you all like I do.


It's a good birthday right. It's a good birthday.


Today is the best birthday because it's the first time I've done a live stream on my birthday.


Let me turn the fan off.


So I hope that answers your question Quentin.


I really wish I could have shown you that photo but for some reason my computer is going very slow.


OK here we go. Santos happy birthday. Thank you Santos.


Carlos hey you're from Peru. Nice to meet you my friend Carlos.


Happy birthday. Thank you very much Mr. Transits. Happy birthday. Thank you.


Programmer Sid a video on this would be amazing.


Might be very well. Sid let me put that down for you right. I'll show you. Here we go.


Here's how I would do it.


What we got here. So I put the video tag.


Video and I'll put your name so I remember.


Programming with Sid.


How do I manage to do so many things.


Productivity themed video.


So yes it's on the list right.


I'll make it nice and nice and quick just some tips that you can you can help organize some of the projects that you're working on.


Oh I haven't seen Leak Code before maybe I start practicing.


I really want to improve my code but I found the best way I improved my coding skills is by just coding right.


So I work on projects.


I try to what I'm trying to do now more so than do more courses is I want to start building longer term projects.


And so I've got I've got a video coming out shortly on an end to end machine learning projects.


This is a spoiler. Actually I'm not going to say anything more I'll just let it come out.


So stay tuned for that.


But if you're just joining in we are here we are having a live stream birthday party.


It is my birthday. I turned 26 today and the channel also just clocked over about 14,000 subscribers.


So welcome if you knew I really appreciate it.


Ask me any question that you may have.


I do one of these streams about once a month and so we're just here to have some fun.


Ask me about machine learning. Ask me about life. Ask me about health. Ask me about whatever.


Say happy birthday or just yeah just enjoy the stream.


OK. One more bite of brownie and then we'll get into the next question.


Mmm.


That's actually really.


Miguel says happy birthday Daniel. Thank you very much.


Miguel programming was said.


Daniel ever tried using ML or AI in space or to make something that might be useful in space industry.


I haven't.


I haven't right. So.


And space.


No I'm trying to think of what could be used.


I mean there's a lot of space debris.


So maybe navigating through space debris could be an AI problem.


Maybe a computer. Well I suppose they already are right because they have to map it.


Maybe it's not a maybe it's not an ML problem more so an optimization problem trying to work out the best route like mapping like on Google Maps.


Right. And so no.


But yeah if you do something work on something with ML in space let me know.


Code at black and I'm sure SpaceX and all that sort of stuff are using it somewhere.


Right. Maybe I did see NASA used evolutionary algorithms to evolve an antenna.


That's not really ML but if you could think about that.


So NASA evolutionary algorithms antenna.


And today I felt antenna wrong.


And yes. So look at this evolved antenna.


So this is this antenna was designed.


And involved antenna is an antenna designed for fully or substantially by an automatic computer design program that uses an evolutionary algorithm.


Right. That mimics Darwinian evolution.


So this is an antenna that was an evolutionary algorithm was used to design this for the best best reception in space.


Right. So the complicated shape was found by an evolutionary computer designed to create the best radiation pattern.


So if you are a human looking at that that's like just a weird shape.


But apparently that created the best radiation pattern.


So evolutionary algorithms not exactly machine learning but but kind of in that realm.


Right. That's I think that's pretty cool.


And OK. Rohan. Happy birthday brother.


Thank you Rohan. I appreciate it.


Revante says Java is a robust and secure language.


Java is the best.


I've never used Java but of course it is amazing if it if it stayed around.


So here we go. We've got.


Can you do ML and HTML.


And I think HTML is more of a markup language.


So look I'm going to say I don't know but I don't think so.


Right. Because it's more just to design the way I understand HTML is more just to design the schema of a web page or the way things should be laid out.


Rohan says Happy birthday. Thank you very much.


Siraj says Happy Born Day. Thank you very much.


My nickname is also born because my last name Burke autocorrects to born on iPhones.


So apparently I skipped over.


So C++ sized dick says hey you skipped over my question asshole.


Well sorry man. I didn't even see it.


What were you studying. Oh there we go.


I did. What was I studying in college at university was just following a girl that I had a crush on and she was like I'm studying this and I was like OK I'll study that as well which was biomedical science.


I'd say like the degree that you start off with before going into medicine and I was like yeah I'm going to be a doctor.


This is going to be great. I'm so cool.


I'm going to be a doctor or whatever which is awesome but it wasn't for me.


And so I was really just following following a crush on girls.


Girls had control of me back then.


Not much has changed.


But yeah no that's what I was studying and then I changed to food science and nutrition which is why I'm eating brownies now.


And then after that I got top grades in food science and nutrition but I was always interested in technology.


So after university I studied Japanese and Mandarin because I was interested in technology.


No I studied languages for a year and then switched.


I learned to code in 2017 and now we're here.


Brownie time.


We've all been there. You've got to chase love at some point.


Love makes the world go round right.


This is a classic cliche.


Yeah how good is this cup?


It's a mason jar with a handle and it is the most hipster drink.


Look at this shirt. I suit the personality type right.


It's my birthday. See this is coffee. This is just iced coffee.


I make a mean coffee now.


Filter coffee. There's a little brand called Padre and yeah it's amazing.


So Mahendran.


I like your display picture Mahendran. I think that's an Indian cricket player.


I really like cricket.


Happy birthday Daniel. What is your go to book for statistics?


I'll be honest with you I don't have many off the top of my head that I can list.


The way I learn statistics is kind of...


See the thing with machine learning right is you kind of don't need the theory until you do.


So if that makes sense.


You can get a lot done with code until you kind of need the theory to back up your code right.


So the way I kind of learn things is ad hoc.


So when I'm working on a project and I get to a point it's like oh I might need a test set before it's actually legit.


Then I'll start to look up the statistics.


But one that really got me a conceptual understanding of statistics is called Naked Statistics by Charles Whelan.


I also studied statistics in university so that was kind of a bonus when I did statistics as part of my science degree.


So as for a book I'm not entirely sure.


There is the Elements of Statistical Learning which people have recommended.


I've never read it before.


But if anyone has an idea if they want to recommend a statistics book please leave it in the comments so you can help my friend Mahendran out.


Revent says can I say am-dee out loud.


Am-dee out loud.


Did that work?


We're almost finished this brownie so I want to get rid of it so I can just talk and not be distracted by this delicious goodness.


This is gluten free, organic or something brownie.


But look it's a brownie.


No grass in it.


It's just a delicious brownie.


So we have the BOW says happy B-day Daniel.


Thank you so much.


What is the difference between taking class on Audacity versus Cora for ML?


I mean sorry.


