If you're just starting out, you probably won't touch these tools until you're at least


2020 Machine Learning Roadmap (95% valid for 2022)

a year in at least building your own products or machine learning powered applications.


And then if you want to build some awesome proof of concepts, use Streamlit.


I'm just going to say that a web dashboard that someone can use on their own computer,


use Streamlit, deploy it with one of these cloud services, and Bob's your uncle.


That was pretty oversimplified, but that's the main gist of it all.


So let's go back to our little flow chart here, or our mind map, or our machine learning


compass is what we've been calling it, haven't we?


So toolbox, experiment tracking.


Now we've got TensorBoard, we've got a link here.


Let's have a look at what this looks like.


Basically, as I said, machine learning is going to be training a whole bunch of different


models and seeing which one works best.


TensorFlow, I mean, TensorBoard helps you to figure that out.


Now if we go here, you could also use Dashboard from Weights and Biases.


Dashboard experiment tracking for machine learning models, a system of records, a system


of record, hmm, does that make sense to you?


Anyway, for your model results.


So here, if you imagine here, you've got runs 210.


We've got all of these different options here, and maybe these are all of your different


results there.


So you might go, okay, well this model up there, that little one there, that got the


best accuracy.


I'm going to click on that one and see what hyperparameters got that best accuracy.


Neptune AI, similar version as this.


I've personally had hands-on experience with Weights and Biases.


I actually love all of their offerings, so this is a big shout out to Weights and Biases.


Absolutely love what they're making.


So make sure you check that out.


It's becoming basically a must-have in my machine learning workflow.


And this is not sponsored by Weights and Biases.


I just really love the products they're building.


Pre-trained models.


So for transfer learning, you might want to go to TensorFlow Hub.


TensorFlow Hub is a library for reusable machine learning modules.


As I said, it's getting rarer and rarer these days to build a completely new machine learning


model from scratch.


This is how easy it actually is to incorporate, like this one line of code, or these two lines


of code, incorporate a pre-trained TensorFlow model from tfhub.dev.


So you can actually come here, find your problem.


If we want image classification, we go image, image modules.


How cool is that?


Then the same thing is with PyTorch Hub.


Now I believe PyTorch Hub, it's not as well refined as TensorFlow Hub, but it's still


got some pre-trained PyTorch models.


Great offering there.


Hugging face transformers.


Now this is, Transformer is a natural language processing architecture.


Now Hugging Face is a natural language processing research team, and they have the biggest open


source repository of transformers.


So there you go.


You can read about all of them.


Read about what it's got here.


Long story short, if you're working on text problems, you're probably going to want to


look into using transformers of some sort.


Detectron 2 is Facebook's open source software.


There we go.


Facebook AI research for state of the art object detection algorithms.


So a lot of computer vision gets done with Detectron 2.


I've had hands on experience with that.


I am probably missing some things here.


So if you have any more resources for transfer learning type models, please let me know and


I'll put them in here.


But there's some of the main ones like this will take care of almost 80% of your problems


if you check in here.


Now it's one thing to track modeling experiments.


So what changes have you made to your model and how does that affect performance?


It's another thing to start tracking data.


So remember in the machine learning process, we expand this big dog here.


We had data preparation.


So preparing your data to be trained for a model.


Now when you collect data, chances are when you prepare it, you're going to make some


changes to it.


So you might want to keep track of the changes that you've made to your data sets.


That just makes sense, right?


Like if you've got the original data set that you've collected and then you make a change


on it, you've got change one and the model does performance on that.


You got model one, you make change two, but then you rerun model one on change two.


All of a sudden you do that a couple of times and you're going to get very messed up.


But artifacts by Weights and Biases, remember how I said I love Weights and Biases offering.


Now have a way to version your data set.


So track the changes that you've made to the data that you're using and then build reproducible


machine learning models on those different data sets.


Awesome awesome tool.


Then you've got DVC, which is, I think it's open source actually.


There we go.


Open source version control system for machine learning projects.


So this is really like machine learning.


It's hitting the pointy part where it's merging with software engineering.


Remember at the start of this presentation, we talked about software 1.0 and machine learning


being software 2.0.


Well they're kind of merging into being one thing.


But data version control, check that out if you need to track your data sets, track your


models.


Now cloud compute services, you might be wondering why do I need a cloud compute service?


Well remember our overarching definition of what machine learning is.


It's finding patterns in numbers and then doing something with those patterns.


But to find those patterns in numbers is fairly compute intensive, which means it requires


a lot of computing power.


But good thing is GPUs, which is a graphical processing unit.


Let's look up this GPU.


Graphics processing unit.


Here we go.


Specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate


the creation of images in a frame per...


Oh wow.


That's a formal definition there.


What I want you to think of a GPU is just really fast at processing numbers.


Now if you don't have a GPU on your laptop, my MacBook Pro, what I'm actually using to


record this, has a GPU, but it's not an Nvidia GPU.


So there we go.


Now Nvidia kind of have like, how do you say, monopoly on machine learning GPUs.


So that's my MacBook Pro's GPU.


If I wanted to use TensorFlow or PyTorch, one of these machine learning libraries, it


doesn't work without a lot of hacking very well with my MacBook Pro's GPU.


So I need a Nvidia GPU.


Now you might be wondering how do I get one of those?


Well Google Colab provides one for free.


If we go to Colab, I'm just going to show you the dog vision project that we went through


earlier.


If I wanted to set up a GPU here, I could go change runtime type, hardware accelerator.


I can even use a TPU, which is a Tensor processing unit.


A Tensor is just a neat package of numbers.


So it's like rows, columns, and also dimensions that are going back, like the Z dimension.


And then it can actually be an infinite amount of dimensions.


So a computer is very good at visualizing things or understanding things that has more


than three dimensions, whereas we humans aren't very good at that.


Now if Colab, because it's free, it has some limitations.


If that doesn't suffice your needs, you may want to look into a cloud compute service


such as AWS.


Their machine learning offering is SageMaker.


Cool name, hey?


Machine learning for every developer and data scientist.


AI platform on GCP, which is Google Cloud Platform.


