This is one of my favourite spots to just sit,


The Ultimate Skill: Learning How to Learn (course review)

let my diffused mind take over and think about,


or not think about anything


and let the waves roll in into my brain like thoughts.


That analogy really didn't make sense.


Or maybe it did, maybe it kind of didn't.


But I need to let my diffused mind take over.


I'm gonna go watch the sunset probably down.


Can you see?


I'm gonna go down there and watch it.


You wanna come?


Let's go.


Starting Term 2 of the Udacity Artificial Intelligence Nanodegree | Learning Intelligence 19

What is going on learners? Welcome to Learning Intelligence 19.


And this week's episode is going to be really cool.


I'm into the second term of the Artificial Intelligence Nanodegree on Udacity.


And this term is all about deep learning.


So we're building projects using deep learning.


And right now I'm up to the first module and I'll show you what it's about.


We look in here. The first project is computer vision capstone.


Building a facial key point detection system.


And I'll show you that in a second. I'll show you an example of what it is.


I've been through all the initial classes here.


So this is what you get when you start.


So you've got to set up an Anaconda environment for your computer.


Set up some cloud computing on AWS.


Really cool thing about the AI Nanodegree, much like the Deep Learning Nanodegree,


you do get $100 worth of AWS credits. $100 US dollars that is.


That's really good to use Amazon web services such as EC2 GPUs,


which are really good for deep learning.


And so that's one thing sort of if you imagine the cost of the course,


you could essentially take $100 off of it because you get those AWS credits.


But then we go into here. So we started off with intro to computer vision.


Mimic Me is a project, an optional project. I'll get onto that.


I'll show you what it's about in a second.


And then we did image representation and analysis,


image segmentation features and object recognition.


And now I'm up to the project. I'm 100% viewed it, but I haven't completed the project.


And one really cool takeaway I took from the intro to computer vision class


is that the basis of any AI system is that number one,


it can perceive its environment.


And number two, it can make actions on its own will based on those perceptions.


So if you think about it in a two-part phrase, it's all.


Imagine a self-driving car as an example.


So self-driving car needs to use computer vision as well as other systems


to perceive its environment.


And then it has to be able to take actions based on those perceptions.


So that's a really, I think, cool way to introduce AI to someone


if they're not familiar with what it is.


And perceptions about environment can be anything in environment.


If you imagine a voice recognition system,


it has to be able to perceive sounds in its environment


and then make actions on that.


Two really important points, I think, in terms of describing what AI actually is.


Now I'll show you something really cool.


Is this facial key point system.


Let me set this camera up.


Now this piece of software is called Affdex Me, which is made by Affectiva.


And I don't know if you can see, but in close here, it's got on the side,


next to my face, it's got all these labels.


It's got anger, joy, sadness, disgust, surprise, and fear.


And on the right side here, all the webcams back the front for me,


it shows an emoji of what I'm feeling.


So let's try this out.


Wink face, it's got tongue out, a little smile.


See if we can get angry.


So that's really cool.


And this is essentially what we're going to be building


for the first Capstone project in computer vision.


So I'm really excited for that.


If I can leave a link in the description so you can try out using this piece of software,


I will, but if not, head over to the Affectiva webpage,


which I'll leave a link in the description.


They're a startup using computer vision to detect emotion in faces,


which I think is really cool.


All right, guys.


So we just finished part one of the computer vision Capstone project,


and I'm going to get on to part two and part three later this week.


So I'll show you here what I've done.


I've essentially created just a document here.


Like just a bare note, just a simple note,


and gone through the project and just wrote down what's required for the setup,


what's required for each different part,


some links to the rubric,


and then some optional tasks that I can make my project stand out,


and then a whole bunch of notes for each part.


What I want to do with this project or each of the projects in the term two is to,


once I finish them, make a blog post or a video about it.


So how a guide of how you can implement it yourself.


That's what I'm taking extensive notes on this,


one and working through it as hard as I can,


and sort of writing down everything that I found out how to help me.


So you can work on these things as well.


Stay tuned.


That'll come out in the next few weeks.


But if we look here, this project is all on computer vision,


and we started out right up the top just detecting a face in an image,


and then we detected the eyes of that face.


And then if we go down here,


we detected a whole group of faces in a blurry image,


and then unblurred or denoisified the image,


and then redetected it.


So if you see up here in this one, it's missing that face of the girl in the purple dress.


But then we took some of the noise out and made it a bit clearer,


not perfectly clear, but clear enough so that you could detect that extra face.


And then I just finished off, oh, actually, sorry,


then we did some edge detection.


You see here, we got a face there and then detected the edges of it,


cleaned up the edges so they were a bit cleaner.


And then we did, I learnt how Google,


so if you know, if you've ever used Google Maps in in-person view,


you know how everyone's faces blowed out?


Well, we did the equivalent of that.


If you come down here, we go into this.


So that is the original image.


And then I learnt how to, oh, I detected the face in it,


and then I blurred out the face.


So you see there, blurred out the face,


but you can still see the rest of the image.


I'm really excited.


I'm really liking this project.


I think the next stage or the next part of it,


let me just check, I'll fill you in.


So the next stage is part two.


So we're training a convolutional neural network to detect facial key points.


And then part three is putting parts one and two together.


So I'll hit you up once I've done some more progress.


I need a break from it.


I'm going to get something to eat.


Oh, one more thing.


Don't be afraid to reach out for help.


Look at this on the Udacity and the Slack channel.


See, I asked a question here.


I'm not sure if you can see it, but that's 3.14 PM.


I got a response back by 3.18 PM.


And the same goes for the forums.


If we come over here, where is it?


I got a response back 29 minutes ago.


And my question, I only asked one hour ago.


That's a note to self.


If you're having a problem with something, don't be afraid to ask out.


And that goes for you guys as well.


If you need any help with anything, ask me.


I'll do my best to help.


Leave a comment below and I'll try my best.


Three quick things from today's coding adventures.


So the main one is I'm still working through the computer vision project,


AKA to train a facial recognition system to detect facial key points.


Ideally, what we want is some sort of outcome like this.


See the blue dots there?


And so what I'm doing, I'm building a convolutional neural network.


You can see the epochs here and the training output, the losses there.


And I'm doing it in Keras.


Where is it?


Where's my implementation?


There it is there.


So starting off convolutional layer, max pooling layer, dropout layer.


However, it's got some tweaking to do, but I'm going to save that for tomorrow


because I'm going to go work out and clear my brain


and sort of let the subconscious do the work.


I've got my model pretty accurate,


but I think I need a few more iterations before I get it perfect and ready for submission.


This is how I'm tracking what I'm doing.


So I've got iteration one.


I put down the neural network architecture here.


And then for each iteration, so iteration two, I use the same network as above,


but I change to the atom optimizer.


I'm just writing down and taking screenshots of the results,


writing down the changes that I implement and taking note of what results


and how it affects the results so I can update it better in the future.


The second thing for today is I went over this lecture on deep learning with my brother.


And I'll leave a link in the description.


It's a deep learning lecture from the MIT self-driving cars course.


And it introduces deep learning from the eyes of self-driving cars.


So I found it really valuable.


I'll put a link in the description.


And it's by Lex, who's also a wizard.


So check his channel out.


I mentioned him in last episode, Lex Friedman.


And the third and final thing is if we go here, playground.tensorflow.org.


So this is where you can tinker with a neural network right here in the browser.


You can just change the architectures.


See, I've got mine running here.


It's running almost 4,000 epochs, learning rates there, activation, regularization.


Look at all this.


You can add more layers, add hidden layers, et cetera.


And it shows you what it outputs.


I think that's a really cool visualization for how a neural network looks like in TensorFlow,


what it's actually doing behind the scenes.


That's it for today.


It's really hot here.


As you can see, I'm sweating just standing in my room.


I'm going to go to the gym and sweat a bit more.


Apparently, 33% of the test miles driven in the MIT self-driving car course test cars,


which are all Teslas, are in autopilot.


Check this out.


And apparently, that reflects the actual use case of Teslas on the road.


And I'm not entirely sure how true that is.


I tried tweeting Elon Musk to see if that was correct, but I didn't get a response.


That's all right.


I imagine Elon's a very busy guy.


I've left a note here of what I have to do next week just so I can get on top of things.


I've got a few questions to answer.


I've got a few check boxes to answer.


And I'll show you how far through it I am in the Trello board.


This is how I keep track of all my projects.


So we go down here.


I've got a checklist of all the tasks I have to do.


I've done up to step four, step five.


I've probably done about 50% of step five to create a convolutional neural network


to recognize facial key points.


Put it on about 50% of that.


I have to finish off that and then get through the rest of that.


Guys, it has been a big week from deep learning with self-driving cars


to artificial general intelligence to building a facial key point detection system.


I'm loving this.


And next week's going to be no different as I work towards finishing the first major project


of artificial intelligence nanodegree term two,


building a facial key point detection system using a convolutional neural network,


combining OpenCV and Keras to build a system that can recognize seven, I think it is,


seven or so key points on a face.


And stay tuned in the future.


Once I finish the project, I'll release a video detailing the steps I took to do it,


as well as I'll put out a blog post of how I work through the computer vision pipeline.


But stay tuned for that.


Subscribe to me on Medium or subscribe to my channel


and you'll be the first to see those videos and blog posts when they come out.


But without any further ado, it's time to get out to some shout outs of the week.


So these guys either commented on my videos, reached out to me via email


or contacted me in one way or another.


You can always do the same.


My email is danielatmrdeeburke.com.


My Twitter account is at Mr. D Burke.


If you want to reach out to me, I respond to everything.


And I'll do my best to answer any questions you have or any advice you want.


Or if you want to give me advice, feel free to reach out as well.


I'd love to hear it from you.


In no particular order, we have Carlos.


So all the best with your AI nanodegree program.


And thank you for that Kaggle learning resource.