Audacity I think that's Udacity versus Cora for ML.


They're very similar but in terms of aesthetics like in terms of the way they do things.


Udacity's classroom is probably the best that I've ever used.


So in terms of the way they have their like student dashboards and mentorship and all that sort of stuff is probably the best I've used.


But Cora is not very far behind right.


So they're very what matters most is rather than the platform itself is the skills that you're learning.


So they're very high quality.


I would say go with the one that you can that best suits your resources.


Therefore not way more.


Look whatever a bit more expensive than Coursera.


However it really depends right.


Does Udacity have the things you want to learn on it.


Beautiful you might want to choose Udacity.


Does Coursera have the things you want to learn on it.


Beautiful they might you might want to learn a Coursera.


If you're comparing two of the similar ones you might want to do a bit more research and research each program like independently and figure out which one suits you best in terms of price and timeline.


These are things you're going to have to factor into your decision right.


I found personal value from both of them.


If I had to pick one I would probably pick Coursera.


But that being said Udacity is still phenomenal.


To be honest I haven't used Udacity in about 18 months and I know they've done a lot of upgrades.


Coursera I've kind of jumped in and out of every couple of months.


So I would say do your research but they're both very high quality.


Here we go.


Mr. Transits are these special brownies.


No no they're not special brownies they're just delicious brownies.


Rajan happy birthday bro from Nepal.


I already wished you on LinkedIn.


Thank you very much.


Thank you Rajan.


I really appreciate it.


I'm messing things up here.


Joe why do you think many people are afraid of AI?


What risk of artificial intelligence imposes on humans in the future if any?


Well that's a deep question.


Brownie first.


Maybe a special brownie would help answer that question.


So look I think the reason why many people are afraid of AI including me sometimes like even though I know what it's doing it still like surprises me.


So if you look at it what are humans afraid of?


The one basic thing is the fear of the unknown.


So anything we don't know we kind of make reasons up to try and explain it right.


And of course there's a lot of smart people saying AI is going to reach human level intelligence in X amount of years and other people saying no it's not going to reach that level of intelligence in X amount of years.


The truth is you really we really don't know as much as we've predicted the future as much as we can try as much as we can assume as much as we can.


At the end of the day who knows right.


We don't really know.


That's like as well like just if you're not even just artificial intelligence just in terms of like why are we actually here.


The reason is kind of you could spend all day thinking about it and you'll kind of just keep getting deeper and deeper into the wormhole.


But at the end of the day the reason is just there is no reason it's just because.


So tying this back into why are people so afraid.


It comes back to just that unknowing reason.


The fact is there is no reason or there is no actual answer as to what AI will actually do right.


OK we know we know it might get to human level intelligence but the thing is what is human level intelligence and what is general intelligence right.


So humans we are really biased right because we try and imagine something smarter than yourself.


I just try and think of it.


So if you look at us and monkeys right.


Monkeys are 99 or 98 percent of our DNA but yet we are so much more intelligent than said monkeys.


But what what scale is that on right.


If that if that scale is in that 2 percent difference of DNA try and think of something that's 2 percent different ahead of us right.


And so you can you can you can think about this and saying oh if we had all the all the compute power in the world to combine and all human knowledge and all that sort of stuff.


Try and imagine that scenario right.


It's very hard for a person to.


Can you imagine something with your level of intelligence that's more intelligent than you are right.


Is it picking up what I'm putting down.


Maybe I can't explain this well enough in words but the main reason is just the fear of the unknown.


I think the artificial intelligence see the way economy economies used to work is via materials.


So if your country had the most material goods you had the biggest economy.


Now the economy has shifted to information and information right.


You don't need physical space whereas materials like coal oil steel etc.


You needed physical space to be able to store it.


So now with information you don't need much storage space right.


You could have data warehouses but what we live in now a knowledge economy which is where all of this this artificial intelligence gains come from.


You watch technology companies they use technology.


It's all knowledge right.


So 10,000 engineers at Facebook can build a platform for 2 billion 3 billion people around the world.


And so where the risks are going I think is giving giving people right up the top and this is capitalism in a nutshell right.


But I think if you give and this is the danger in the AI race like the AI race.


If you give one entity too much control of all of the knowledge right without anyone else being able to access that data or access that knowledge.


Then you're sort of you're sort of governed by how that company sees right or how that that entity sees right to use that information.


So one part of me I am a big fan of capitalism right it enables us to do labels me to do a lot of the work that I do.


And on the other hand it's kind of if one one entity or two entities get too out of control with in terms of knowledge they have.


Right they can build the artificial intelligence which is just using all the information relating it statistically they understand you better than you understand yourself.


That can kind of get and again I don't know the answer right but that can kind of get I can see where where that can kind of become an issue.


So that was kind of a long-winded answer to just expanding the fact that a lot of people are afraid of AI because of the fear of the unknown including me right.


So I don't know where AI is going to be in 50 years I'm excited to see what happens but I think it's got to be a global game.


So it can't just be Australia is going to do better at AI or America is going to do better at AI or China is going to do better at AI.


Intelligence is a global problem right so that's why it's so beautiful with people like Siraj Raval and YouTube channels and people just educating others on how they can use data.


To bring value to the world and I think a real real niche important point coming coming through is how individuals take control of their their data right.


So rather than you handing over your private information which is kind of kind of weird me saying this on YouTube because Google collects a lot of data.


But how can individuals take control of each each portion of their data right.


So if I go online how can I make sure that whatever I do is I own what I do not some other company that can trace every one of my actions and then use that to increase their power over me and over others.


So that's a that's another big point that will probably come through over the next couple of decades as AI just increases into or seeps its way into everything which it kind of already is doing.


That's a long-winded answer but great question Joe great question maybe we can discuss that at length in another video or a podcast or a post or something like that.


We're going to finish up the brownie.


Oh my goodness we're done I'm kind of sad.


I'm kind of sad.


But it was worth it happiness is in scarcity.


If you have too much of a good thing it doesn't become a good thing anymore.


So Sid says what made you choose Y Combinator Startup School and how do you make sure you're not money wasting money on courses.


I chose Y Combinator Startup School because they've got a great reputation they've got great information I've seen their videos on YouTube but I've kind of stopped it.


Why because I was doing it and I realized I'm doing too many things I'm listening to all these lectures when I kind of know what I want to do.


So I was kind of using it as an excuse right to do you notice this right.


If you if you start to realize what it is you want to do you'll unless you go straight towards it you'll notice all these obstacles that you just start creating for yourself.


Because you know what you want to do it's kind of counterintuitive right.


It's like I know I wanted to build this thing but I was throwing up all these distractions for me so that I didn't actually have to do the thing that I knew I had to do.