It's formerly called Machine Learning Engine or Azure Machine Learning from Microsoft Azure.


Now these, okay, they're going to have different names and fancy different marketing terms.


They're basically different versions of the same thing depending on where you work or


your own, like personally, I just like Google Cloud Platform because that's the one I've


got most experience with.


I've used AWS in the past.


I've worked with companies that have used Microsoft Azure.


It really just depends.


But basically these three big dogs kind of own the cloud computing game.


There's probably a few more out there that I haven't listed.


If you do have one you'd like me to recommend, I think there's Floyd Hub.


What's another one?


Gradient.


PaperSpace, that's another one.


PaperSpace.


Floyd Hub.


Deep Learning Platform.


But yeah, you could use these if you want.


I'm just putting these there because they're the most common.


Hardware.


If you wanted to build your own deep learning PC, that means you're doing some, maybe you're


building a machine learning startup or you don't want to pay for cloud services every


month.


These are the articles you should read.


You're basically going to need a GPU.


So read this post by Tim Detters and this article by Jeff Chen, which is arguing why


you should actually build your own deep learning PC.


I think the math comes out to it.


If you're using cloud compute for a long time, you want to build your own deep learning PC.


That's the sort of route I'm taking.


I'm building my own.


I'm currently in the process of building my own deep learning PC.


Now another tool in your toolbox is AutoML.


Ooh, auto machine learning.


So remember how I said building a machine learning model, like a custom machine learning


model by hand is becoming more and more less common?


Remember how I said right back at the start how machine learning is like figuring out


or calculating the rules or figuring out the patterns in your data set?


Why not use machine learning just to build itself?


That's the premise of AutoML.


You can also use that for hyper parameter tuning.


So just like we were turning the dials on our oven when we're cooking our Sicilian


grandmother's beautiful roast chicken dish, we could actually use machine learning on


that to figure out what the best set of hyper parameters are or the best settings on the


oven are.


So we could just end to end machine learning the whole thing.


Now there are a few different libraries that you can do.


For scikit-learn style machine learning algorithms, you want to have a look at Teapot.


We kind of touched on this before.


Really cool piece of software.


What it does is a nice graphic here.


Here we go.


Teapot will automate the most tedious part of machine learning by intelligently exploring


thousands of possible pipelines and find the best one for your data.


How good is that?


That's a great sales pitch.


Automated by Teapot.


So this is what you're going to have to do.


You're going to have to collect the data, clean it, get it ready for a machine learning


model.


Teapot will do all of these steps, such as feature processing, selection, construction,


model selection.


It will also do parameter optimization.


Then once it does all that for you, you're going to have to validate it.


So Teapot takes care of all that.


That's pretty cool, hey?


Google Cloud AutoML.


So you can come up here.


If you don't have much machine learning expertise of how to write code, you can just plug your


data into Google Cloud.


You got AutoML Vision, video intelligence, natural language, translation, even machine


learning on tables, automated on structured data.


The downside is that you need to run API calls to Google for inference.


So if you wanted to deploy your model and have it run locally on a small device, that's


probably not a great option because you're going to have to, you don't get to keep the


machine learning model if you train it on AutoML.


That's kind of a common theme with a lot of AutoML services.


Maybe that'll be changed in the future.


Microsoft have an offering.


Weights and Biases, again, have another offering for scalable, customizable, high parameter


tuning.


Now you just go to Weights and Biases and check out their whole offering.


I mean, products, amazing resources.


If you want to learn machine learning, check it out.


Documentation could do with a little bit of improving, but we're not going to complain,


really.


If you build some awesome stuff, you're going to get popular.


Finally, there's KerasTuner.


Probably a few more options here, but these are just some of the main ones.


Keras is another deep learning library or framework that is now part of TensorFlow.


So it's quite confusing, but if you go to keras.io, you can use Keras on its own.


Simple, flexible, powerful.


Sounds like me, except for simple.


I'm a complex being.


You can run it on TensorFlow 2.0.


So built on top of TensorFlow 2.0.


There you go.


Deploy anywhere.


See, TensorFlow and Keras had a love child.


And if we go back, where is it?


Get started.


Oh, there we go.


Introduction to KerasTuner is actually in the TensorFlow documentation.


So this is just going to help you tune your Keras model.


We got some stuff here.


We don't have a great, but that's all right.


Now, once you've trained a machine learning model, you might want to explain it.


We kind of touched on the what if tool before.


So the what if tool helps you to compare different machine learning models.


This is really important, right?


If we're deciding whether or not someone has heart disease, you might want to know why


the machine learning model predicted that.


So if we go here, what is it?


Compatible.


See?


Supported data and task types.


Binary classification.


We've touched on that.


Multi-class classification.


Regression.


Tabular.


Image.


This is the explainability problem.


You'll often hear in machine learning that it's a black box.


Like, you kind of pass a whole bunch of things to an algorithm.


The algorithm figures out the patterns, and then it just gives you an output, but you


don't know the patterns.


The what if tool and SHAP values, that's the other explainability tool, uses game theory


to explain the outputs of your machine learning models.


SHAP is actually pretty cool.


There we go.


You get some interesting graphics like this coming out of it.


And it's all to help with explain why your machine learning model made the decision it


did.


So again, if you're predicting heart disease, your machine learning model goes, you have


heart disease, and the doctor's looking at it, it's like, well, I don't really have anything


else to offer except that the model says that you have a heart disease.


Well, you probably want some information to go, oh, well, model noticed that your average


heart rate is 145.


And so your heart has to beat really hard to pump because your veins might be blocked


to the brim with cholesterol.


Now again, I'm making that up.


Don't take that as a real explanation.


The what if tool and SHAP values actually don't produce that, but they can produce different


things like why a certain feature contributed how much it to a certain prediction.


All right.


Now machine learning lifecycle, this is probably going to be a little bit further on in your


journey.


When you're first starting out, you're going to go through, I think mostly just in this


stage.


Like this is how I've actually tried to stage a lot of this roadmap or compass, whatever


you want to call it, is the branches here in cascading effects.


So you'll start with the libraries, you'll track your experiments, you'll look for pre-trained


controls, or maybe you might do that before that.