I'll definitely be checking that out in the near future.


Druv, I hope I'm pronouncing this name right.


But thank you so much for your kind words.


And all the best with your machine learning nanodegree.


Sreyas, again, I hope I'm pronouncing these names right, guys.


And if I don't, I apologize.


And I'll definitely keep these videos going.


I'm loving it so much.


It's I love learning and I love sharing what I learned.


So I'm going to keep making them.


Don't you worry about that.


Justo, I hope the note taking advice I gave in my deep learning nanodegree review video went well.


It's worked for me.


So hopefully it does the same for you.


Koran, thank you for your consistent advice, your feedback and your words of encouragement.


Plus that link to the deep reinforcement learning video.


That's available on Lex Friedman's channel.


The link's in the description.


And that's a wrap for Learning Intelligence 19.


Thank you so much for tuning in.


I'll be back next week with Learning Intelligence 20.


Can you believe it?


We're up to the 20s already.


It's been an awesome journey so far and I can't wait to see what's next.


Adversarial Machine Learning explained! | With examples.

Hello there! Today we are going to talk about adversarial machine learning.


No, Ms. Coffee Bean, do not be afraid. Of course we will discuss what adversarial attacks are,


but also how to defend against them. So come on, be courageous!


First thing that we need to know about adversarial machine learning is how to tell it apart from


generative adversarial networks, okay? GANs are a class of models training in a


non-cooperative way, hence the name adversarial. In GANs the goal of the adversaries is to make


the models stronger, so the game is well intended. On the other side, adversarial learning is the set


of techniques that try to fool models by providing deceiving input, so here is a certain malvalence


to the whole thing. In this video we will focus on these bad boys here, and let's start with a


notorious adversarial example. We look at these two images here. Ms. Coffee Bean does not see


any difference, do you? Well, there actually is a difference between these two pictures


and the difference is a tiny fraction of the magnitude of the noise here. While this difference


is imperceptible to the human eye, it is making a huge difference for a CNN-based neural network.


The same CNN predicts for the image on the left with quite some confidence that the image represents


a panda. Well done! But on the image on the right, where we have added noise, the CNN thinks it's a


gibbon. And this with an overwhelming confidence. How could this happen? This is an example of


adversarial noise attack. The algorithm behind this noise attack is fairly simple, even though


the name sounds very intimidating, Fast Gradient Sign method. Here we explain it briefly. Remember,


we train neural networks by Stochastic Gradient Descent, in short SGD, in the following way.


For finding the optimal parameters of the neural network to solve the problem at hand,


we compute the so-called loss function. The loss function measures the difference between


the current prediction of the network and the correct prediction, here panda. Of course,


we want the difference between the current prediction and the ground truth to be as small


as possible, hence we adapt our parameters of the network by minimizing the loss function.


And Stochastic Gradient Descent, SGD, comes in. By computing the gradient of the loss function


with respect to the parameters, we know the direction in which the loss grows bigger. But


we want to minimize it, so we move into the direction of the negative of the derivative,


in order to minimize the loss function. But this was SGD, which we employ when training the network


to perform as best as it can. Only that now we are in the malevolent realm of adversarial attacks.


So it is normal that Fast Gradient Sign methods try to revert what SGD has done during training,


by moving into the direction of positive gradient, upwards, not downwards on the loss landscape. And


instead of updating the weights, it updates the input itself, since the model itself now is given


and fixed. In the normal Fast Gradient Sign methods this procedure is done once, but we can


also perform it iteratively, like in this formula, to be sure one has moved away enough from a local


optimum. This noise attack was an untargeted white box attack. Untargeted means that we


manipulated the input such that the response of the network is anything but the true label.


We just ensured that the network misclassifies the panda, mistaking it with something else.


The opposite of an untargeted attack is of course a targeted one, where we want a network to


misclassify the panda and to label it with a specific label, for example Gibbon and nothing


else. And because all that the Fast Gradient Sign method did was to move away, untargeted,


from the correct label, the Fast Gradient Sign method as presented here is untargeted. But this


method can be also used in targeted attacks, of course, with a minor modification, by computing


the negative loss with respect to the target class instead of the positive loss of the true class.


Done. White box means we have access to the model's parameters. For the Fast Gradient Sign


method we do need this access in order to be able to propagate gradients back to the input.


In a black box attack, however, the attacker does not have access to the model. Let us think of an


example, one from NLP perhaps. What about breaking a machine translation model by introducing noise,


such as misspellings or character swaps? Well, a paper from 2018 did exactly that and has proven


that these changes, representing no challenges to human, broke the model without having access to it,


so in a black box attack scenario. Examples of adversarial attacks are far too numerous and show


how susceptible neural networks are to attacks, but also represent good tests from which researchers


learn and interpret something from the failure cases. Some adversarial examples have very strong


societal aspects and implications. Take for example this work that proves how easy it is to


fool a neural network that detects persons, where all you need to fool it is to print yourself the


right motive on a t-shirt. One has to think also about adversarial attacks for self-driving cars.


Take this case, where inconspicuous changes to the stop signs make the neural vision system


interpret the stop signs to be speed limits. Now, miss coffee bean, we have seen only a tiny fraction


of attacks. What do you say? Let's defend! Sure, let's start thinking about how to defend


adversarial attacks. The simplest and most frequently deployed idea is to just include


adversarial examples in the training set. This is called adversarial training and has some upsides,


like that it not only enlarges the training set, but including adversarial examples in the training


set leads to a more robust model against attack types it has seen during training.


However, this is a heuristic defense, meaning that this defense measure might perform well


in defending some kind of attacks, but there is no theoretical guarantee for it.


Adversarial training is one of the most successful heuristic defense.


A certified defense, on the other side, provides certifications for the lowest accuracy they can


reach under a very well-defined class of adversarial attacks. Recent approaches formulate an upper


bound which guarantees that no attack under specific assumptions can surpass the certificated


attack success rate. But certified defenses perform still worse than adversarial training.


We observe how hard defenses are and how adversarial training relies on knowing what


the attack is in order to have training examples addressing that kind of attack.


Sadly, this is an arms race, as illustrated here by Ms. Coffee Bean and her twin from


another dimension. Attackers come up with methods of attack and usually only afterwards the defenses


can be formulated, especially if we use adversarial training as a defense mechanism. Only after the


attackers raise the bar, the defenders respond and so on. This dynamic will definitely make


adversarial machine learning a dangerous but technically engaging field. But while we are


busy with technicalities, we should not forget to think about the ethical aspects. But this is a


Pandora's box we will not open in this video, maybe in a future one. Instead, we'll leave you


My second week running an online business

Week 2, run the business. So we got the whiteboard set up for today, the notes from Sunday, plan


out the week on Sundays on the notepad. Boom, this is my personal assistant, Borty. This


is the other one, Paddy. So today we're keeping it nice and simple, same as Mondays to Wednesdays,


basically the same as last week. We're going to ride to the cafe, read, write, then come


home, client work in the afternoon. Thursday, Friday, Saturday, we'll be learning, creating


slash project work. Simple. Let's do it. It's cloudy, this microphone isn't suited for the


wind, that's a good metaphor. Still beautiful, but even if you don't have the best equipment


or it isn't the best day for it, you can still create. Let's come back when it's sunny.


Can you step up to here?


Yeah. Home just in time. Starting to rain. Reading done, writing done, time for some


client work. Morning. It's nice and early this morning. I've got a call with the US.


Now when you wake up early, or actually any time you wake up, it's important to replenish


your water in your body. Why? Because if you've done well and you've got eight hours of sleep,


you've just been asleep for eight hours and you haven't drunk a single gram of water.


So start your morning off, 500ml to a litre of water and take it in a bath. All right,


I've got a call with the US. These time zones, man.


Man!


Test. Yo, yo, yo. All right, out of office notification. I must have messed up with the


calendar a bit. These things happen, that's life, it goes on. We're going to do today


as normal, so you know what that means. Whiteboard, ride to the cafe, read, write, client work.


I hit a little roadblock yesterday, but we'll try and fix that today. Well, we will fix that.


Keep your eyes on the road, Daniel. It was rainy the other day, well not today. The 4017


is putting on a show. Check that out. Just check that out. That's what it's about. Nature, baby.


Morning. I've decided every morning I'm going to do about 10 minutes of physical activity


slash a little bit of dancing. Why? Because you feel good after dancing. Put your favourite


tune on, get a little bit of grooving, get moving, and you won't wake up.


Very big day on tour today. As usual though, we've got reading, writing, and then tonight


I'm actually really excited for is doing an Ironman roll at Jiu Jitsu, which means an


hour long, half an hour with the gi, half an hour without the gi. But actually today's


Thursday, I'm supposed to be getting into fast AI and a bit of swift, but I've got some


client work I need to tidy up because deadlines are real.


Work is good. A little bit puffed from that ride up the hill. All right, we've got to


change a plan for today. Oh, PS, I finished, where is it? I finished Deep Medicine. Can


you see that? There we go. Deep Medicine, amazing book, how artificial intelligence


can help make healthcare human again. If you want to check it out, there'll be a link in


the description. My favourite chapter was Deep Diet and Deep Empathy. And as a follow


on to the Deep Diet, we have another book, The Personalised Diet. In a sense, I'm reading


that because I want to work out how technology can help me figure out what I should eat because


I did a nutrition degree in 2015 and I don't know what to eat. So yeah, maybe I'll just


build a company to figure it out. More on that later. But change a plan today. I was


planning today to go through the fast AI course, homework, etc., etc. However, I did touch


base with some of the clients that I'm doing some of the work, got some feedback. My stuff


is good, but it could be improved. And so what are we going to do? We're going to spend


today improving it. Why? Because there's this thing called lifetime value. Paper, Sharpie.