So this is another one of my weakness but it is something that you can notice almost straight away because inherently I think a lot of people know the thing that they want to be doing or know the thing they should be doing.


But there's a good reason and a real reason for basically everything.


The good reason I was doing Y Combinator Startup School was because I wanted to learn more about building a startup and doing it right and all this sort of stuff.


But the real reason was I was distracting myself from actually working on my startup.


So I chose Y Combinator Startup School because it's amazing but I've kind of stopped it instead I'm going to spend more time building up just just building up my business just doing it instead of trying to figure out how to.


I kind of I think I know what to do.


So is that is that dumb of me to maybe make mistakes.


Look I don't I don't mind right because at the end of the day I think that's how I learn best.


I've just worked it out. I learn best by just doing things.


And how do I make sure I'm not wasting money on buying courses. I do my research right simple as that.


I research I think about how should I spend my time.


Do I want to buy this course. Do I not. If I buy it gives me one small little skill that I can use for decades to come.


Well it was worth the investment. Same with books.


I've got a stack of books over here. If a book cost me thirty dollars and I learn I remember one sentence from one page for the rest of my life.


Well then that was a good investment.


And so here we go. Hope that answers your question Sid.


Rev Ant says Did you know that Verma intelligence can supersede artificial intelligence in the near future.


I'm not even even sure what Verma intelligence is to be honest.


Verma intelligence.


I'm going to Google that. Google is going to track me.


Well it didn't really come up. Anyway.


I've watched some of their videos. They're pretty legit. Yep. They know what they're doing. Put it that way.


Mr. Transits did you see Lex Freeman latest podcast with Yann LeCun.


No I didn't. Yeah I must check that out. I love Lex's podcast.


I listen to George Hots. I listen to the one with Chris Latner.


It's a phenomenal resource world that we live in that we can have access to such experts and I feel really lucky to be able to get this information from such people.


Neural says seen data quest for learning. Yes data quest is also amazing data quest data camp.


I'm big fans of both of them. But again there is a lot of resources out there.


My advice is to always choose the one that suits you best. How do you do this.


You do your research. That is a big part of being a data scientist or machine learning engineer is to do your research and make a decision and experiment.


Roman says happy birthday from a recently joined subscriber. Thank you so much Roman.


I appreciate it. I hope you find some value from this channel. I have a joy making videos and I really I really appreciate you subscribing.


So what kind of math is the most valuable in your work. Well definitely linear algebra right because and matrix manipulation that's basically all of what deep learning and machine learning comes down to an optimization functions.


So very hard to explain it all in one hit.


But I would say if you want to look up a book that can do a way better job because I am a code monkey my math skills aren't as good as someone you would see machine learning researcher right.


I do things with code. I don't do as much math as you would think. All of the work I do is mostly in code.


I do have some understanding of the math but I couldn't work it out by hand for you.


But yeah look up ML math for ML book. That's a free book on GitHub and probably all of that is the best for machine learning.


But again I'm all all about code so I'm a bias towards if you want to get something running just get it running right in machine learning you don't need the theory until you do.


We have Akhil. Happy birthday Daniel. Thank you very much Akhil. I really appreciate it.


Any insight on ML and analytics in the field of bioinformatics. Yes this is a big one right.


So I do I do kind of want to get into the field of well I actually I do want to get into the field of bioinformatics.


But it's another problem where it's like where do I start. I did start going through Coursera bioinformatics specialization.


It kind of it lost me because it wasn't going through like I couldn't relate it to anything I was working on real life.


That was a big trouble and it was kind of more dedicated towards like if you want to if you're a researcher like and I'm more of a I want to build things in real life.


So maybe I need to revisit it and kind of work things out. I'm trying to I'm building an MVP in an air table of where I want to start using machine learning in with biological data.


Like I'm tracking my blood ketones and all that sort of stuff blood glucose levels and how different foods influence my different body parameters like different sleep different weight or that sort of stuff.


And I'm starting to realize maybe it wasn't a thing that I want to really work on.


So who knows. We'll see. We'll see. We'll see where it goes.


But as for where we can use ML in bioinformatics I have a book called Deep Medicine.


It's not exactly bioinformatics but it does have a lot of stuff of how artificial intelligence can make health care human again.


I would suggest checking out that book for an overview of the state of AI in health care.


But otherwise if you find out any bioinformatics resources let me know.


The Coursera specialization is probably the best resource online at the moment though that I've seen.


So there may be better ones out there.


Sebastian says Happy birthday Daniel.


Thank you very much Sebastian.


Same with Natesh.


Thank you Natesh.


Ravin happy birthday.


Don't you have actual people to spend it with.


I do Ravin.


I went to breakfast this morning with my family.


It was amazing.


And I had dinner last night with family and friends.


So it's been a big weekend of birthday celebration and I'll probably have dinner again tonight with my family.


See the thing is that that is the best birthday present I can ever have is just having it.


That's one of the things I love to do most in the world is just have a meal with people I love.


That is simple.


That is how I like to try and do that every day.


And I think it's valuable for people to do that every day.


Sai says Happy birthday man.


Thanks for the great work.


Thank you so much Sai.


I really appreciate it.


I love that wintery smiley face because winter is over.


At least where I live it's beautiful and sunny and it is now spring.


Look at this.


Can you see that outside.


Look at that sun.


BOMB.


Big summer on tour.


I'm excited for summer.


I love summer.


OK well spring and summer I love them both.


And so oh a very exciting thing.


I need to tick off this whole list because you see this right.


This is create every day and write every day.


So that poster there is I cross out one of those every single day of the year where I write.


And I mean there's you see I haven't crossed out half of it because I've just built the habit.


That means I've done a blog post every day for the last year except for today.


If you look at this.


Scroll down.


August 31 August 30 August 29 August 27 August 27 August 26 25 24.


If you keep scrolling on my blog Mr D Burke dot com slash blog you will find that there has been a post every single day for the last year.


So make sure you check that out right.


If you like reading articles on machine learning on health about life about whatever you want to work on go to my blog read every single post they're all free and there's a lot of them.


And there's plenty more to come and there's going to be one today and it's going to make it a year in in a row of a post every single day.


And what's the lesson there.


Well if you want to learn how to write if you want to learn how to make videos if you want to learn how to code.


Don't forget the power of compound interest practice a little bit every day you don't have to practice every day but I find I do it best when I practice every day.


So. So I. You're one of the reasons I've restarted my study of I discipline again.


Thank you. Well so I that's amazing.


Good on you my friend. I really appreciate that.


I get I get demotivated too sometimes but it's good I have such an amazing community to see all the work you guys do which motivates me as well.


So Carly says facing hard time applying object detection algorithms using custom data set any resource or tips or object detection.