That's probably better.


Data and model tracking, that kind of comes under experiment tracking.


Cloud compute services.


But yeah, once we get into the full blown ML machine learning lifecycle, you want to


look at things like ML flow, kubeflow, Seldon, streamlit.


Also let's just have a look at one of these.


Also that document that we just had a look at before.


Ship, Hewin, ML Ops.


We had a look at this one before, but this is actually, this article only came out a


couple of days ago, so it's really worth checking out.


I enjoyed it.


There's a great resource right at the top here.


This is a guide to production level deep learning.


Maybe I'll just put that in there.


Boom.


And I encourage you actually, if this roadmap isn't supporting your needs, if you think


it can be improved, just make your own.


This will actually help you, what is it called?


A guide to production level deep learning.


GitHub repo.


If it doesn't suit your needs, create your own.


It'll actually, even if you just copy out what's on here, and make sure you actually


read the different things, it will help with your understanding.


So MLflow, that's what we're going to look at.


An open source platform for the machine learning lifecycle.


MLflow tracking, projects, models, registry.


Built in integrations.


These are all other machine learning tools that you can use.


There we go.


Azure machine learning.


That's Microsoft's office, I mean offering.


Microsoft office.


SageMaker, we mentioned that.


Google Cloud, Kubernetes.


That's what Kubeflow is.


I imagine Kubeflow is just a workflow that's built on top of Kubernetes.


If you don't know what Kubernetes is, it's a framework for building containerized applications.


E.g. one container does one thing, such as pre-processing data.


Another does another thing, such as model data.


If we go to Kubeflow, the machine learning toolkit for Kubernetes.


This is a lot to take in.


Again, these sort of tools won't be until later stage in your development, especially


if you're just getting started with machine learning.


But they're worth knowing about because at the end of the day, if you want to build things


with machine learning, this is where you're going to end up.


If you want to build things that get into the hands of people, this is where you're


going to end up.


One of the most beginner friendly points here, actually, we'll put it up here, is Streamlit,


or beginner friendly tools, sorry.


I've used this to create proof of concepts.


So Streamlit, the fastest way to build data apps.


Look at that.


So say, for example, you're building a model which detects cars and pedestrians and traffic


lights in photos of street photography.


This is what Tesla could use to test out how their different models are going.


Now of course, I don't know what Tesla actually use, but this is what Streamlit is intended


for to go, you've built a machine learning model.


Now let's build something that you can actually explore what that machine learning model has


learned.


And look how easy it is to get started.


Pip install Streamlit, Streamlit hello, they're two lines of code that you run in a terminal,


by the way.


There we go.


So if I just ran pip install Streamlit, I'm pretty sure I've already got it.


I'm not going to do that.


But that's a little challenge for you.


Try that out.


Try Streamlit out.


Highly recommend.


All right.


Oh, and then I just put this blog post here.


So what I learned from looking at 200 machine learning tools.


Now we've covered what, maybe 20 here.


Now again, these are just the 20 that come to my mind or the 20 that I have at least


a little bit of experience hands on using.


So there's going to be many more.


There's definitely many more that I've missed.


But the tools themselves don't necessarily matter as much as what you do with them.


So don't be, it's a typical problem for an engineer is to get obsessed with the tools


rather than what they actually build with the tools.


So right back to the start, we said we were going to explore this roadmap as cooks.


What does a cook do or a chef?


A chef uses the controlled use of fire and a knife as their two major tools.


So in fact, you can actually get majority of what you need done with something like


just using pure TensorFlow and then build a model with that and then build a proof of


concept with Streamlit.


You'd be surprised how far you'll get with just those tools.


I mean, if you use TensorFlow, you're probably also going to use TensorBoard.


So that's probably three.


And maybe weights and biases, four.


There you go.


Don't overload yourself.


Now we've gone through tools.


What are we up to next?


Come back to our keynote.


I'm going to have a little sip of water.


As you might have read by this, this is point four, machine learning mathematics and this


beautiful little abacus emoji.


There wasn't one for a calculator, but that's all right.


Let's have a look.


Now, machine learning mathematics, these are only some of the main ones.


And as I said, I'm not going to be going into each of these in depth.


I'm just really basically going to be listing the names because one of the main questions


I get people asking questions about machine learning is how do I learn the math?


And I think it's because math has actually taught pretty poorly in school.


When I started learning machine learning, I started to like math way more.


I mean, I've always been a numbers dude.


I like numbers.


But when I started to learn machine learning and I realized, wow, it's actually just applied


math and math is really just the language of nature.


So machine learning is one part linear algebra, one part matrix multiplication manipulation,


especially if you're using neural networks, one part multivariate calculus, one part the


chain rule.


This is basically the entirety of backpropagation, which is how many neural networks learn basically


or improve their errors is through backpropagation, probability and distributions, and then optimization.


So, again, when we said finding patterns and numbers, how do you find the optimal pattern


in numbers?


So if you imagine this little top of the hill, that's the maximum.


Usually in machine learning, you're trying to find the minimum.


So you're trying to reduce the loss function, which is usually something that measures how


wrong your machine learning algorithms predictions are to what they should be.


So that's an optimization problem.


But if you just literally go to the Wikipedia page for each of these, read just what they


are.


If you went through high school, you might have covered them.


Do you need to know the ins and outs of all of them to get started with machine learning?


No, my approach is write some machine learning code and then learn these parts when you have


to.


So let's come back.


Machine learning mathematics, what's running under the hood?


There we go.


So linear algebra, creating objects and a set of rules to manipulate these objects,


eg, x squared, x is the object and squared is manipulating that object.


Machine learning is about finding the right set of objects and the right set of rules


to model a data set.


Well, that's a pretty cool explanation, if I do say so myself.


Again, you could read through this, you're going to get a lot more complex explanation.


I can only fit, I'm sure, as you'll imagine, someone will probably get angry at me for


describing these in only a couple of sentences.


I can only fit a couple of sentences here.


So if you have a better explanation, please let me know.


But there we go, linear algebra.


If you want to read through that, look, if you haven't done math in a while, you're probably


going to be like, well, that's a lot of things I don't understand.