Lifetime value, now, or LTV. Now this concept is very underestimated, right? So a lot of


people, what they get mistaken with is thinking, well, this is you here, that you do your work,


say you charge $1,000 for it, this number is arbitrary. You get customer one and customer


two for $1,000. And then you're done here, right? No, that's not the case. What you really


want to think about is the long term. This transaction, this one here, this is only one


transaction. If you want to generate something over the long term, you want to be focused


about transaction two and transaction all the way down to X. Why? Well, look at here.


So you've got you, you produce something, your work one, you get customer one, customer


two. They're both $1,000 customers. Then say, for example, you got your work two and you


charge $10,000 for this. Customer one decides to follow on. Now, customer one is an $11,000


customer, right? And if this kept going, customer one could turn into potentially a $100,000


customer after 10, 20, 30 years. All right, so how does any of this relate to me? Well,


if I'm setting my business up for the long term, in order to ensure that deals keep coming


through to me, because that's the ideal place that you want to be, right? You want deals


to be coming your way. I need to ensure that the work I'm doing in the short term, I enjoy


it and I put it out as high quality so that it builds for the long term. This is where


all the value comes from. It's compound interest. Those gains you gain over a long period of


time. So when I get an email saying that, hey, Daniel, the project you're working on,


everything looks great. However, there's these small tweaks that would make it better. Of


course, I'm going to implement those small tweaks. So that's this afternoon's project,


but I'm going to have some lunch first and then we'll get into doing the actual work.


All right, it's a bit dark here. I think it's the light on. Much better. Now, never underestimate


the power of communication or the importance of communication. Why? Well, because all the


changes that I just implemented then are a result of me following up going, hey, is what


I submitted correct? Is it ready to go for the next stage? So if you have a remote client


or a remote project, never be afraid to touch base, right? And just to make sure everything's


going okay because communication, I mean, although the technical challenge, like I've


solved those, communication is still incredibly hard challenge to solve, right? So you can


never overcommunicate too much, but that's enough. I got to go get ready for a Jiu Jitsu


Ironman tournament. I'm excited.


Ah, well, if it isn't my arch nemesis, Gordon Ryan, AKA Davo.


Look, guys, this is going to be my opponent for the night. You got an hour roll, no gi


and gi. You excited, mate? What's your tactic?


I'm looking forward to being able to teach you some stuff.


So he thinks.


Round one. How'd you feel big boy?


Old mate. The effort required to run right now.


And this thing has a clutch on half.


Got you on the foot locks, baby.


What was the count, Davo?


Mate, my bet, I think you got me by a couple for sure.


Got you by a couple? Did you hear that? 75 kilo rig, still got it. See you tomorrow.


All right, reading done. Writing can wait till later tonight because I've cut off everything else.


You see here, now I said yesterday that the deadlines are real.


And so that's what I'm doing all today is just making sure that the project is done.


Because today's Friday and I want to have it done so that my mind is free to just work on my own things over the weekend.


So I'm going to finish it today. So I'm saying no to everything else.


That's kind of what I have to do. So fix ACG project. That's a cloud guru.


No to everything else. I'm going to do some yoga later because I work out every day. That's by default.


That's what it's about. I got the feedback at the start of the day.


All implemented and submitted.


Well, actually it was kind of yesterday, but long story short, all implemented and submitted by the end of today.


There we go. Second draft. Boom, boom, intro, outro, full video. That's uploading now.


That'll be submitted. I need to send through an email to update them on what I've done. So proud.


Look at this setup. I got lights. I moved all my stuff from my desk to my bed.


Another light. We're getting fancy in here.


And I've left myself a note as a reminder.


My ability to stay in business depends on my ability to iterate and deliver.


That is the most important part.


What a big week on tour. Second week in business. It's good fun. It's good fun.


My brain is absolutely racked from working on stuff today, but it's worth it.


You know what? Only two weeks in, but I can't help but think I'm doing something wrong.


I've kind of got an idea of what it is. But we'll save that for the next video.


Anyway, you know the drill. Keep learning. Keep creating. We'll see you next week.


Oh yeah, I forgot to say. If you have any questions, leave a comment below.


I'm Addicted to Learning - TDBS 28 April 2017

What is going on everyone? Welcome back to day 87 of the Daniel Burke Show.


Thank you all so much for tuning in YouTube, tuning in on the podcast, whatever you're up to.


I hope you're having an awesome day but I'm gonna have to keep this one relatively short.


I'm holding everyone up for dinner right now. Well not really, I think they won't matter too much.


It's a fairly large party and it's not like I'm the star attraction of the party but


where am I right now? I'm at Noosa which is a coastal town about two hours drive from where I live.


I'm actually here for a family friend's wedding but I'm still going to keep the podcast going


over the next few days. No matter what that's not negotiable for me. I can't give up now within


the last two weeks of the 100 day experiment. I've sort of decided after the 100 days it's going to


be once a week. That way I can, well the podcast that is and the YouTube videos, don't get me wrong,


I'm going to keep that going. It's going to be once a week and I'm sort of tossing up a few


topics of what I can talk about. I definitely want to talk more about artificial intelligence


because that's something that I'm studying right now. I mean I'm so addicted to work. I've got my


laptop here with me and I was just studying before, while I'm supposed to be getting ready for dinner.


I've sort of pushed the podcast right back to the last minute to do sort of thing because I was so,


at the moment I'm addicted to studying. I'm addicted to work and I don't know if that's


a good thing or a bad thing. Any sort of addiction in my opinion it can be dangerous at times.


I've been trying to think about how I can get more balance and whatnot because I'll be,


as I said in previous episodes, I'm going to go to this dinner right now and we're going to be


thinking about what I could be learning, what I could be studying. I'm sort of,


I'm always in that driven mode at the moment. Maybe I should fully embrace that and just follow


it but I know if I keep going at a pace that I would, at a pace that if I don't limit myself,


like I just keep going, studying, studying, studying into hours of the night, not getting much sleep,


then I'll burn out and any study that I end up doing won't be effective. So I think it's important


with everything to get some sort of balance. I mean I had a drive up here, a two-hour drive,


and I was trying to, I was actually on my, not doing a good thing. I was on my phone,


I was driving trying to find an audiobook on artificial intelligence to study,


to listen to while I was driving. And so it's called Super Intelligence by I think Nick Bostrom,


or I might be getting the name wrong, but it's called Super Intelligence. I want to check it out.


A lot of people recommend it but don't do that. Don't use your phone to drive. Don't be like me.


I was an absolute idiot. I should not have done that but nonetheless, I think it's just me trying


to fuel that addiction. I had a workout break today actually though. I worked out for a while


with my mate Jay at Raw Training Australia. We had a really good session. He's got an awesome


gym there. Check it out. Instagram at Raw underscore Training underscore Australia.


Actually filmed the training session we did. We did a whole lot of mobility work


rather than lifting some heavy weights. Yesterday was my friend's birthday and the gym session that


we did was because he was turning 22, we did 22 deadlifts, 22 bands, 22 push-ups,


22 bench press, 22 squats all in a row of his body weight which was 78 kilos. So he rounded it to 80


and it took me about six minutes and I almost threw up. I was in a very, very nauseous state


after finishing that. You know when you're in that group environment and you sort of push yourself


harder than you've ever thought you would go? If I was by myself, I don't know if I would have ever


done that sort of workout but because I was with other people, I just pushed myself to the next


limit and I think that brings it back to something I've been preaching so much like preaching a fair


few like on the previous episodes of the podcast let's just say that and in just in general


conversation, surround yourself with those that are going to put you up as well as what's the


law of the law of thirds actually. It's like be around 33% of the time of your time spent with


people who are who are better than you, 33% of your time spent with people who are on the same


level as you and 33% of your time spent with people who are below you. The idea is that the


people above you, you'll learn from them. That's what I'm doing right now with the deep learning


course that I'm involved in. There's so many smart people. I'm definitely the bottom of the


bottom of the team right there but nonetheless I'm learning a phenomenal amount and that's why


that's probably why I'm addicted to studying right now because I want to catch up to everyone else.


I'm trying to, I'm learning so much it's like giving me hits of dopamine here and there and


I don't know triggering that addiction mechanism that we've all got in the brain,


the one that gets triggered by anything that hits us with dopamine right and so yeah 33% of the time


with people above you, 33% of the time with people on the same same level as you that way you can


feed off each other and be competitive with one another and then 33% with people who are less


skilled than you so that you can teach them because I definitely believe that you don't fully


understand a skill unless you can teach it to someone else. Just knowing the name of something


doesn't mean you really understand it and I've been like that in the past. I don't know a lot


of things. The more you learn the more you find out you don't know and knowing the name of something


is not knowing it and so unless you can sort of look at a bird and say the name of it you don't


really know that bird you just know the name of the bird. I'm using a bird as an example. I think


I read this in Richard Feynman's book The Pleasure of Finding Things Out and that's what his dad used


to teach him that saying just knowing the name of something doesn't mean you actually know it.


It's not until you can sort of teach someone about about the actual thing that you really


gather a deep understanding. So what's the daily challenge? Hmm daily challenge is


learn something new. You learn something new every day so whatever it is learn something new. Let me


know what it is. I'd love to hear it out. You can follow me on any social media at mrdeberg


m-r-d-b-o-u-r-k-e or my website mrdeberg.com. Contact me wherever you want. I'm happy to


sort of hear about what you're learning. If you want to find out more about what I'm learning


I'm going to be releasing content over the next few weeks of how I'm learning to program, how I'm


learning artificial intelligence, how I'm making the podcast, how I'm making YouTube videos, it's


cooking, you name it. I'm going to try to broadcast it. How am I going to do it? I don't know yet so


you'll see the whole process. Nonetheless thank you so much for listening. Thank you so much


One Neural Network to Rule Them All (and more) | Machine Learning Monthly June 2020

Let's have a bit of fun, shall we?


And so every month I write an article, here it is.


I actually write a few articles every month, usually.