I would check out pie image search dot com depends on what kind of object detection you want to do.


Custom data sets can be a bit fiddly but that is that is the fun of the project right is if you can do it on your custom data set while you're going to learn a lot because you're doing something that probably hasn't been done before.


So I says what business are you running.


I make things on the Internet. So I write articles I get paid by medium.


I am partnered with Coursera and data camp with affiliates.


So if someone signs up to a course and pays for it I get money back if they click through my link.


Amazon also have an affiliate program which I am partnered with.


I get money from YouTube ads which is not not very much.


The main source of my revenue is through affiliate links at the moment.


And I also I also partner with places like a cloud guru and data camp and make courses for them and get paid for that.


But I am moving more towards a product eyes model.


So I want to build products and sell those products.


So what will that be more so in the digital space probably in terms of digital asset whether that be a book whether that be an article whether that be videos whether that be courses whether that be.


I really want to get into just like software products.


Right. That's what I'm going towards.


It's a long term game. I want to keep working for myself.


I don't plan on applying for another company another role.


I'm going to keep working on myself.


Keep writing keep coding keep making videos.


But that is how I make money through content at the moment.


And Mike what do I think about basic income.


I think you mean Michael.


Sorry I think. Do you mean universal base income.


I'll probably answer it for that.


I think it's very valuable but I also think it's also good to it's look it's one of those things right.


It works. It works for a lot of people might not work for some people but I think it would be amazing if you could add in some way for example like show the world that you're putting in value but you're not necessarily getting paid.


Straight away.


But you did not have to worry about whether you had money for food that week.


Right. So for example when I was first making YouTube videos and writing articles I didn't get paid.


Nothing zero dollars for a long time.


It took me like two years to get money from YouTube or writing articles.


But so I was working jobs which I'm fine with right.


I really I like it. I like having it that way because I had something to work towards.


And now that I do make money from it it's like I want to keep keep going with it.


I don't want to lose it because I'm not going back to getting another job.


That's just no no no option for me.


But if someone at the start could show the world hey I'm making this thing of value for the world whatever it may be right.


Whether it's you're making things online or you're making little little arts and crafts or something like that something that brings value to the world.


And that you could get something let's say a number like a thousand dollars a month to pay for your food and basic living expenses.


Right. Now is that enough. I don't know.


Right. You could fiddle with the amount until it works out to be enough but I think that would be a great resource.


The thing is you would have to us if you just give people like a handout because then they get used to not having to trade effort for for value.


And I mean maybe some people want to live that way right.


That's totally up to them. But I think it can get dangerous right.


As you you see kids raised in a family where they've had everything given to them and all of a sudden they experience some sort of hardship.


And it's it's they can't deal with it because they're just used to having everything in abundance.


But I do think in a sense that if you can show the world that you're making something and somehow get get a little bit of value so you don't have to worry about food and living expenses while you're building up.


That would be very valuable. That's that's my thoughts on it.


So we have years says Happy birthday Daniel.


Thank you very much my friend. I really appreciate it.


Hush it says Happy birthday brother.


But what is that shirt.


Man this is my party shirt.


This is my birthday shirt.


We're going to get a little dance going right.


This is a giant. This shirt is actually from the 1970s.


It's it's it's rocking and rolling and it's fresh.


It's the first day of spring.


Every man's got to have his own style.


Every man's got to be a little fashionista.


You know what it pays to it pays to look good.


Michael says that's definitely a birthday shirt.


I agree. I agree.


And Vigette says how much do I make as a machine learning engineer.


When I was a machine learning engineer at a tech company in Brisbane I earned between fifty five and seventy five thousand Australian dollars.


The salary wasn't something I was after.


I was more after the learning.


Right. So I learned a lot.


That was the most important thing.


I learned enough to be able to come out and start running my own business.


I probably could have done it without the job but I did learn a lot on the job.


Right. And so my living expenses are because I live in Brisbane at home in with my parents in this converted garage.


Can you see that that where those doors are that used to be a garage.


Maybe I'll do a room tour.


If you'd like to see a room tour of my of my garage space let me know.


So my living expenses are very low so that I'm I'm technically rich right because I earn more than what it cost me to live.


So if I lived in another part of the world like San Francisco or New York I would need a big salary because my living expenses is so high.


But I've done a lot of effort.


I put in a lot of effort to minimize.


So the basic things I don't spend money on anything except food rent and like subscriptions to technology services that I use to build my business.


So they're my three main expenses and that's what makes me happy.


And that is all I really need is like a laptop to create and I'm good.


I'm good.


So that's how I want to live life.


My living expenses is just small less but better.


And Kumar off topic.


Are you excited for Rick and Morty season four.


You know what I haven't seen Rick and Morty I've seen like probably half a dozen episodes but not all of it.


I should probably start watching it because if Elon Musk likes it maybe I should too.


But I do it I do like it.


I didn't know season four was coming out.


Can you watch it on Netflix or something.


I have Netflix so that's why I haven't seen it.


Maybe I need to get YouTube premium or something and watch it on there.


Esteban says Hi bro.


How are you doing with your project Esteban.


Depends which project.


So let me show you what I've got for what I'm tracking at the moment for my personalized nutrition experiment.


So this is kind of like an MVP of what I'm trying to do is work out what I should eat.


And I just ate a brownie when I'm not I haven't tracked that yet so it's kind of inconsistent.


See this is the hard part of kind of worked out that doing this tracking for every single like every single half an hour of a day is a lot of work.


So I need to work out.


Maybe I need to get into some engineering and how to figure out how I can track these parameters automatically and have them load in here.


At the moment it's a lot of manual tracking.


So this is basically what I'm what I was thinking of doing is I read the book The Personalized Diet and what they did was they tracked a bunch of parameters and then tried to predict how a certain food.


We got here let's look at this how a certain food right.


So if I put this down this is mixed berries that's almond butter that's coconut yogurt.


How would this food impact.


So in the morning let's go to morning this is on the 17th of August morning this is when I woke up.


I had a lot of sleep that day which is great.


I slept really well.


My weight was seventy one point six kilos and we go across my ketones was zero point five.


I was still in ketosis at that stage and my blood glucose level waking was six point one.


So relatively high there compared to what the average is.


So if we go here the average is five point one.


Right.


So if I work out what's the standard deviation of that.


What essentially this this personalized nutrition experiment is trying to do what I'm trying to do is work out what foods can I eat to minimize the fluctuation in my blood glucose to maximize my energy to keep my hunger levels low and keep my mental clarity high.


So that is basically the problem I want to do.


So is I want to figure out what can I eat.


So take a photo of it or something like that or whatever it is figure out what's what's in this meal.