But then what you're going to do is you're going to go three blue, one brown, linear


algebra.


And you're going to watch all of these.


And you probably, again, still won't understand it.


But then you're going to go to Rachel Thomas, computational linear algebra.


And then you're going to learn linear algebra with code.


Look at this bad boy.


We got code in here.


I think this just might be an intro.


There we go.


We got some code.


So I'm going to, where is it?


Come back here.


I'm going to copy this.


And I'm going to put this in the roadmap.


Linear algebra.


Boom.


Actually, this should probably go in resources, but that's all right.


Computational linear algebra.


Well, that's how I do it.


You can do whatever you want.


I'm not your boss.


But that's how I go about these certain things.


If I need to know a topic, I go to multiple different sources to figure out things.


I compress the knowledge in my own words into a couple of sentences and then start to build


upon that.


Now you might be thinking, Daniel, when should I start learning all of these different math


things?


Well, my biggest thing is, can you solve the problem you're currently working?


This is no shortage amount of content here.


This is a lot of math.


They do have overlaps.


Yes, you're right.


But if you just go and try and learn these willy nilly without having something to relate


it to, at least in my case, I find it very hard.


Like that's just pushing a boulder uphill.


When I am working on a problem and I am stuck on something, then I find it really easy to


learn something like matrix manipulation.


In machine learning, data, all kinds of it, often gets turned into rows, columns and features.


Features is the third dimension.


It actually is the end dimension.


Oh, which can actually be many dimensions and numbers.


Actually forgot what I wrote.


These collections of numbers are often referred to as matrices or tensors.


So then I go to something like that and then I go to here, I go to Wikipedia page, I read


through it.


I go, yep, yep, yep, that's matrix multiplication.


I try it out for myself.


So as I said, I'm not going to really go through these other than just a high level overview


of them.


You'll probably want to know some multivariate calculus, probability distribution, probability,


optimization, the chain rule.


Again, how much?


Can you solve the current problem you're working on?


You'll probably find that 99% of the time you can with code.


That's in the engineering sense.


If you do want to be an artificial intelligence researcher, chances are you're going to have


to know these inside out.


So get good at math, read this book End to End, the machine learning for mathematics


book, my favorite resource for learning mathematics for machine learning.


Here you go.


Some of the things we've just talked about.


Simple foundations, you've also got the deep learning book, fast.ai deep learning from


the foundations and various other resources.


So see the resources branch.


That's math in a nutshell.


The reason why I've gone pretty quickly over that is that I'm not taking away from mathematics,


but for some reason the most common question I get is how much math or one of the most


common questions I get, how much math do I need to know?


They're scared of learning math because some high school teacher said they were bad at


math or they didn't teach it very well and they didn't tell them that math is actually


the language of nature and it's actually beautiful once you start to get into it.


They look at the Greek symbols and they're like, wow, I can't do all this, but you actually


can.


So my approach is to start learning code first and then learn math when it's required.


So if we come back to the keynote, these are the topics that you're generally going to


touch on.


If you want an overview, again, just read the Wikipedia page for each of these.


Even if you don't understand it, over time you'll start to get used to seeing these different


terms, these different symbols or whatever, and you'll go, okay, I'm reading this machine


learning document and suddenly the Greek symbols don't look too out of place because I know


that under the hood, all this code that I'm writing, all of this machine learning code


is actually just executing mathematics for me.


Wonderful.


So let's go into the final step.


Number five, everyone's favorite machine learning resources.


So you might be thinking, well, let's come back to the roadmap.


We've gone through problems, we've gone through process, we've gone through tools and we've


gone through the mathematics, or at least just covered each of these in a high level


overview.


You might be thinking, this is great, Daniel.


How do I learn all these things?


Well, let's go through that now.


So let's click here.


Whoa.


Boom.


This is a pretty big dog one as well.


Now again, we've got here.


Note, there's a lot here.


My advice is each of the resources are great, but you can't use them all.


Choose a couple.


Start with beginner if you're a beginner, explore, figure out what suits you and doesn't.


It's pretty poetic, right?


You might like the videos instead of books, but eventually you will have to learn to love


reading.


I'll put it this way, there's only so much videos can cover, but books, you can have


a 700 page book like the one I'm holding in my hand right now that you can't see that


says Hands-on Machine Learning with Scikit-Learn TensorFlow, Keras and TensorFlow.


To turn that into a video course, it'd be hundreds of hours.


So learn to love reading.


And often the latest and greatest research is published in text form, not video form.


If you're just getting started, accept the materials here to be plenty enough to keep


you content for two to three years.


The equivalent of an undergraduate degree.


Now I'm being serious with that.


You'll probably see a lot of things online like learn machine learning in six weeks.


Sure, you can, you can learn the overall concepts, but if you want to get really serious with


it, you'll probably realise that once you get into it, machine learning is not just


about learning models.


The majority of machine learning problems are actually infrastructure problems.


So software engineering meets machine learning.


And we've covered a few things on software engineering meeting machine learning.


Remember, that's called ML Ops.


So make sure to check out that blog post on ML Ops.


But without any further ado, let's get into machine learning resources.


So if we come back, I've actually made another little cool graphic where to start learning.


Oh, look at that.


That's pretty cool.


So if you're an absolute beginner, expect this little flow chart here to take three


to six months.


If you're an advanced learner, you've got some familiarity with all of these, go through


something like this, expect this to take six to 12 months plus.


Remember, there's no rush here, right?


If you're learning something worthwhile, it's going to take a while.


Now the bonus, you can sprinkle in the only limit here on time is how much effort you


put in.


So me personally, I can't really study more than four hours a day.


After that, I've kind of found out that's just my limit.


My brain starts to go to mashed potato.


So yeah, four hours per day is kind of what I've modeled this off.


If you can do two hours, you're probably looking towards the latter end of this, and maybe


a bit more.


If you can do four hours, you're probably somewhere smack bang in the middle of both


of these numbers.


Anyway, while you're doing that, it'd be a good idea to start sprinkling in some of these.


So this is a missing semester, the missing part of your computer science degree.