But every month I write an article


called Machine Learning Monthly, June 2020.


Well this one, this one's for June, it's July now,


but this is for last month.


And so basically the start of every new month,


I'll publish this article of the coolest things


in machine learning from the previous month.


And so this is on xerodomastry.io


where I'm an instructor on there,


I teach machine learning on there.


It's on the blog there.


But if you go to this, you can see ML Monthly.


This actually happens every month, so ever since January.


So June was January, February, March, April, June


is issue six.


And so I figured, you know what would be fun?


Is if we made a video version of going through this.


Look, see, there's yours truly.


Need to reflect the mullet though.


That one, this photo's a bit more professional.


But I thought it'd be fun, why don't we go through,


there's about 10 things here.


Why don't we spend this video going through


all of the little tidbits that I found in June 2020.


You know what, I might actually start


making video versions of these.


If this is something you'd like to see more of in the future,


leave a comment below.


Again, if you'd like to submit anything,


maybe something you've worked on


for the next issue of Machine Learning Monthly,


be sure to check it out.


I'll leave a link in the description


or send me an email directly


of something you'd like to share.


Anyway, let's start off.


So I started off with this cool little tidbit.


Welcome to Machine Learning Monthly.


One of the best ways to get started in the industry.


The first part of this series


is about solving problems in machine learning.


The second part is about how to use machine learning


to solve problems in machine learning.


That's a bit weird.


Why did I write that?


In this post, I'll introduce you


to a number of new machine learning algorithms


that I've been working on for the last year or so.


Well, that's another lie.


I haven't been working on my own


machine learning algorithms.


I'll also cover some of the more common problems


and techniques that you'll find


as you learn about machine learning.


Well, I usually do that.


I will also be discussing the various problems


that you'll encounter in machine learning.


Well, that's kind of the same thing that I said here.


And the ways that you can learn them in this series.


You know what?


You know why that doesn't really make grammatical sense?


Because I'm, frankly, I'm a better writer than that.


Or at least I tell myself that.


Is that this was written,


this entire paragraph was written by a machine.


See there?


Well, the above paragraph


was completely artificially generated by GPT-2,


which is actually number two in this list,


but we'll get to that.


There we go.


AI TextGen.


Is my head covering this?


Not really.


You can still see that.


So if we go to this,


if we click this one,


boom, GPT-2.


I actually used this OpenAI GPT-2 model,


which is a natural language processing model,


to generate that entire paragraph.


All I fed it, all I fed the model was this text here.


Welcome to machine learning monthly.


And it spurted out all of this stuff.


But again, we'll get to that.


Anyway, let's go to the first two things were my posts.


So how can a beginner data scientist


like me gain experience?


If we check that one out briefly.


Little blog post here.


So if Manuela sent me an email saying,


hey, how can a beginner like data scientist


And I went through a bunch of different


cool little points here.


You might want to reference that


if you're going for some kind of role.


If we go back.


How I passed the TensorFlow developer certification


and how you can too.


So I made, I've kind of got like a little style going on


with machine learning and content creation is,


I write an article and make a video to go along with it.


Because I find myself, I like to read some things


and I like to watch some things.


And I thought, well, if I'm making things for other people,


maybe there's some people out there like me


who like to read, who like to watch, who like to listen.


A lot of the time I'm watching a YouTube video,


I've got it running in the background


while I'm cooking food or something.


I usually like just watching people talk


or hearing people talk.


Anyway, this is an article, a guide.


If you want to go through


the TensorFlow developer certification,


you can check out how I did and how I passed the exam.


There's a video to go along with it as well.


Anyway, finally into machine learning monthly.


Oh, this is a little bit of a spoiler for what's to come.


This is the machine learning roadmap for 2020.


I'm actually really proud of this.


If you check it out, we're not gonna,


I'm not gonna read straight through the courses.


I mean, straight through the post.


Oh, there's a little issue there.


Maybe my browser is not showing me something.


But check this out.


This is, as I said, this is a spoiler of what's to come.


I haven't announced this,


but because you're watching this video,


I made this really cool interactive mind map


that you can go through.


Starts here, machine learning.


You probably want to go here, machine learning problems,


process, machine learning tools,


machine learning mathematics, machine learning resources.


And then they all branch out.


I mean, look at this process goes out.


Stay tuned.


I'll leave a link below that you can access this


and interact with it.


Oh, this is in the way here, isn't it?


I'll move this down.


But there will be in the coming,


in the next couple of weeks, or maybe next week, actually,


by the time this video comes out,


the video walkthrough, I'm gonna walk through.


I actually made like a little documentary


walking through all of this, so stay tuned for that.


But that's machine learning.


Again, this is machine learning monthly.


This is what it's all about.


Those are the first three things.


We're all from me, so I kind of hog the spotlight there.


But if you want to get this article,


there's a lot more we're gonna go through.


If you want to get this article delivered to your inbox,


you can sign up.


If not, you can just go to the zerotomastery.io blog,


or potentially come back to this YouTube channel.


If you like this sort of video of me just walking through


the coolest things I've found in the last month


in machine learning, I might keep making them.


But that's, I'm really proud of that.


I spent like a week putting this together.


Kind of connects all the different concepts


of machine learning.


So if we go back to the article.


Now number two was AI TextGen.


Leverage OpenAI's GPT-2 architecture to generate text.


So if we go to, let's check it out.


Again, we're not gonna read through this,


because you can do that in your own time.


I just want to play around with these things, you know?


So AI TextGen, by the way, what's his name?


Max Wolf, I believe.


Max Wolf, where is he?


How do you get to a GitHub profile these days?


Max Wolf.


GitHub just updated their layout.


So I'm still getting used to it.


It does look pretty nice, actually.


Max Wolf, awesome, awesome creator.


I love his work.


And this is phenomenal.


AI TextGen.


So it's a robust Python tool


for text-based AI training generation


using OpenAI's GPT-2 architecture.


So if we go to this, this is OpenAI's blog,


another blog that's really cool to read.


So better language models and their implications.


So you can read through that of GPT-2.


Long story short, it's this language model


that came out how long ago?


Maybe 18 months ago now.


And it, you feed it a whole bunch of text.


So basically to train the model,


it's read through the entirety of Wikipedia


and a whole bunch of other texts.


Maybe I'm getting Wikipedia wrong,


but let's just say it's read a whole bunch of online text,


learnt what words should come after the other words,


statistically, of course.


And then you can use it to generate new text


based off what it's learned.


And this is what Max Wolf has created AI TextGen


to make it really simple to do.


So you can go just pip3, install AI TextGen,


run a few lines of code here.


That's basically what I did in my Colab notebook.


I literally just went through this, fed it a prompt.


This one here, where is it?


Welcome to Machine Learning Monthly.


I just fed it those one, two, three, four, five words,


and it produced all of these.


So have a play around with that.


You'll probably scare yourself a little


because some of the stuff that it comes up with


are pretty real.


Like I read that.


Yeah, this paragraph, probably not.


You could probably decipher that was a bit fake,


but some of the other stuff,


I mean, especially if you train it,


one of the things I've thought of doing


is training my own version of it using transfer learning


on all of my past blog posts,


and then seeing what kind of things it can come up.


So maybe that could be a future video.


Who knows?


I just, because I've got a lot of blog posts out there,


and maybe I could get it to write one for me.


Anyway, that was really cool.


That was number two.


Number three.


Now, we've seen AI generate text with AI TextGen,


but you can also use AI to play it like Buck.


And if you don't know who Buck is,


I think it's Johann Sebastian Buck.


Yeah, JS Buck.


Google's Doodle, so that little graphic


that you see on google.com,


the one that usually goes here,


a while ago was to celebrate Buck's 334th birthday,


was allowed you to play music.


Let's see if we can.


The Last Post


In before that gets copyrighted,


but apparently that's generated by


an artificial intelligence model.


So you can read through this of how it was done,


pretty in-depth blog post of teaching you how to do it,


but you can also play along.


Where was that?


Today's Doodle.


We go here.


There was somewhere where you could play along.


Why won't you let me play along?


Anyway, check that out.


But this is really cool, right?


So these, I like the idea of generative models


because I never really learned


to play a musical instrument myself,


but I feel like now that I know how to code


and I know some machine learning,


I could actually code up my own band.


That'd be so much fun.


But this is just one example of how it's done.


So you can, what you can do is you can,


if I found the demo, I'm not sure where the actual demo is,


you can put in your own notes and then Google's CocoNet,


we'll check out the notes that you did


and we'll make it sound, we'll add some more instruments


and make it sound like a symphony made by Bach.


But of course it's just us sitting at our keyboards


and clicking different notes


and then it makes it sound really cool.


That was number three.


And then number four is data science,


expectations versus reality.


So let's check this out.


This is from Dan Friedman, really cool blog actually.


So check out Dan Friedman's or dfrieds.com.


Again, all of these links, the article itself,


this article will be below.


And then all of the other links that I talk about


will be in the description so you can check it out.


Some great points.


So Dan's story, the synopsis of this,


he learned data scientists right at the peak,


or learned, went to a data science bootcamp


right at the peak of ML and AI on the news


and all the headlines and that sort of stuff.


And he got a job in the field and then found out


it wasn't quite what he expected it to be.


I mean, yeah, you see all these posts that say like,


data scientists, amazing role.


And of course it is, like for some people


it's truly amazing.


But again, this is the thing.


When you set your expectations really, really high,


well then if you get into the actual experience


and you find out, well, hey, these things didn't suit,


fit my expectations.


And so what he's done is he's taken his experience,


and this is the kind of posts that I actually like.


It's like when someone's actually set out to do something,


got into it and then realized, you know what?


It wasn't what I thought it was, here's what I've learned.


I really like those types of posts.


So the points are, people don't know what data science does.


So people think different things of people's,


like you just asked someone to define machine learning.


How many different definitions are you gonna get?