How will that affect my blood glucose.


Can I predict this.


Can I predict what food will maximize my energy levels.


As you can see I'm usually pretty high energy and keep my hunger levels low because if I'm feeling hungry I can't work on the things I want to work on and keep my mental clarity high.


And these are these are qualitative metrics.


So they're kind of biased as well right because I'm pretty positive guy so I'm probably going to usually rate high on all of these.


So I've kind of this is my basic MVP that I'm using just to see what it would be like to track data and then the long term goal.


See this is a pretty big glucose spike right seven point nine.


The average is five point one.


Let's get the standard deviation here.


Standard deviation zero point eight.


So if you imagine anything maybe two standard deviations I need to work out what the what the curve looks like what the distribution looks like of my blood glucose levels.


So I think seven point nine would be more than two standard deviations away from five point one.


Well it is.


And so this was the meal that I had.


So we got mixed berries.


There was a lot of tomato in here.


So I think that was a lot of sugar in the tomatoes to probably raise my blood glucose from there.


And if you look after that I had pretty low energy right.


Mental clarity probably because my blood glucose was so high my body was in a fed state and I was releasing a bunch of hormones to sort of put me in a relaxed mode to digest all the food that I'd eaten.


So who knows I'm tracking more data.


I want to get about a thousand of these records and then start doing data analysis.


Put it into some sort of app and see see if I can pull anything out of it.


But that's a long story cut short on how my project is looking.


So yeah I'm kind of working out that I may maybe don't want to track so many things all the time.


So that's the value of building an MVP right.


Something simple like a Google Sheets.


If I want to work on using machine learning for something I would start with using Google Sheets or something like that to track different stuff which I have and then see if I can use data analysis on it to figure out the insights.


I hope that answers your question Esteban.


My project on personalised health is in the I guess you could say prototype stage to track data because again the big point the big point of being able to use insights is having a good good way to track data.


So we got Muhammad happy birthday Dan.


If I want to start my own machine learning data science business where should I start from any online resource in the subject.


Well Muhammad it really depends on what you want to do.


So I make I make content around machine learning and data science.


So that's what you could potentially say that's a machine learning data science business.


It's not necessarily I'm consulting people on their data.


I did have a few people contact me but I was trying to get out of that consulting space right.


Because as I said I want to work in the product development space I want to build products on my own and not just work on on on less things but better things.


But how we worked at my old business was Max at my old work was Max Kelsen was we partnered with with companies and enterprise and that didn't really have data scientists or machine learning engineers in house.


And we offered to offer offered out that machine learning data sciences science services to them.


Well I can barely speak it's been like an hour.


We offered our services to them if they wanted to see if machine learning could help their business.


So it's like a consulting firm.


That's one way you could do it.


But otherwise I don't know if there's any online resources and how to do it.


You could try freelancing you can try a whole bunch of things.


If I would say try some stuff posting stuff on LinkedIn and asking if people have done it before because at the moment my specialization is making content rather than then actually starting a data science business.


So gum says happy birthday.


Thank you very much.


So gum gum.


How our graduate program is shaped machine learning.


I don't know to be honest gum.


I never did a graduate graduate program.


I just studied myself online and that's probably how I'm going to.


Well that is how I'm going to continue doing it.


I don't plan on going back to university and I'm playing getting another job.


I plan on building building a living for myself by creating things online.


Shake says happy birthday my man.


Thank you brother.


Really appreciate your support.


So proud of you for crushing it.


So proud of all of you.


Like a bulldozer says happy birthday.


Chidon says happy birthday.


Thought you might be older.


No I'm 26 26 today born at 946 a.m.


It was first of September 1993.


So at this moment I am about 26 years ago I'm about three hours old.


Thank you little Daniel.


All right.


We're gonna we're gonna do a few little.


One or two more questions and then we'll wrap this up because it's just hit about an hour and I think that's a good amount of time.


I've had a good birthday party live stream.


If you guys enjoyed this let me know if you have any more questions I can answer them in a future stream or if you want to contact me directly.


You can do that at my email Daniel at MrD Burke dot com.


But I think it's time.


We do a little little party dance and we'll wrap this stream up.


So we got Vijeth says happy birthday man you're a big inspiration for me.


Thank you Vijeth.


I really appreciate it.


That makes that inspires me.


Right.


So this is how the circle works.


I make things because I'm inspired by the work I see from you guys and you guys get inspired by the work I do.


So it's it's a circle right.


We help each other out.


Excessive says happy birthday from an aspiring ML engineer.


Thank you so much Excessive.


Drew says do you still lift weights.


Have you ever done any martial arts.


Drew I do still lift weights and I do do martial arts.


I just signed up to jujitsu.


Well not just I've been doing jujitsu for almost a year now.


But here's my two weights of choice right now.


We got a sledgehammer.


I have a big rubber tyre in the backyard and so I just whack.


Oh shit.


Be careful handling sledgehammers near computers.


So I just swing that around and I have a kettlebell.


So basically this is a this is a 20 kilo kettlebell.


I could probably get one a little bit heavier and one a little bit lighter and that would be my complete set.


But I have one kettlebell.


I have a big sledgehammer whose name is Gloria.


I hope you can see that Gloria.


And this is what I do right now.


So I whack I whack a big tyre in my backyard.


I just cancel my gym membership.


I have a pull up bar in my backyard.


I could start making workout videos if you wanted to see them.


Like I've been thinking about different like quick 20 minute workout videos that you can do as a study break for beginners right up to more advanced stuff.


I've been getting deep into calisthenics and I do basically a lot of body weight stuff like animal movements.


And so that will be my weightlifting sort of routine.


Hill sprints and that's not so much weight but a lot of body weight stuff is what I do.


And kettlebells of course.


And I'm also doing a bunch of gymnastic rings.


So I have these.


I set these up in my backyard.


Hang them up on a tree at the park.


And I do a lot of weight workouts with that.


Body weight workouts with that.


But I am a white belt in Jiu Jitsu.


I have two tips.


I've had five fights and I have lost four.


I've won one on armbar submission and lost three on points and lost one on armbar submission.


So body weight workouts in the backyard.


But yeah.


So I hope that answers that question.


I do a lot of running too.


Ran 10k yesterday morning to celebrate the last day of winter with my best friend.


So yeah.


I do both.


PJ, hello, good afternoon.


Hello PJ, nice to see you my friend.


Thanks for joining in.


Najendra, happy birthday.


Thank you so much.


Santosh, Jack, my boy.


Thank you.


Appreciate it.


You were there from the start Jack.


I remember your name my friend.


Mint Chill, good morning.


How are you Mint?


Good morning.


Esteban says, thanks bro.