This is going to teach you this little curriculum here.


We'll teach you a lot of the little parts that machine learning courses tend to miss


out on.


And that's just some computer science things.


You'll probably also want to choose one cloud provider that you get familiar with, because


as you might have seen before in my Airbnb project, because I knew how to use Google


Cloud, I could get my code out into the world.


That means someone could access what I've done in a web browser.


Now if you want to learn web development, free code camps, probably one of the best


resources online.


Khan Academy, which is great for math when needed.


If you want to figure out or find state of the art research, you probably want to visit


archive.


That's all the technical papers of computer science, physics, mathematics, and all that


sort of stuff.


And if you want to version control your code, which is where you save the code that you've


been writing to multiple versions.


So if you code on day one, when it breaks on day two, you can revert back to day one.


But if you want to add a book to this, I would highly recommend part one of the hands-on


machine learning with Scikit-learn, Keras, and TensorFlow for this little beginner section.


And part two for this advanced section.


So what you might do is let me walk you through this.


You might go machine learning concepts, get your mind ready.


We've covered a lot of the concepts in this video, and there are plenty of resources linked


to the roadmap.


So go through some of the concepts.


You're going to learn these tools, Python within Jupyter or Google Colab.


So Python, NumPy for numerical Python, so remember, machine learning is turning data


into numbers and manipulating those numbers.


A lot, a lot, and I mean a lot of data processing of numerical processing is based on how NumPy


processes data.


So that's why it's worth knowing.


Pandas is going to help you manipulate structured data.


So like tables and Excel spreadsheet type data.


Scikit-learn has got a whole bunch of machine learning functionality and machine learning


algorithms built in that you can use.


So this is at least six months worth of work.


At the end of this, you want to build a milestone project, at least one, and that's with Streamlit.


Of course, it doesn't have to be a Streamlit.


I'm just recommending Streamlit, what we went through before, because it's actually a beautiful


tool and just allows you to get some experience writing Python scripts.


So definitely check out Streamlit.


Remember this is like four hours a day.


So if you can't do that, just extend this timeframe.


Now advanced, I'd recommend something like this.


So once you've done all this, go through fast AI part one, TensorFlow and practice from


deep learning.ai.


After you've done those two, you can do those two in parallel, do these two in parallel,


then you want to check out full stack deep learning, which is building a machine learning


model and bringing it to production.


There you go.


Learn production level deep learning from top practitioners.


That's what's up.


Then you got a milestone project two.


So these are probably the two most important parts.


Now you don't necessarily have to wait to do these until you've learned some of this.


Like you could do these as you're working through these, like your whole learning journey


could be purely just building projects.


That's actually what I'd recommend.


And at the same time as while you're going through this, read part two and then sprinkle


in these and reason why I say sprinkle in is because it's hard to really know when you're


going to need which parts of these.


You're smart enough to use your own best judgment as to when you might need some more computer


science knowledge, when you might need a cloud provider.


And again, if I'm saying these things and it sounds like, whoa, Daniel, you're going


way too fast.


This will start to make more sense once you've gotten hands on.


There is no way that you're going to know what you'll need from here until you've gotten


hands on.


And then if you want to learn this, this is a plug for my machine learning course.


This is what I teach in dberk.link.com.


Andre teaches the Python part.


I teach the machine learning part.


So the machine learning concepts, the machine learning tools, and Andre going to teach you


the Python part.


So that's my business partner.


And if you want to learn this beginner step, check this out.


We designed it specifically for someone who's brand new to machine learning and wants to


get started with all of these tools.


These resources are actually also all here.


So if you want concepts and processes, these are all free.


Elements of AI is actually really cool.


All of Google's machine learning course is open source and free.


I mean, this is what's up.


If you want to learn these things, the resources are there.


So look at that, machine learning crash course with TensorFlow from Google.


So if you don't even want to take my machine learning course, just take Google's course.


I mean, there you go.


Google's AI for education, Facebook's field guide to machine learning, Facebook also do


some massive amounts of machine learning.


Oh, made with ML topics.


You also want to check out this.


This is one of my favorite new websites for machine learning.


Made with ML.


Check this out.


If you want to learn Python, it's got a whole bunch of topics where you can learn Python.


If you want to learn TensorFlow, it's got a whole bunch of machine learning basics, tutorials,


hands-on machine learning, algorithms, linear algebra, linear regression, decision trees,


convolutional neural networks, transformers, attention, text classification.


These are all subsets of machine learning, computer vision, natural language processing.


So definitely check this out.


There is no shortage of resources on all of this stuff.


The best way to do it, full stack.


There we go.


Boom.


Full stack, APIs, Docker, web scraping.


If you wanted to collect your own data, you'd probably look into web scraping.


I mean, this is phenomenal, like a phenomenal resource.


But as I said, it will require you to choose something.


If you're in doubt of what to choose, choose anything and just go with the flow.


Follow your own curiosity.


That's how I always advise people, to follow your own curiosity.


And then if you've got some skills, you can test them on workerror.ai.


Whoa, sounds pretty fun.


Standardized tests for AI skills.


So take the test.


This is after you've been learning machine learning for a while, or AI or whatever.


You can sign in here.


I think I have an account.


Anyway, you take some tests.


It evaluates your skill levels on different things.


And if you want to, you can check out how to boost your skills.


Prepare for the machine learning test, prepare for the deep learning test, the data science


test.


Figure out if you know about machine learning algorithms, figure out if you know deep learning


algorithms.


Look at this.


Amazing.


So learn some stuff from Made with ML, go through these topics, and then test your skills


on Workerror.


And then the real test of skills is actually not even any type of exam.


It's actually what you've built.


I'm on fire here.


You enjoying this?


Leave a comment below.


Let me know if you're enjoying this, because I'm having an absolute blast here.


I hope you're finding some value out of this, by the way.


I tried to put as much in here as I could.


So it's just like a really just a one stop shop for getting started with machine learning.


Beginner.


If you're completely new, start here.


There we go.


If you're completely new to machine learning, start by learning some Python code first.


Okay.


If you want to learn Python code, you can learn Python in one video on YouTube by Free


Code Camp.