Data science leadership is sorely lacking,


can't always be built to specs.


See a lot of data science and machine learning projects


stay in proof of concept stage,


because it's quite easy to build a model these days,


but actually getting into people's hands,


that's another challenge in itself.


You're likely to be the only data person,


especially in, so as in,


if you were like working at a small startup,


maybe there's a lot of people who are engineers,


but you might be the only data person.


And so, because think about this,


if you're working on a project,


it's likely that the project needs engineers,


more so in the beginning than it does any data science,


because if you don't have anything to build,


you don't have or any resources to build something,


if that something doesn't exist,


then you don't have any data,


then why do you need a data scientist?


So hiring a data scientist first,


before you even have any data is kind of


the cart before the horse.


It's that little metaphor there.


Number five, your impact is tough to measure.


Data doesn't always translate to value.


Data and infrastructure have serious quality problems.


I like this point, let me zoom in actually,


that's probably a better idea.


In data science, books, online classes,


online tutorials and Kaggle competitions,


the problems are faced are radically different


from what exists in industry.


In those resources, you'll have clean data


that's easily available, well documented,


and structured in an outline that allows you


to apply data science techniques to answer the problem.


Again, you'll find once you start getting on,


working on actual projects,


it's not quite as easy as downloading a CSV file.


Sometimes it is, not quite as easy as downloading a CSV file


and then just going model.fit.


Here's a good little tweet.


How much do you, how do you spend your time?


Cleaning data and moving data.


Yep.


And then another one, analyzing slash presenting data.


So look at this, picking features and models


is 6.2% of your time.


This is from 2,111 votes.


Vicky, actually, she's also amazing,


so you should check her out.


But here we go.


Data work can be profoundly unethical,


moral courage required.


Yeah, you're right, this is true,


especially in today's world,


like a whole bunch of privacy stuff to go along with data.


So what if you're asked to do something with data


that's against your ethics?


I mean, one time I was asked to build


a machine learning model that would be,


that would help sell more pizzas.


That wasn't the exact thing that it was doing,


but it basically would end up doing that.


And I could not, it was against my ethics


because I'm a proponent of health


and I can't use code to sell food


that I wouldn't personally recommend to someone.


So I had to say, no, I can't work on that project.


We go here.


That was number four.


Number five is what the hell is MLOps anyway?


So you might come across this term,


and as we said, a big problem of machine learning


is getting a model into production


because at the end of the day,


machine learning is an infrastructure problem,


like setting up the tools around your model.


So if you ever heard of development ops,


MLOps is like the machine learning version of that.


And if you're not sure what development ops is,


check it out.


I think if we go here, dev ops,


the rise of the dev ops mindset.


Another great blog post here,


which was actually part of the web developer monthly,


which is on Zero to Mastery.


My friend Andre writes that one.


So I'll leave this in the link below.


Anyway, where were we?


Here we were.


Chip Heughan, another great blog.


Check this one out.


What I learned from looking at 200 machine learning tools.


To better understand the landscape of available tools


for machine learning production,


because I don't know about you,


but maybe that's what you wanna do.


You wanna get a machine learning model out into production.


You wanna get a product into people's hands.


And now of course, machine learning may not be


the right tool for the problem you're trying to solve.


But if it is, you probably wanna check out


these different resources to figure out


how you can get that model into production.


I actually really liked this first link here too,


full stack or production deep learning.


Because if you see here, zoom right in on this one.


If you wanted to, there's your modeling code.


So these are all the steps.


This is like MLOps, all these steps around your model.


Configuration, data collection, serving infrastructure,


feature extraction, data verification, monitoring.


You may not have all of these,


but you'll probably have at least a few of them


in whatever product you're trying to build.


But this post by Chip is phenomenal.


Like it goes through all of the different MLOps tools.


So the landscape is underdeveloped.


There are many AI startups.


Most of them are application startups.


So using AI in some sort of application,


but a lot of them aren't building tools for that


sort of thing, tools for AI.


I could find only 200 AI tools, which sounds like a lot,


but if you search for Python application tools,


you're gonna get a lot.


Problems facing MLOps.


So some of the problems facing ML applications,


deployment, monitoring, data labeling, CI, CD test,


deployment, model compression, inference optimization,


edge device, privacy.


We spoke about that before, privacy, big thing.


Personally, I think privacy is a little bit overrated,


but that's my opinion.


And I plotted the number of tools


by the main problems they address.


This is a great blog post.


So check this out if you wanna look at MLOps,


or if you wanna understand MLOps a little bit better,


and what Chip learned from going through


200 machine learning tools.


Again, these are the type of blog posts


I really like coming across,


where someone has gone basically just surveyed the landscape


and distilled them.


And even better is Chip, I think, what we got about.


Anyway, I think she's a computer scientist


as well as taught machine learning at Stanford.


So part of, is part of the industry.


One neural network to rule them all.


This was actually outstanding research


called the once for all network.


Now, a real big thing when training machine learning models


is, let me zoom in.


Again, this is specialize it for efficient deployment.


I think this is really where the next big phase


in machine learning.


We've kind of reached the point of,


this is again, my opinion, my observation,


reached the point of, all right, yep,


it's fairly easy to get a pretty decent model off the ground


if you've got the data.


The next thing is, again, getting it into people's hands.


This is where machine learning and software engineering


is really merging.


If I had my time again,


if I was learning machine learning again,


I'd probably spend 80% of my time


learning software engineering


and the other 20% learning machine learning.


So that's probably a video in itself


or an article of some sort,


but that's how I'd start again.


So if you wanna take that, me going forward at the moment,


I'm practicing, I'm learning software engineering.


Now, what the once for all network is, OFA,


is train once and specialize for many deployment scenarios.


So if you train a machine learning model


on the cloud, say you're using a cloud GPU,


like Google Cloud, that GPU is probably,


I mean, sorry, yeah, Google Cloud or Google Colab,


that GPU is probably pretty powerful,


but that model might not be able to run on a mobile device,


say an iPhone, and probably definitely can't run


on a tiny AI, so like a small camera,


like a Raspberry Pi or something like that.


And so what once for all does


is it gives you a training framework


for training a model that can be not only used on the cloud,


it can be used on a mobile,


and it can be used on a tiny device,


an Internet of Things device.


And it also has far less carbon emissions,


because these are things we actually have


to really start considering.


If you know what's going on with the amount of carbon


that's been released into the atmosphere,


training a machine learning model,


although you can sit here,


you and I can sit here and type in things, click run,


we don't see all the carbon emissions


and all the electricity that our models are actually using.


So things like an MNASnet,


so I think it's model or neural architecture search net,


or massive, I'm not sure what M is,


but neural architecture search net,


uses a lot or emits a lot of CO2,


whereas once for all, because it's so efficient,


uses, look at that, what's that?


Three orders of magnitude less,


or over three and a half orders of magnitude less.


And it still gets amazing results.


So check out that once for all network,


very handy if you wanna train a machine learning model once


and deploy it to several different device types,


which is, I mean, these days,


if you're building a machine learning model,


you probably want it accessible as much as possible.


We go back.


Number seven, all of the matrix calculation,


or all of the matrix calculus you need for deep learning.


So one of the biggest questions I get


for learning machine learning is,


Daniel, how much math do I need to know?


The truth is, it's how can you measure that?


How much math do you need to know?


You can't measure that.


And so matrix calculus is actually a really big part


of deep learning.


It's actually all of what back propagation is,


how a neural network improves its guesses,


or improves the patterns that it's learned.


And so Jeremy Howard and Terence Parr,


Jeremy Howard is the founder of fast.ai,


wrote a book and it's available for free.


This is all of the matrix calculations or calculus.


Why do I keep saying calculus, calculations?


This is all of the matrix calculus


you need to know for deep learning.


And you probably look at this,


and don't worry, I look at this,


and I go, look at all these Greek symbols.


But treat this as a reference to come back to.


The way I say things is when you need to learn things


when you need to learn them.


So if you find that you start to realize,


okay, I'm building my machine learning models,


and I'm kind of at a breaking point,


I wanna really improve them.


I'm trying to read the latest state of the art


research papers, but I'm really getting stuck with math.


This is where you come in and you read things like this.


You could start, spend a week reading it end to end,


even if you don't understand all the symbols.


Just read it end to end.


Don't even bother like trying to read the symbols.


And then come back, leave it settle for a week,


come back, start reading it again.


That's how I learn things.


I find it really helpful to basically just get a textbook,


read it end to end, and then refer back to it when I need.


So we come back, check that out.


Number eight is can AI play Minecraft?


So this is actually a really cool challenge


using reinforcement learning.


Personally, I haven't,


I've only really worked on one reinforcement learning


problem, but there's a big competition going on here.


Can you train an artificial intelligence agent


to play Minecraft?


Now I have really no interest in playing video games,


but I do have an interest in training AIs


to play video games for me.


So this would be a project that I might check out.


And again, it's really cool to,


I mean, if you did well in this project,


you could probably apply to open AI or DeepMind


and be pretty suitable for a role there.


Like, I mean, you gotta do well on the project


and of course document all your work.


It of course is gonna take a lot of work.


It's open for 87 days, I think they've got left,


but that'd be some prime experience to get ready for,


I wonder what prizes they have to be determined.


So here's the timeline, just started July 1.


Check that out a couple of days ago,


you've got to the end of the year.


So that's a good six month project.


Might be able to even join a team as well.


And, but check that out, AI Crowd.


We go here.


YOLOv4, number nine,


state of the art real time object detection model


you only look once is back, is back.


YOLO is back for a fourth time.


Let's check it out.


We go YOLOv4 and the medium article to go along with it


announcing it was also incredible.


Oh, they both linked to the medium article.


If we go here, code,


dark net, there we go.


YOLOv4, where's the demo?


Demo,


I want a video.


Anyway, there's a code on GitHub,


I think the video might actually be in here.