When are you going to launch it?


When are you going to launch a ML course?


Good question.


Stay tuned.


Something coming soon.


Also not exactly in terms of...


I'll just keep it at that.


You'll find out when it comes out.


Santosh, do you watch cricket?


I do.


I love it.


I love cricket.


I used to play cricket.


I was a leg spin bowler like Shane Warne.


So I was...


I could turn the ball really far if I landed it on the pitch.


Mr. Trans, it's mad respects.


Daniel, stay Daniel.


You got it bro.


I totally agree.


I'll stay me.


Where to start machine learning?


Mint Chill.


I would say look up Daniel Burke.


I want to start machine learning where to start.


That'll be a good post for you.


All right.


We're going to rock and roll.


This has been an amazing time.


So Mitchell Moore, happy birthday from Canada.


Thank you Mitchell.


Really appreciate it.


Just picked up the 100 page ML book.


Loving it.


Glad you're enjoying it my friend.


I really enjoyed it too.


I spoke with the author Andre and he's a really good guy.


Drew is a white belt in BJJ.


Woos, woos my friend.


Maybe if we meet in real life we can have a few rolls together brother.


I'm really loving it.


So we're going to keep going.


Jack, love you too.


Swapna, we'll do one last.


Any best machine learning type to apply in mobile security?


Happy birthday.


I'm going to be honest with you Swapna.


I don't know the answer to that one.


How to best apply to mobile security.


I'm sure there's a lot more smarter people on the internet that can be able to answer that for you.


That's always what I'll try to do right.


If I don't know something I'll tell you.


If I do know something I'll try my best to transfer my knowledge to you.


But as for mobile security I'm not the best person to talk to for that.


Chummy says happy birthday.


And if I could make it tell us how you learn things.


How do I remember everything?


How do I take notes?


Let's leave that for a future video.


Great idea.


Because it is an important topic on how to learn things and how to take notes and whatever.


So we will look at that in a future video.


But otherwise thank you all so much for joining.


If you're new to the channel thank you so much for tuning in.


This has been a birthday live stream.


We will do another one in about a month's time.


Of course it won't be my birthday then.


But I'm going to enjoy the rest of the day.


It is the first day of spring.


It is my 26th birthday.


I'm going to have a blog post to write because that will be the final blog post for the year.


Well not for this 2019 but 365 days in a row which I am really excited for.


A year.


Every day for a year I've done a blog post.


Maybe I could do that with videos.


That would probably be a bit harder.


But yeah.


Have a great day.


Enjoy spring.


Tell your family you love them.


And as always keep learning.


Keep creating.


I didn't eat food for 5 days, here's what happened

Last year I went for four days.


This year I decided to do a five day water only fast.


I mean, there was some salt in there and a few magnesium tablets, but we'll get into that.


And so what this video is, is kind of at the start, it's answering some rapid fire questions over what happens during a five day extended fast of just water only.


And towards the back end or actually the majority of the video you'll see in the timestamps below.


Is kind of like a day by day journal style video diary documentary of my first extended fast or first five day fast.


Last year I did, as we said, I did four days.


So this was one more extra day.


A big preface to this video is what you're about to see is just anecdotal stories of my experience doing a five day fast.


I'm not a doctor.


This is not medical advice.


If you think about starting your own extended fast, you may get some ideas.


But of course, if you have any concerns, you're smart enough to know that you should do your own research or consult with a medical professional before starting any of your own health endeavors.


There is also an article to go along with this video.


So if you want to check that out, if you prefer to read because the video is fairly long, check that in the description.


It's got basically all the same information that we're about to go through.


So that being said, let's start on some questions.


Why did you do a five day fast?


Well, aside from the plethora of health benefits that science is only really just starting to to unearth about fasting, I just wanted to see what it was like as an experience to not go just to go five days without food.


See, if you had told me three years ago that I would be like almost completely normal functioning human being after not eating for five days, I would have told you to get lost.


Maybe maybe a bit more aggressive than just get lost.


So it was really just to see what it was like to go without food for five days, not for weight loss, or any take care of any gut issues, just to see what it was like how how I would feel after not eating for five days.


How did you start the five day fast?


So I decided to go from Sunday night dinner to Friday night dinner.


And so Sunday night dinner, I had a relatively low carbohydrate. So it was just chicken and greens and some almond butter.


I mean, I have almond butter almost with every meal. It's my favorite food.


And so I did that because I wanted to I read somewhere that it's you should probably have a low carbohydrate meal for your last meal.


And then next day, so you wake up. So that's going to be Sunday night had last meal before starting the fast.


So Monday morning, I went for a really long walk about 90 minutes, maybe a bit longer.


Wasn't wasn't too like intense. It was just just a walk chilled out walk.


And that was to kick start the body burning excess glycogen stores because one of the which is a fuel source.


One of the hardest parts about a fast is the switch over between your body using glycogen as its primary fuel source to using ketones,


which is once you start using ketones you into ketosis. Once you get into ketosis,


it's sort of like you're on cruise control for the fast. It's really hard to explain, but that's that's kind of how it felt.


Once my blood ketones were elevated, I didn't feel hungry at all. And yeah, I felt good.


How much weight did you lose during the fast? So got some average weights here for each day.


So the reason why it's average because I weighed in a few times per day.


Usually I wake up in the morning and weigh in first thing after waking up.


So go to the bathroom, take a leak, weigh in basically naked.


And then I would weigh in again at lunchtime or just whenever I go to the bathroom to wash my hands because I mean,


you just stand on the scales and see what happens. And so, of course, during the day as I was drinking water,


not eating anything, but so I'd weigh in really light in the morning or lighter than usual.


And then as I had water, my weight would increase through the day. So these are just averages of what happened.


So day one, I was 74.8 kilos, which is 165 pounds. Day two, 73.3 kilos. Day three, 72.9. Day four, 72.6 kilos. Day five, 71.8 kilos.


And day six, so one day after the fast, I broke the fast, I was 71.4 kilos.


And then day 12, so one week post the fast, I was 71.8 kilos. So I started relatively like heavy for me.


I'm usually hovering around 70 to 73 kilos. So 74.8 on day one. And so by the end of the fast, I was 71.8.


So down three kilos. And even now, two weeks past, I haven't put that weight back on. So maybe I got rid of some excess fat, excess water weight.


I'm not entirely sure. What did you consume during the fast? I had water, water with sprinkles of salt,


like just regular table salt and just sprinkle it in water or sea salt. I didn't measure it.


I would just crack the salt cracker a few times. Sparkling water and then magnesium tablets.


So I had two just standard magnesium tablets you can buy off the shelf. I just had two, which is the recommended dosage on the bottle.