You can do the Zero to Mastery Python course.


So this is full stack Python taught by my business partner Andre from the Zero to Mastery


Academy.


Look at that.


See, Zero to Mastery Academy is this is a big disclaimer.


I'm part of this, right?


So Andre, there we go.


He teaches the Python part of the machine learning course.


But this is just to show you resources of where you can learn these things.


You don't even have to use anything by us.


There is so much out there.


You got Python like you mean it, which is more Python, but for numerical computing.


So like STEM applications.


There we go.


Data analysis, machine learning, numerical work.


If you want to learn Python specifically for these type of things, you probably want to


go through there.


Anyway, tooling.


You will need some tooling bare bones to start writing Python code.


So you might want to look into Anaconda or Conda or Python Virtual M for managing all


of your Python code.


Jupyter Notebooks is where you write and explore machine learning code.


If you want to learn it all in one place, the Zero to Mastery machine learning course,


if we go here, again, massive disclaimer.


This is what I made.


We got all these topics in here.


You'll find that on the Zero to Mastery Academy or on Udemy.


You can also go to the Kaggle Learning Centre, Faster Data Science Education.


I'm pretty sure this is all free.


Data Camp, Data Quest.


Now if you want example projects, once you've got three to six months plus of beginner work,


the next step is to go to the advanced path.


So if we go here, you want to also have done, I can't stress this enough, a milestone project.


No matter what an instructor, including myself or including anyone from another course or


whatever, no matter how much they tell you about these things, including this video,


it won't matter until you put it into practice.


I was going to say into project, but that didn't really make sense.


Remember when your parents told you that the stove was hot?


I'm sure.


How many times?


Mother and dad or mother and father or whoever looked after you when you were growing up,


the stove is hot.


The stove is hot.


Don't touch it.


How did you figure out that the stove was hot?


You touched it, right?


You touched it.


You found out it was hot.


Didn't touch it again.


So this is what a project is.


These concepts here are the equivalent of your mother and father telling you that the


stove is hot.


The project is you touching the stove and figuring out, okay, you've told me all these


things.


Now it's my turn to touch the stove.


So once you've done some beginner stuff, come into the advanced, have a look at some end


to end projects, what they look like.


This is probably before you got into the advanced stuff here.


This is probably what you'd want to be working on is some projects like these.


So here's an example binary classification project that I did, which is the heart disease


one.


If you can like look through this or, or run the code through all of this, going through


the beginner path and understand it and write it yourself, then you're ready to go on to


the advanced.


And now I'm scrolling through this and there's a lot here, but as I said, remember, this


is a three to six month journey to get from zero to going through all of that minimum.


But then you'll probably want to go through deep learning.ai curriculum, the fast.ai curriculum.


Remember you can go through these simultaneously.


After that, you want to look at full stack deep learning, AKA don't let your models die


in Jupiter notebooks, get them live into people's hands.


See what you can do with machine learning.


If I had my time again, I would go projects first.


Don't get obsessed with the latest and greatest tool.


Just build something that, that brings value to someone, to anyone.


And then at the same time, I'd probably get proficient with at least one cloud service.


So again, choose one of these to get proficient with.


Finally, what's missing from machine learning, most machine learning curriculums is general


software engineering practices like Docker, missing parts of your CS degree.


If you wanted to go really, really deep, you could teach yourself computer science at teachyourselfcs.com.


Then we had the mathematics.


So linear algebra, calculus, matrix manipulation, neural networks, probability, linear statistics.


If you wanted to learn something from all of these, you go to the essence of linear


algebra page.


Rachel Thomas's computational linear algebra.


This is linear algebra with code.


Let me put that here.


Rachel Thomas, computational linear algebra.


Add link.


There we go.


See, this is a living and growing document.


So if you want anything added, let me know.


Python, if you want to read some books on it, try automate the boring stuff with Python.


Here are some of my favourite data science books, machine learning practices and code.


Would not recommend this book enough.


Hands-on machine learning with scikit-learn and TensorFlow.


I've literally got that sitting right next to me.


See, there we go.


You last purchased this item on 11th of April, 2020.


Deep learning for coders by the fast AI teachers, Jeremy Howard and Sylvia Ngugger.


That's coming out in July, 2020.


So this is actually a preview of this book.


Look at that.


You come to this video and you get all these things that aren't even out yet.


Come on, Daniel.


Interpreting machine learning pipelines, this is more of a full stack machine learning stuff.


That's actually coming out later this year too.


So stay tuned for that.


Interpretable machine learning.


So that's explaining why your machine learning model is making the decisions that it's making.


Mathematics.


Now, the number one book we've already covered, this is mathematics for machine learning.


Read that end to end if you really want to get an overall concept.


You can also get, if you just want the bare bones, matrix calculus you need for deep learning.


You've got this book online by Jeremy Howard, who's the fast.ai instructor.


So if you come through here, when you first read this, you're probably going to look at


these Greek symbols and going, whoa, that's a neural network there and going, whoa, what's


happening here?


What is that?


But as you start to read through this, you'll start to see, there we go, power rule, sum


rule, difference rule, product rule, chain rule.


You'll start to see, okay, I can start to piece together these different bits and I


can start to read these heavy equations and go, yeah, okay, they're starting to make sense.


Now truth be told, if you asked me to go through this and read it all, I probably can't.


What it takes me is instead I have to revisit these and learn these things as I go as well.


So if you're watching this video and thinking that I'm some sort of expert on all of this


stuff, please don't get too far ahead of yourself.


I'm still learning all of this as well.


If you want to learn a cloud service, a cloud guru is probably one of the best places to


go.


I've done a few courses on there, especially getting certified with Google Cloud, highly


recommend that.


And then Google Cloud, if you want to learn that, AWS, Microsoft Azure.


Remember, you only have to really choose one of these.


Some rules and tidbits.


Oh, these are my favorite kind of articles actually.


Andrej Kapathy, he actually did that Tesla Autonomy Day talk that we referenced at the


start of this video.


A recipe for training neural networks.


Now every so often we'll get blessed with the privilege of finding one of these amazing


blog posts by a practitioner.