I love this paragraph.


This is how you announce some state of the art research


that you've done.


Darknet YOLOv4 is faster and more accurate


than real time neural networks,


Google TensorFlow Efficient Net


and Facebook Pytorch Detectron Retinet slash mask,


CNN on Microsoft COCO dataset.


So it's like basically going,


Hey Google, Hey Facebook,


I built this thing and it beats you guys.


I love that.


Stick it to the man.


Alexi, what a legend.


If we come here, there's a little demo of what YOLO does,


real time object detection, here we go.


I love it.


How cool is that?


Might turn that down again, copyright rules.


That's pretty quick.


Try out YOLOv4, there was a little bit of a controversy,


someone also released YOLOv5,


but I'm pretty sure from my research,


YOLOv4 is someone who wasn't part of,


so the story with YOLO is it's from Darknet,


from PJ Ready, I believe,


but PJ Ready has left the AI field.


Long story short, Alexi released YOLOv4,


someone else released YOLOv5 at the same time.


I think it turns out that YOLOv4


is actually better than YOLOv5.


Correct me if I'm wrong.


Number 10 is end to end object detection with transformers,


on the topic of object detection,


Facebook just released a model,


so transformers are really taking over.


They might be the universal architecture, maybe,


by universal architecture I mean,


one architecture that works well


on a lot of different problems.


Convolutional neural networks actually work pretty well


amongst vision problems and text problems


because convolutions are actually


quite efficient calculations,


but transformers have kind of taken over the NLP world,


natural language processing,


but now they're making their way


into object detection computer vision.


One would have thought maybe this was once ruled


by convolutional neural networks.


I think they are actually still a part of it.


Yeah, there we go.


So this is their new model, DETR,


I think it's detection transformer,


so object detection transformer.


They use CNN features with standard transformer,


encoder, decoder, and then predict boxes and classes.


Now, this is a more traditional object detection model here,


faster RCNN, and it has a bunch of different steps in there.


So using transformers kind of simplifies things,


but again, this is a blog post,


there's a paper to go along with it.


There we go, advancing towards a unified approach


for NLP and computer vision tasks.


One of the cool things about using transformers is that,


where is it?


There we go.


DETR's novel architecture can also help improve


the interoperability of computer vision models


because it relies on attention mechanisms,


which is a part of the transformer neural architecture,


attention, I'm not gonna go through that,


but check it out.


Basically, it's what does a model pay attention to


when it's figuring out different things?


It is easy to see what parts of an image the network


is looking at when it makes a prediction.


So that's a real big point in explainability


is if your model makes a prediction on an image,


what part of that image caused that prediction?


So if we come back, the last one is 10.5.


Speaking of transformers,


which is what this post was kind of about,


end-to-end object detection with transformers,


if you wanna learn what a transformer actually is,


apart from Optimus Prime,


this blog post is a pretty good overview,


and it also links to different resources


about other transformers.


So there we go, classic fully connected neural networks,


CNNs, RNNs, kind of talks about different


other architectures and then breaks down,


there we go, more on attention,


attention in CNNs, the transformer,


attention is all you need.


So check out this post if you wanna understand


more about transformers.


That's actually a really cool one.


So there we go.


I'll see you next month.


I mean, that was it for June.


If you did, we'll go back to the main screen.


If you did enjoy this video of me going through,


I'm not sure how long it went for,


but if you did enjoy this kind of video of me


just going through some of the coolest things


over the last month, let me know in the comments below


if you think, well, Daniel,


I just prefer to read the blog post.


Well, that's cool too.


But again, if you'd like to see more of these,


let me know.


If you have anything you'd like to see in another issue


of Machine Learning Monthly, also send it to me


or leave it in the comment below.


Otherwise, if you'd like to get that post,


it comes out at the start of every month, roundabout-ish.


Some of the coolest things that I've found


over machine learning and why I found them cool,


be sure to subscribe on the zerotomastery.io blog.


But otherwise, as always, keep learning, keep creating,


and I'll see you next time.


You're Never Too Young to Start Learning AI - Learning Intelligence Talks with Shaik Asad

What's going on guys? Welcome back. So today I have a very special guest on the show today


and we're going to record our conversation and we're going to put it up on the YouTube


channel eventually so you guys can hear what we're talking about. But if anyone else would


would like to have one of these talk-ins as well, leave a comment below or reach out to


me via email. You know my email is Daniel at MrDberg.com and we can organize this. But


nonetheless I'd love to introduce you all to Sheikh Asad who's a 14 year old. Hey guys


today I'm going to be talking to Daniel Berg. Welcome Sheikh, thank you so much for coming


on and I'll just, Sheikh, tell everyone a little bit about yourself. Where do you come


from and what are you currently studying? I'm from India and I'm studying GANs right


now. GANs, Generative Adversarial Networks. So what makes you interested in, what makes


you, and you're 14 years old right? Yeah. And you're an artificial intelligence developer.


I wish I started that early, that's amazing. And so what excites you about AI? Yeah we


can do almost anything with it. Yeah? Like possibly anything. Like you can even make


crime detectors with CNNs if you want. Yeah. So that's really exciting to me. And we, humans


don't need to do everything. Yeah. So yeah, it simplifies our lives. That's really cool.


I remember you were telling me yesterday you had a really good idea actually, crime detectors


with CNNs. And so what was it you would say, start off by, you'd need data from video cameras


recording crimes. Yeah, CCTV cameras. CCTV cameras, exactly. And then you'd use that


and get the ones that have crime in them and find out so you can sort of predict in the


future. Yeah. What's that, have you seen that show before? I'm not sure what it's called.


I forget what it's called. Person of Interest. Have you seen that before? What? It's a TV


show called Person of Interest and there's a big AI and what the AI does is, or big computer,


and what the computer does is it predicts crime and then sends like a James Bond type


character out to fix the crime before it happens. I didn't see it but that's cool. That's cool.


And so are you in high school? Yeah, eighth grade. Eighth grade. And where at? In India?


What? In India? Yeah, in India. In India. Wow. So how many people are at your school?


About 2000 I guess. 2000? And do they teach computer science there? Do they teach AI at


school? No. What have you been learning at school lately? History, kings names, you know,


Tamil kings names. They're just so big, you have to regress all of them and write it in


a test. In a test? And do you like learning kings names or do you prefer learning about


the kings? Yeah. Because I remember you saying before when you emailed me and you said school


teaches a lot of useless and boring stuff. Is that part of the useless and boring stuff?


Yeah, absolutely. And so how are you learning AI? You sent me through a screenshot of some


convolutional neural networks that you built. That was really cool. So you made that on


your own? Yeah, but I also took deep learning nano degree term one, that's it. So there


was only term one available, so I had to take only term one. There's no full nano degree,


I have to take it. I wanted to take it, so I took the term one. Term one, the deep learning


nano degree, is that from the foundations one? Yeah, Srirajaraj. Wow, that's incredible.


So how long have you been programming for? About a year. A year? Yeah. And where did


you learn how to do that? Was that at school or were you teaching yourself? Yeah, I was


teaching myself through the internet. Through the internet? Treehouse. Treehouse. Oh, me


too, I use Treehouse as well. And was it always Python? No, CE, Java and last Python. Oh wow.


Okay, so is there anything else that you, apart from deep learning models that you've


built or that you've coded? Yeah, machine learning, through scikit-learn. Through scikit-learn?


It was easy. Yeah. Tell us about that. I find scikit-learn hard, so if you're doing that,


that's amazing. So I took machine learning nano degree as well, only term one. Yeah.


And Sebastian Thorn and a girl, I don't know her name, but they both were teaching machine


learning. Yeah. And okay, so how did you find out about artificial intelligence? So yeah,


I was playing chess in the computer, so I don't know how that works. So I thought, I


googled it, how does chess work? And then an article about AI came. I didn't even know


about what deep learning was, so I thought deep learning was learning advanced deep concepts.


Yeah, well me too, I didn't know what it was until recently as well. It's sort of a strange


name once you start to actually learn about what it is. Okay, that's really cool. So you're


playing chess against your computer, and was the computer winning? Is that why you wanted


to find out? Or were you beating the AI? No, no, no, every time. Because there's no chance


for me, I think I still fail on level one of the chess computer engines. Me too. They're


too good these days, they're making them so good. Like have you seen AlphaGo Zero is it?


Or AlphaGo Zero? Yeah, it's not chess, it's Go. Oh, Go, yeah. But they also use that same


engine, the same algorithms to learn chess. And so now AlphaZero has beaten the best chess


engine as well. Yeah, that's good, cool. Reinforcement learning, is that what you said? Yeah. Yeah,


I think that combined with deep learning is going to bring out some really cool things


in the future. So who's your favourite, or what's your favourite resource to go to learn


about AI? Yeah, just some blog posts. Yeah. And Udacity Nanodegrees, if you can pay for


it. Yeah, Udacity Nanodegrees are great, that's true, but of course they do need to be paid


for. But I think it's really good as well, there are a lot of free resources online and


whatnot. Have you heard of Fast.ai? Yeah, yeah. Yeah, that's something I want to try


in the future and it's totally free. And of course, Siraj Raval, all of his videos are


really, really good. And one more advice. Yeah? Check out my YouTube channel for more


tutorials and GitHub. Oh yes, yeah, I'll put the link in. What's your channel and what's


your GitHub? GitHub is developer404 and the channel is also. Developer404. Okay, I'll


make sure I put the links in the description so everyone can check them out. Because what


you were telling me before, what do you want to do? You want to start making videos and


posting code on GitHub of the AI models you're building? Yeah. And so where did you start


learning the convolutional model that you were building? Actually, I emailed you saying


that I had a hard time learning convolutional neural networks. So that was a long time ago


and I got a reply on New Year, I think. I'm sorry, I was slow replying, I'm pretty sure.