Two of those before bed on nights two, three, four. So only three out of the five nights. Yeah. That's the idea of a fast, right?


You're not consuming anything. Did you exercise during the fast? Yes. Every morning I went for a slow, below stressful walk in nature,


usually with my dad or with my friends or with my younger brother. That's basically how I start every morning.


So I during the fast, actually, I wasn't trying to change anything. I was just trying to just do what I would normally do,


except not eat food. That's it or abstain from food. As for I also did weightlifting.


So on the night of day one, I played touch. So I usually play touch on Monday night.


So I went to that touch football. That's about 40 minutes of running around. Day two,


I did a weightlifting session and Brazilian jiu-jitsu session for the weightlifting and Brazilian jiu-jitsu sessions.


I was a little slow out of the gates, but once I found that I got sweating or the blood was pumping, I felt incredible.


I mean, the pump is the cure, right? Day three, I did nothing except to walk.


So I rested and then day four and day five, I did a weightlifting session as well as Brazilian jiu-jitsu session.


And as for the weightlifting sessions, it wasn't anything massive. I was probably doing about 70 to 80% of the volume I would usually do.


So about four to five exercises in about 20 to 25 minutes maximum.


I wasn't trying to push anything or do any personal best. I was just going in there to get a pump and get out.


What was the hardest part about the fast? Well, I started the fast with a friend of mine and a little brother.


And so the idea was we're all just going to do it for the five days. This is a little bit of a spoiler for what's to come.


But my friend decided he'd had enough by about halfway through day three.


And then my little brother decided he'd also had enough by the end of day three.


So massive effort to them. Of course, it's perfectly fine at any time if you want to end the fast.


But I had decided I had told myself that I was going to go the full day.


So there was no chance and I was giving in. But it was like it was playing around with my head.


The fact that my friend and my little brother had stopped and I was just over halfway.


So I knew how much longer I had to go. And yeah, I wasn't doing it. I was on my own.


But that was another benefit of it is that it's like a little bit of a challenge.


And so once I got over that, I had a nap and then just woke up and it was fine. I continued on.


Also, the other difficult part was it was the only thing I really missed was the ritual of mealtime.


So one of my favorite activities in the whole world, or probably this is top three, maybe top two,


is to sit down and have food with family, have food with friends, or just have food after, like as a reward for finishing a heart training session or doing some study or work or whatnot.


And so that was probably the only thing that I missed was just the ritual of eating food and enjoying food with others.


How did you spend time during the fast?


Now, you probably don't realize or I certainly didn't realize it.


It really makes it clear is that when you're not eating at all, you start to find how much time you actually spend around eating.


And so what I mean by that is like you shop for food, you prepare food, you sit down, you eat food and you clean up after that.


And if you're doing that potentially multiple times per day, that ends up being a lot of time.


So during a fast, because you're not doing any of that, you wind up with a lot of free time.


So I found myself just having had naps during the day, like if I got hungry or tired, which I didn't really get hungry, actually.


But I found myself just having like an afternoon siesta.


Usually I do a weight training session, probably midday-ish, because that's when I usually eat.


I usually only eat lunch and dinner during the day.


Like, so that's my regular like sort of eating schedule, eating pattern.


I find that just works best for me.


And so when it came to around lunchtime, when I would usually eat, I would go to a workout, come home and then just have an afternoon nap and chill out for a bit.


So I had plenty of time, but most of the time in that plenty of extra time, I was either doing nothing or just doing what I would usually do.


Writing, coding, reading, studying.


How were your concentration levels during the fast?


As I mentioned before, I was trying to keep everything just as the same as I would usually do.


So I didn't really notice a major drop in concentration.


If anything, there were periods of time when I felt more concentrated because I just had extra time and I wasn't sort of distracted by an impending meal time coming up.


So that's usually what happens during the day is like if I'm working in the morning and doing whatever it is that I'm doing and it starts to get around lunchtime, it'll just be that habit forming.


And it'll just sort of whatever I'm doing, I'll be like, oh, no, I have to stop this because I'm going to be having lunch or something like that.


And so because that wasn't there, I could just keep doing what I had to be doing.


And yeah, I was I did a bunch of writing. I did a bunch of coding. I did a bunch of reading.


And yeah, I just had it was as normal.


What did you break the fast with?


A delicious black Angus sirloin seasoned with sea salt.


It was actually I'm actually going to leave that.


That's like a surprise for the end of the video.


But I did read somewhere that you want to keep your first meal kind of simple.


You see, I've never had a problem with a real major problem with digestion or anything like that.


So I found after breaking the fast about an hour later, I felt 100% fine.


So I had some more food, some vegetables and some some almond butter, my favorite food again, and felt completely fine.


No, no troubles at all.


What was your sleep like?


So a lot of times or actually my last fast like I saw a four day one, I had a little bit of interrupted sleep.


And so I'm not sure what was different about this one, but I didn't notice any major changes with sleep.


I think I found out myself I was sleeping deeper.


I think this one actually the main change between the last four day fast and this one was last one.


I was having black coffee during the day.


And so this one was just completely completely water only.


No black tea or black coffee or green tea or anything like that.


Just water only water and salt.


And so I think maybe that's why my sleep wasn't affected or potentially was the fact that I had a couple of magnesium tablets before bed.


So maybe that's again, I'm not entirely sure that's just what happened.


Sleep didn't really change too much.


I found myself waking up a little bit dehydrated.


So a little slow getting out of bed.


But again, that quickly fixed itself after having some water after waking up.


What were your blood ketone measurements?


So about 36 hours in.


So by the end of day two.


No, yeah.


The end of day two.


So 48 hours in.


I started measuring my blood ketone levels.


I didn't measure my blood glucose levels this time.


So it was I started with a minimum of 0.2 millimoles per liter of blood ketone levels.


So once you're I mean, there's there's debates on what the proper range is from but the zero fasting app said that it's zero to 2.0 millimoles per liter is about nutritional or fasting ketosis.


And so end of day two, I was 0.2 millimoles per liter.


And then the maximum was 3.4 millimoles per liter on the morning of day four of ketones.


And then it was it ended up being 2.1 millimoles per liter right before breaking the fast.


So a little bit of a range there.


It usually hovered around 1.5 to three ish during days three to five.


What was going to the toilet like without eating?


Pretty uneventful.


I only went to the bathroom twice on the morning of day four and day five.


And as you could imagine, not much happened.


Did you ever get hungry?


Only on day one and day two around lunchtime.


Did I ever really feel hungry?


And I mean, that's not even when I say that.


It's not like I was I was ravenous for food.


It was I think it's just completely out of habit because I said, as I said, I usually eat my first meal around lunchtime.