So this is why my advice, look at this, where's number one?


Become one with the data.


That's step one in back over here in our machine learning process in data preparation is exploratory


data analysis, learning about the data you're working with.


So coming back to the blog post, every so often we'll get blog posts like Andrej Kapathy's


recipe for training neural networks, practical advice of building deep neural networks.


We'll get things like these.


And now although you might be thinking, well, these are just blog posts, they're not really


like hard rules, but what did I say all throughout this video is that machine learning is experimental.


So it's only really after trying things a lot of times that you start to realize, okay,


this works, this doesn't work, this works, this doesn't work.


And so every so often we'll get blessed with a beautiful blog post like this.


And my advice is always create your own blog.


Yes, you should have one.


Now if we come up, have a look, you should read this article by Rachel Thomas, why you


should have your own blog.


But one of the main reasons is that so you can craft these beautiful blog posts.


And now I'm not asking you to write a blog post like Andrej Kapathy for training neural


networks.


This took probably 10 years worth of experience to write something like this, but write something


for yourself six months ago.


If you've just gone through this beginner pathway, write the things that you've learned


or what's wrong with this pathway?


Did I do it wrong?


Or if you'd like to have known something when you started learning machine learning, write


about it because truth be told, there's enough resources out there for learning the code


and whatnot.


What there isn't enough resources out there is for the process around learning these topics,


for the process around the bits and pieces that aren't just code, like just the general


how things fit together, like this machine learning mind map.


So write things like that, help your previous self.


And what you'll be surprised to find is that a lot of people probably have the same questions


that you do.


So if you want to create a blog, try fast pages or GitHub pages or medium.


There's a whole bunch of different reasons that you can try.


But what writing does is it shows you how poor your thinking is.


So when you think you understand something, try write about it and teach someone else


about it.


That's when you'll really start to understand it.


Some bookmarks, archive sanity.


So if you want to look through the latest machine learning research, check out this


little tool, helps you weed out some of the best stuff, some of the most popular stuff.


Made with ML.


So this is a community driven resource for your projects.


We've actually just been through that.


But if you do make a machine learning project, you should definitely post it there, whether


it's something as simple as a blog post of 10 things I learned in my first machine learning


course should put it there.


Or whether it's something phenomenal.


I'm not saying that 10 things I learned during my first machine learning course isn't phenomenal.


Or whether it's something crazy like I turned my car into a self driving car.


You should put whatever you're doing, put it there.


Everyone has to start somewhere.


I cannot stress this enough.


So we come back to the keynote.


Where are we up to?


Oh, example curriculums.


Before we finish off the tools, if you want to figure out or if you want to teach yourself


machine learning using something like we've covered up here, the beginner or the advanced,


I'd highly recommend you check out these two posts.


This is by yours truly.


So I'm pretty biased in that one.


But you can see how I kind of do it.


That's my own machine learning curriculum, studied machine learning.


And you can read Jason Ben's post on how I learned web development, software engineering


and ML.


I actually I'd actually favour his truth be told, like if I was studying again, I'd do


it something like this.


So check out this article.


And I do something more like this, but I'd construct it in the style of mine.


Because I actually just tweeted out something today.


If we go to Daniel Burke Twitter.


Here we go.


How I'd learn machine learning in 2020.


80% software engineering web development.


B machine learning 20%.


If you're interested in building things, B doesn't exist without A. So I'd learn how


to build software products and then add machine learning in later.


So that's how I would do it.


But yeah, check out those posts.


This one's a really great one.


And then you can also check out my style in terms of how I would design actually creating


a curriculum that's actually a cool startup idea.


Or a cool product idea is being able to create your own learning curriculum using resources


online and then potentially get in other students who want to do a similar approach and teach


themselves and you kind of have a community around learning the same thing.


That'd be pretty cool.


If you build that, let me know.


So that's an example curriculum.


The next one was some useful places to visit.


Sota Bench papers with code made with ML.


So Sota Bench is where you're going to find all of the state of the art machine learning


models benchmarked on a number of different datasets.


Papers with code is where you'll get the latest machine learning research with code attached


usually.


So although it's called papers with code, I think one of the criteria actually for something


to come up on papers with code is that it has to be a research paper.


So like state of the art machine learning, but with code.


But don't get too distracted by these.


You want to build things with machine learning and that's where you can find on made with


ML.


Yeah, there's papers with code.


There's Sota Bench.


If you want some datasets to where to find different data sources, if we come back up


here back to process, one of the steps in machine learning is data collection.


So one of the ways that you can collect data is through existing datasets.


So Google dataset search, Kaggle datasets, a curated list of free datasets by DataQuest.


If you have anything else that you want to add to this, please let me know.


Cool resources.


How do I actually learn all of this?


Now I'd highly recommend go do the learning how to learn course on Coursera.


That'll basically teach you how to learn.


Or there is actually on Zero to Mastery, a learning how to learn course by Andre.


Where is it?


There we go.


Learning to learn.


Zero to Mastery.


If anything, out of all of these courses here, even my own, I would say do this one first


and then do something else.


So if we come back here, read this article by yours truly.


This is what helps me study machine learning five days per week.


And then if you want to create your own curriculum, as I said, this is my self created AI masters


degree.


But if I was to do it again, I'd probably do something more like this by Jason Ben and


as in learning machine learning plus software engineering plus web development.


So these two software engineering and web development are basically all about getting


things into people's hands.


Whereas a lot of machine learning is about building proof of concepts.


But for me personally, I want to get things into people's hands.


So that's where I'm going to be spending a lot of time and my next learning journey is


software engineering and web development.


But if you want to go into just pure like AI machine learning research, you'll probably


just want to go full blown ML and lots of mathematics.


So I think we have actually come to the end of the roadmap, but it's really not the end


of the road, isn't it?


Let's go back to the keynote.


And summary plus next steps.


What we've covered, did you like that?


I kind of did a little artistic title there and crossed out that bit.


So what we've covered broadly, we started with machine learning problems.


What does a machine learning problem look like?


We went through the process of how you might go about trying to solve one of these problems.