So thank you for your patience. I think I was on holidays when that happened. Yeah,


that was December. I learned from a blog post and Suraj Javar has some nice videos on it.


On convolutional neural networks? Yeah. Yeah, I'll have to check them out. Introduction


to convolutional neural networks, part five. I mean, introduction to deep learning, part


five. Part five? Okay, I'll link that in the description as well so people can check that


out. I'll need to watch that because that's what I've been learning about in the Coursera


course recently was about computer vision. And convolutional neural networks, of course,


are the best for computer vision. I think Facebook were trying to use convolutional


neural networks for translation. Tagging. For tagging? Yeah. Oh, really? Like photo


tagging? Yeah. Because they're really quickly at a... you upload a photo now and it knows


instantly who's in it. Even better than I do. It knows my friends better than I do.


So what are your... do you have any favorite AI companies or favorite people that are building


AI? Yeah. Geoffrey Hinton and Yann LeCun. Oh, yes. And also, who's that? Invent of Gants.


Ian Goodfellow? Yeah, Ian Goodfellow. Yes, they're all very smart people, aren't they?


And what makes them... why do you look up to them so much? Because, you know, they're


the ones that never give up. Yeah? That's important, right? Yeah. Geoff Hinton, he was


told that neural network, it will not work. But he proved them wrong, right? Yes. There


was an interview actually with Geoff Hinton. Yeah, I saw that. Have you heard of Andrew


Ohm? Andrew A. Yeah. You've heard of him? Yeah, the interview between him and Geoffrey


Hinton is really good. That was a part of the Coursera course. I saw that. I really


like that. Deep Learning to AI. Yeah. Have you checked out that one yet? Yeah, I'm planning


to take that course. Take that course? And so do you think it would be good for schools


to teach people about AI and deep learning? Yeah, but I don't really like school because,


you know, I like self-education. You know, me sitting in a room instead of meeting in


person and studying. Oh, you prefer that way? Yeah. Sometimes it's good when you're alone


and quiet and you can work on those models. And so what's your learning process at the


moment? If say you wanted to learn something, how do you go and do it? So first of all,


check out GitHub. GitHub? Yeah. And you know, search for nine lines of code in Keras or


maybe TensorFlow. Yeah. What's nine lines of code? What do you mean? Yeah, I mean, Keras


is pretty simple, right? Yeah. I like Keras. Yeah. So you can code up a convolutional neural


network in nine lines. Nine lines of code? Yeah. In Keras. So if you search in GitHub.com,


nine lines of code, convolutional neural network. And how did you find that? By searching. Searching


GitHub? Yeah, that's true. I need to start using GitHub more. I think I'm still trying


to work out how I best learn. I think I'm very visual. So I imagine things in space


and I compare it to other things like using analogies. And so what do you want to do once


you finish school? Because you're in year eight now, does it go to year 12 in India?


No, till 10. Year 10? Yeah. And then do you go to university or do you have something


after year 10 before you go to university? Yeah, something after the 10. After the 10?


Yeah. And do you want to go to university to study AI or do you want to keep learning


on your own? Keep learning on my own. Yeah, good idea. And then you can start an AI company


and build convolutional neural networks to detect crime. Crime, yes. What about what


are other projects are fascinating you at the moment? Is there anything someone else


is working on that you've seen and you think, wow, that's really cool? Yeah, that one, you


know, with Gantz, you draw something and it generates a picture of something. Yeah. So


you mean like you just draw lines or outlines of something and it turns that into an actual


picture? Yeah, that one. Oh, that's really cool. I think, is it Google that are doing


that or is it multiple people? Multiple people like OpenAI beat an Atari game. Not Atari


game, but some game. I don't know. Oh, OpenAI, I think that's the one I read about that.


It was Dota 2, was it? Oh yeah, Dota 2. Dota 2. Have you ever played that? Yeah. No. Do


you play any games at all? No. No? So what do you do for fun outside of or is making


AI models fun for you? Yeah, making AI models is fun and I sleep. You sleep? Yeah. So what


does a day look like in Shakespeare? Is it you get up and you go to school, come home


and code? No, I have to do lots of homework. So you go get up, you go to school, you get


home and you do your homework and then you start to build AI models? Yeah, sometimes,


you know, like maybe I will start at 11 o'clock or something. 11pm? Yeah. Wow, you're staying


up. What time do you start school in the morning? 8 o'clock. 8 o'clock? So you have to stay


up quite late to build models. Sometimes I don't. Sometimes you don't? Yeah, of course,


you need sleep. That's important. You're a hard worker. At 14 years old, you're doing


the right thing. You're doing some good things. And so do you play any sport? No. No? No?


No. Do you like cricket? Cricket? Yeah, I watched it when I was, I think, 11 years old.


11 years old? Because cricket, yeah, India, I think is the best in the world at cricket,


I'm pretty sure. Yeah. Yeah. I like playing cricket. I think the cricket, my Australian


team is playing at the moment on TV, so that's why I asked. Okay, and so do you have any


brothers and sisters? Yeah, I have a sister. You have a sister? And is she into AI as well?


No. No? What about your parents? Do they like that you're coding or they want you to keep


going with school? Yeah, sometimes, you know, my father and mother say keep balancing with


school life and AI. That's a good idea. That's important, I think, because school, keep paying


attention to school, do enough to pass, but I think what you're doing outside of school


is really good, working on your own projects, because if school, they teach a lot, but they


don't teach you everything, so that's where it's just really good to develop that habit


of going out and finding what you want to learn about. So, in the future, do you want


to work for an AI company or do you want to start your own? Start my own. Start your own?


I like that. You can start young too. Yeah. So, do you have any questions for me? Yeah,


I do. How do you maintain your health? My health? So, as in like fitness? Yeah. Yeah?


Okay. So, for me, it's an everyday thing. So, like if you wanted to learn about AI,


you try and do it as much as possible or at least try to do a little bit every day if


you can. So, that's the same with me and health. So, I try to eat well every day and eating


well for me is a diverse diet, which means plenty of different foods, mostly single ingredient


foods, and what I mean by that is foods that don't come in a packet, don't come with multiple


different ingredients making them. So, most of the time, I'm cooking my own food, which


is plenty of vegetables and fruits and greens and whatnot. I know you said before, you're


not the biggest fan of greens. That's all right. You got to start small. You start small.


And of course, exercise for me is like a meditation and it's a way for me to break up the study


periods because at the moment, my schedule is study Monday to Friday and I drive Uber


on the weekends and make YouTube videos on the weekends. And so, every day, I exercise


about an hour or so in between that as a kind of a break to let my mind have a rest and


I work out my body. And so, once you – it's just a time thing, right? Once you get that


habit and you keep doing that, doing that, it just becomes –


It becomes an habit.


Yeah, exactly right. So, like coding, right? I still need to work on my ability to build


models like you're doing the right thing. I should be doing what you're doing and getting


into a Jupyter Notebook and starting from scratch. So, it compounds over time. So, once


you start doing it every day, little bit by little bit, it gets easier and it becomes


a habit as you said. And so, I think if you wanted to start with improving your health,


you start small, you start with something little every day and then build that over


time and then it just becomes one big recurrence.


Yeah.


Yeah. What else? Do you have any questions?


Do I have any more questions for you? I think what's – so, we're in January. What's


your goals for this year?


Yeah. This year, preprocess any dataset that is given to me and apply it to deep learning


because we can't really make the world a better place by classifying handwritten digits,


right?


Yes. That's true. Yeah. That's where you start, right? That's the first – you start


with that as the first bit of practice and then you use that, what you've learned from


there and you have to, as you said, preprocess any dataset that's given to you because


data in the real world doesn't come in beautiful little tables or labeled and stuff like that.


That's really cool. So, that's what you want to be able to do by the end of the year.


I want to start a YouTube channel. I mean, start making YouTube videos. I've already


started.


Yeah. Again, it's developer 404, right? I'll put the link in the description. So, you want


to, on your YouTube channel, start showing people how you preprocess datasets and apply


deep learning to them?


Yeah, eventually. But at first, I'm going to be starting from zero. I mean, and then


we're going to be going into it.


Wow. Yeah. That's a really good idea. I like that. That's going to bring a lot of value


to people. I'm going to definitely be checking out your channel. I've got a lot to learn


from you.


And so, how –


From you.


Thank you. And so, how do you know – what steps does it take to – how much do you


know about preprocessing a dataset at the moment?


Actually, I have to learn that. I don't know about –


Yeah. Me too. I don't know much either.


Yeah. But yeah, I can work with the MNIST and other datasets which are labeled and yeah.


The MNIST dataset, is that the – that's the digits, right?


Just so people know what it is.


And where can people find the – is the MNIST – is that, am I right, is that the one on


TensorFlow with the tutorial?


Yeah, yeah, yeah.


You can learn how to use TensorFlow. And what's – for people watching, what's TensorFlow


to you? What do you think it is?


Yeah. It's a library to build things easier. You know, you can't code up every single


thing. We don't have that much time to look into math and people are not interested in


math that much. So, yeah.


That's true. So, of course, you don't want to be building it from the ground floor up.


You can use the same code that Google use to build deep learning models. I think it's


really great. And there's plenty of great tutorials on there for anyone who wants to


start learning deep learning.


I love TensorFlow, by the way.


Me too. I love TensorFlow and Keras. They're really, really cool.


Okay. So, if you could tell everyone watching one thing about what you think they should


do in terms of learning AI, what would it be?


One thing to do – to learn AI.


Yeah. So, say someone, they're watching this and like, I want to learn AI like Shake and


I want to start building – help Shake build a crime detection model. What should they


start doing if they know nothing about AI?


Yeah. First, they have to read some blog posts and then they have to code up GitHub repos.


Yeah. Just recoding them.


So practice recoding existing models.


Yeah.


That's really helpful. Yeah. And go on.


All right. And then you have to – before that, actually, you have to search for some


nice Andre Capati's blog posts.