And so it got to that point of the day.


And it's just like my brain is like, oh, it's food time.


Yes.


But of course, because I was fasting, it wasn't food time.


And it's actually quite counterintuitive.


The further you go along, the less hungry you get.


So you'll see in the documentary, in the video diary sort of thing and also in the blog post journal entries that I was never really hungry throughout the whole thing.


Even on the final day, day five, right before breaking the fast, after finishing training, cooking dinner for the whole family and my friends, I did not feel hungry at all.


I was certainly excited and ready for food, but it was like not like, oh, I need this food right now.


So, yeah, you get less hungry the further you go along.


What was the best part about the fast?


Feeling in tune with nature.


It's incredibly hard to describe, but by by like day four and five, it was almost like I was I was high.


Like, seriously, like I had I felt like I had nowhere to be.


I was just completely present, nothing to nothing to do except just just just be.


And at the same time, like I felt ready.


So if I had to hunt something, I was walking around the backyard.


I'm like, if I had to chase something down right now, I'm I'm ready to go.


So it got it got primal by the end.


Would you do it again? Yes.


I'm definitely going to do it again.


I think I'll do I do 24 hours as a minimum, like one to two to maybe three times per month.


And I think it's going to be an extended one at least once per year.


So like three, five, six days.


Who knows?


But yes, this is definitely going to be a staple going forward.


So that is the rapid question section over.


If you have any other questions, let me know.


Leave a comment below and I'll reply to them.


Otherwise, enjoy the the mini video diary slash documentary series of what it's like to go through a five day water only fast.


Welcome to the... Fasting couch.


The fasting couch. The fasting couch, yeah.


Well, why is it called the fasting couch, Wyn?


Because you have to fast while you're sitting on this couch.


I'm about to sit on this couch for the next five days and not eat.


We're just going to we actually just start a five day fast, which means water only for the next five days.


For me, it's going to be at least five days.


So today is Sunday evening.


We've just had dinner with the family, a nice chicken and greens, something simple, low carbohydrate, because I read somewhere.


It's a good idea to start low carbohydrate for your last meal before starting a large fast so that you enter ketosis faster.


We're saying fast a lot.


Anyway, I'm going to go for at least five days.


So just water only, possibly a little bit of well, yes, there'll be a little bit of salt in there, but we'll figure that out through the video.


Sam, you're going to do at least five days, maybe longer.


Yeah. So minimum five days.


I'm going to see how long I can... Well, not survivability hold on, but how long I like want to actually do it myself.


That makes any sense.


Yeah. And main reason on my behalf is just I kind of want to do at least once a year.


Like I've done a four day fast before with water only and I've got a video on that.


But this is just not to do any extreme weight loss or anything, but just to kind of have a reset.


It felt really good last time going five days and just want to challenge.


But Sam, do you have any goals?


Well, number one, a little bit of weight loss wouldn't hurt.


And like it's just as well, I want to experiment with it because I do a lot of fasting myself, but I haven't done anything extreme more than like 20 plus hours, I'd say when it comes to fasting.


So yeah, I'd like to experiment with that.


Worst case is you can just eat.


So we've got this like little app here. It's a timer. It's called Zero Fasting.


It's got a whole bunch of information about fasting and they're not sponsoring the video or anything.


It's just a cool app that I've used before.


So, you ready? We're going to kick it off.


Three, two, one.


We can remind you. I don't want notifications.


Boom.


Remind me to eat.


We can remind you after 120 hours to eat.


We just kicked it off.


We're going to document throughout the next couple of days just how we feel and what we're doing.


Tomorrow morning, we're going to go for a walk.


How do you feel right now?


I feel great. I just had dinner.


Yeah, I'm still hungry but I thought we'd keep it a light dinner.


All right.


Yeah.


We'll see you tomorrow.


So, we are 13 and a half hours through on day one.


We're setting up a little place here so we can take progress photos.


Check that out.


And we did go for a walk this morning for about an hour or so just to start burning up that excess glucose


so we get into ketosis quicker.


Anyway, I'm going to write down here.


Day one.


Boom.


How does this look?


There we go.


So, we'll get a photo here.


Then I'll go weigh in. I think I'm about 74, 75 kilos.


See what we're weighing in.


Day one, 13 hours.


Water only.


10% already.


Look at that. 74.9 kilos.


That is me.


All right. Yeah, so 74, 75.


I think I'll hover around that for the five days.


We'll see.


Your turn.


Beautiful.


All right. 13 hours in.


How are you feeling?


I've been hangry actually.


Hangry?


More tired.


That's hangry.


So, angry and tired trying to just collide in one.


No coughing, mate.


No coughing.


Yeah, you feel it.


That's probably why I'm a little bit tired because I'm a little bit lying in in the morning


but normally when I get to the afternoon, I'm fine.


All right. Sweet.


We're trying to just go to the just treating the day as normal.


Just the only difference is water only, no food.


But we'll check back in in a few hours.


Let's do a fast, fast update.


It is 10 to 1, about 1 p.m.


I am 17 hours or so through.


14% through the 120 hours and I am in the fat burning zone which according to the app


is saying as glycogen becomes depleted, glucose from gluconeogenesis starts to rise.


So, that's gluconeogenesis is your body producing glucose based off other things like protein.


So, making glucose from non-glucose sources.


Lipolysis breaks down fat stores.


Fatty acids are mobilized for energy.


The liver begins to synthesize ketones which will eventually be what my body runs off, ketones,


like a fat energy store.


From excess energy produced from fatty acid oxidation.


AMPK activity promotes autophagy.


I'm not sure what AMPK is but autophagy is basically how I understand it is your body


getting rid of excess crap that it doesn't need because you're not putting in stuff.


I put on a long sleeve shirt because I started to feel cold.


Typically, I get this around about 16 hours into a fast and I believe after doing some


research on the app going through these articles and whatnot is that it's because of the thyroid


hormone something is going on there.


I don't know the exact process.


I'm not going to pretend to explain it.


I'm just going to say I felt a little bit cold.


I'm going as my day per usual.


As per usual, I'm building a computer vision model to identify different foods.


I'm kicking myself in the balls there.


I'm going to get back to it.


Maybe have a little nap and just chill out.


Then I'll check back in in a few hours.


Is that recording?


Yeah.


Big boy.


Day one in the books.


Look at that.


25 hours.


25 hours and 17 minutes actually.


We're 21% of the way through.


How are you feeling?


I'm tweaking real bad.


I'm missing food.


How do you feel today?


Really hungry through the whole day.


I'm not even joking.


I probably had six liters of water.


I had nine of them cups.


None of them giant cups.


That's a big cup.


Let me get that cup.


What else did you do?