Andre Capati?


You have to understand it. Yeah. And CNN. I was having a hard time learning it. I took


CS – no, no, no. I just checked out their website and then it was great. Yeah.


And so CNN is convolutional neural network, right?


And convolutional neural networks are mainly used for computer vision.


Yeah. Image classification.


Image classification. So if someone doesn't know AI, they're watching this, they're


seeing you're 14 years old building AI models, which is amazing. They want to start out.


They need to first of all look at blog posts like Andre Capati and then practice building


convolutional neural network models from GitHub repos.


And does your GitHub have a convolutional neural network repo on it? Not yet?


No. I'm going to be making it soon.


Making it soon? Okay. Well, you better – forever is watching. You better keep an eye on Shake's


GitHub because it will have some convolutional neural network models on there in the future.


Do you have your GitHub?


My GitHub? Oh, yeah. My GitHub is mrdburke. All of my socials are mrdburke, M-R-D-B-O-U-R-K-E.


Yeah. That's my Internet name.


So thank you so much for calling, Shake. We'll be in contact in the future and we'll probably


do one of these calls again in the future. So thanks so much for reaching out. It's


been a pleasure talking to and meeting you. And thank you for your support on my videos.


And I'm definitely taking a lot of inspiration from you. So keep doing what you're doing.


And just lastly, one more time, where can people find your stuff? What's your channel


and what's your GitHub?


Developer 404.


Yeah.


So you heard it, ladies and gentlemen. The link will be in the description. We have Shake


who's a 14-year-old AI dev. Thank you so much. And we'll see you soon.


Bye. That's a wrap.


Thank you.


Thank you. How was that? Did you have fun?


Level up your software engineering skills as a data scientist | ML Monthly February 2021

I'm ready. Are you ready? Of course you are. Because why? Well, it's that time of month


again. It's Machine Learning Monthly. Specifically, Machine Learning Monthly for February 2021.


And if you're new here, first of all, welcome. Second of all, Machine Learning Monthly is,


let me just show you it. It's a blog post that I write or a newsletter as well. There's


my face there. There's the title of the post. It's a blog post slash newsletter that I write.


If you want the newsletter, you can subscribe. I'll leave the link in the description. But


it's of the latest and greatest, but not always the latest, things from machine learning that


I found over the previous month. So this one's for February 2021. It is now March 2021. So


it has all of the stuff that I found during February. And if you've been here before,


well, you know the drill. So without any further ado, let's jump straight into it. Again, if


you want to sign up, you can go to the zerotomastery.io blog, enter your email, and you'll get this


delivered straight to your inbox. Or you can just read the post from this link and you


don't have to watch this video. Anyway, what you missed in February as a machine learning


engineer. So very, very exciting. I worked on this for a long, long time and it's still


in progress. However, during the COVID lockdowns of 2020, I passed the TensorFlow developer


certification and there's a video on my YouTube channel about it. I'm not going to go through


it, but you can see it all there. All the, all the stuff about the certification, etc,


etc. Long story short, I had a lot of questions of people asking me, I want to learn TensorFlow,


where do I start? And how do I pass the TensorFlow developer certification? So I created the


Zero to Mastery TensorFlow for Deep Learning course. And if you go here, you'll see the


link right now, it is on Teachable. And you can see all the stuff there. If you click


the link and sign up to the Zero to Mastery Academy, you'll see it's an affiliate link.


So not only will I get paid for the course, I'll get a little kickback if you sign up


to the Zero to Mastery Academy. So go check that out. It teaches three things, mainly


the fundamentals of deep learning, a machine learning paradigm taking the world by storm.


You may have heard of it. You probably have if you're watching these videos. The fundamentals


of TensorFlow, a framework used to write deep learning algorithms, for example, neural networks.


This is a beginner friendly course, by the way. So if you've had more than a year's experience


writing TensorFlow, this course is not for you. If you're new to deep learning and TensorFlow,


it is for you. And then finally, how to pass the TensorFlow developer certification. All


of the materials are on GitHub. And they can constantly being updated. So if you go check


this out, you can see all the materials, there's notebooks, there's exercise and extra curriculum,


guides, etc, etc. And another note, I have been streaming a lot of course development,


the future parts of the course on Twitch. So go check me out on Twitch if you want to


see some live coding. There you go. There's my last video. Anyway, oh, I also made a machine


learning development deployment tutorial for 20% software engineers. So it was for Stanford


CS 329 s, which is, I'll show you this is a very, very good course as well. So check


this one out. This is not in the monthly, but it'll be part of it. So CS 329 s, machine


learning systems design. In other words, how should you design your overall machine learning


system, rather than just building a model? What are all the parts around that model such


as data collection, data verification, modeling the data, model deployment, and then so on


and so on. So I made a part or a tutorial on how to deploy machine learning models.


So the deployment part of that. So and I tried to adhere to the narrative of rather than


just being this build the model and deploy, I put it together like a little like a cooking


show. If you watch the tutorial, that's what I based it on. It's like a cooking show for


machine learning model deployment. You start with a bunch of ingredients, this is a machine


learning model, and you go through a step that's like a method. And then the finished


product is your model living on a cloud service, ie Google Cloud. And you're able to use it


to make predictions. So go check that out. Now on to stuff from the community. There's


a beautiful paper sent to me by mustache and team. So very sorry if I'm and team. Very


sorry if I'm pronouncing that name wrong, but I'll do my best. And the latest paper


takes electro ECGs, EEGs, so brain scanning stuff from a brain computer interface and


passes them through a deep neural network to try and predict what key you're thinking


of pressing on a keyboard. So imagine this, you've got a helmet, and it's reading your


brainwaves. And you're looking at this keyboard, can you predict from just the brainwaves,


what key I want to press? So say if I want to press C, what does that look like in brainwaves?


Now I'll show you the paper. It's on archive may take a few seconds to load. But why is


this important? Well, I personally take using a keyboard for granted. But what if you couldn't


use a keyboard? What if you couldn't use your hands? What if you couldn't use any of your


limbs? What if you could put on this brain computer interface and it could read your


brainwaves and then you could communicate with the world by just thinking rather than


having to physically type something. So if you imagine like Stephen Hawking in his in


his wheelchair would use a service similar to this. Now, I am not well enough educated


on brain computer interfaces to know what all of these things are like state visually


evoked potentials. But long story short, I read through the paper, and they're deep neural


networks. So this is the versatility of deep neural networks, right? If you can structure


your problem into a deep learning problem, in terms of inputs, algorithm outputs, you


can achieve outstanding results. And mustache and so mustache who sent it to me, Ozman and


hoisin hoisin. Their deep neural network outperforms state of the art on both of the benchmark


data sets they used by achieving impressive infrared information rate transfers to 65


bits per minute and 196 bits per minute, respectively with only four sec point four seconds of stimulation.


Now again, I haven't done enough research into what brain computing interfaces is. But


if they're claiming state of the art on both data sets, congratulations. It was a great


paper and all the best with your future work. So thank you very much for submitting that


to machine learning monthly. And we also have Oh, yeah. So if you're interested in in brain


computer interfaces, go and check out this paper. It's to me I was reading it. It's very


similar to how I imagine things are going at neural link. Not that I know what's happening


in neural link. But if you want to see neural link, of course, you can look up neural link,


building a brain computer interface. Yeah, interfacing with the brain, a lot of those


figures. Can you put that into some sort of inputs to a deep neural network and then end


up with the outputs that you're after? On to the next one. Alvaro's guide to TensorFlow


serving. So Alvaro is back. Last time he taught us about pytorch serving. Now is TensorFlow


serving. What does serving mean? Well, it means if you have a TensorFlow model, and


you'd like to serve it to someone. So in other words, get it in their hands. Now, the beautiful


thing I like about Alvaro's blog posts is that they're completely within GitHub. And


so if you don't necessarily have your own blog or something like that to share your


work, you can do it from within a GitHub repo. So if you were applying to a job or you wanted


to show someone your work, it has code. And it has a readme with all the steps explaining


the code. So in this tutorial, Alvaro goes through and teaches you about TensorFlow serving,


which the the cool thing about this is that it kind of links back in with the ML deployment


lecture that I gave or tutorial, because on Google Cloud, we used AI platform. So if we


go AI platform, and we served our TensorFlow model through Google Cloud's AI platform.


Now the cool thing about AI platform is that it's fully managed by Google. So you just


have to upload your machine learning model, you can learn more about this in the tutorial


that I linked. But AI platform runs TensorFlow serving. So what Alvaro's tutorial is on under


the hood. So if you want to learn how to write the TensorFlow serving code that AI platform


uses, go and check out Alvaro's tutorial. So thank you very much, Alvaro for submitting


that. I really appreciate it your second time in machine learning monthly. Now we're on


to things from the interwebs. Oh, and of course, if you would like to submit anything to machine


learning monthly, send me an email to Daniel at MrD Burke.com. And with the title, machine


learning monthly submission. And you might see it in a future episode. First one, Chip


Hewins outstanding machine learning blog. So I'm a big fan of chips work. And so this


is going to be a testimonial to chip. And as well as what she puts out into the world.


So first of all, go and check out a blog as a as a whole, you'll see it in a second, the


links are here. And also follow her on Twitter if you want machine learning related stuff


on Twitter. But let's go check it out. So these are two posts that she's put up recently.


This is MLOps tooling landscape v2. We saw the version one of this post in an episode


of machine learning monthly last year. Now, this has been updated. So plus 84 new tools.


Now if you're wondering what MLOps is, the way I imagine MLOps is, it's kind of like


what we spoke about before with the machine learning systems design. If you have a machine


learning system, what are all the operations that go into that system? So not just the


model, everything around the model, all of the operations, that's what MLOps operations


stands for. If you go through here, one of the main points is increased focus on deployment.


This is a very amazing blog post that goes through what's happening in the MLOps space.