We then looked at the tools of what you should use to build your solution, which is kind


of an endless list at this point.


Then we figured out the machine learning mathematics or at least just covered the names of the


different things.


Remember the knowing the name of something is not understanding it.


That's what's happening under the hood of all the machine learning code that we write.


It's a mathematical problem.


And then we have machine learning resources.


So all of this is pretty cool.


How can you learn all of it?


Well, that we saw that.


There's an abundance of resources online.


You can actually create your own curriculum if you really wanted to.


And you can with some effort go through learning all the things that we've covered.


And how I recommend when you're first starting out, be a cook, not a chemist.


Don't go for exact things like explore things, follow your curiosity.


And then as you start to learn more, slowly fade towards being a chemist and making sure


that what you're building is solid.


What's missing?


Good first model based learning, there's another type of learning that we kind of didn't cover.


We could have actually covered that it's not too, too difficult, but that's all right.


Machine learning pipelines still emerging on best practices, more specific topics such


as computer vision, NLP and RL.


We kind of did hint on these, but I kind of wanted to cover the real foundations that


you'll find across the majority of machine learning projects.


And then, of course, probably much more.


So leave a comment anywhere that you can leave a comment.


If you'd like something to be added or if you think something should be corrected, please


let me know.


Sources, so there definitely has to be some thank yous and rounds of applause.


That emoji up there was like a little trigger for me.


Daniel Formoso's machine learning mind maps, you should definitely check those out.


Hands on machine learning book second edition is where I got a lot of these concepts from


saying with the hundred page machine learning book and countless blog posts.


Check the mind map for all of the links.


So I believe that is it.


The machine learning roadmap for 2020.


We have a lot of different branches here.


So my challenge to you is to explore, to leave a comment, to even make one of your own if


you want to really start diving into understanding what's happening with machine learning.


Try to go through some resources if you want to learn, try out a few of the tools, see


if you can start to diagnose machine learning problems, see if you can start to theorize


what steps, like if you have a problem, what steps might you need to take in that project?


And then when you're ready, start approaching the math because after all, that's what's


happening under the hood when you write machine learning code.


Keep learning, keep creating.


Oh, and machine learning that is.


So thank you so much for sticking around.


If you're all the way to the end, I hope you had a lot of fun.


I had a lot of fun.


See you next time.


I think I need a nap after that.


What a ride, eh?


We covered a fair bit and that's a really important point.


Because we covered so much, don't expect to know it all in one hit.


I mean, I put it there and as I was putting things out, I was finding myself relearning


them as I was connecting the dots.


So that could be a really good practice for yourself is to play around with the roadmap


and then potentially create one of your own.


So you can connect the dots in your own mind and visually.


I like to see things visually laid out.


But most important point, all the resources you need are in the description below.


Check those links out.


If you have any other questions, leave a comment and I'll get back to it.


Otherwise I said in the video, I'll say it again.


Keep learning, machine learning that is, and keep creating.


I'll see you next time.


2018: Thank you

What is happening guys? You know I want to keep this video nice and short


2018: Thank you

This is going to be the last one for 2018 and what a year it has been


2018: Thank you

I mean, I just wanted I wanted to just make this purely to thank you all


2018: Thank you

I mean at this time last year. I was reflecting back


2018: Thank you

I was driving uber studying my own AI masters degree trying to trying to figure out


2018: Thank you

Where I would end up not sure what I was doing making videos, but now


2018: Thank you

It's amazing what what a little time and effort can do and I I really like I've got no script


2018: Thank you

I'm just just going straight from the heart straight from the fly I


2018: Thank you

Can't thank you all enough for the support over the past year or so whenever you join the channel if you're new here


2018: Thank you

Welcome, it's it's been an amazing journey. I


2018: Thank you

Couldn't I honestly couldn't have done it like I started making these videos as a as a way to keep myself accountable


2018: Thank you

And now it's become part of part of the journey


2018: Thank you

It's it's it's actually probably the leading leading reason why I keep keep doing what I do


2018: Thank you

The study and data science staying theater is to is to create to learn to learn to create


2018: Thank you

They're the two things. I really really enjoy doing and so what's what's going to happen for 2019?


2018: Thank you

Who knows I think I'm going to keep going with with what I've been doing if you have any suggestions leave a comment below


2018: Thank you

I won't be able to get to it for a little for a few days because I'll be on holidays my family


2018: Thank you

We're actually packing up now


2018: Thank you

It's it's a it's the house a bit of a mess at the moment because because we're trying to trying to pack it all in


2018: Thank you

Go into an island we go there every year as a family


2018: Thank you

It's a really good time to to just just chill out and think about think about what's next and I really enjoy it because it's


2018: Thank you

Like a it's like a way to reset the year


2018: Thank you

So it happens at the back end of every year and what I plan on doing there is is thinking about


2018: Thank you

What I'm going to do for 2019 right? I usually go over there reflect on how I've spent the year prior


2018: Thank you

So I'll go over think about what I've done this year and then plan out


2018: Thank you

2019 plan out next year what I want to do


2018: Thank you

But I think it's going to be similar to what what I've been going


2018: Thank you

But if you have any if you do have any suggestions, you want to see anything from me


2018: Thank you

Leave a comment below. I'll check it out


2018: Thank you

I've got a lot of suggestions. I've got a lot of ideas and I really appreciate it from you all


2018: Thank you

I've got a couple of books. I'm going to be reading


2018: Thank you

Marketing another one from you. You've all know Harari lessons for the 21st century


2018: Thank you

My best mate shout out to today, but he got me this hat check it out


2018: Thank you

I'm not sure if you can read it, but it says there we go. Mr. D burr creator AI fitness


2018: Thank you

So I'm gonna be rocking this hat


2018: Thank you

Plenty more to come in 2019. Once again, thank you. Thank you to you all


2018: Thank you

There's too many names to to list to list everyone but Shayek Hamad


2018: Thank you

Hassaf Paul Garcia all the names I see regularly in the comments you guys you're amazing and everyone else


2018: Thank you

You're amazing


2018: Thank you

But otherwise have a happy new year


2018: Thank you

Spend some time with your family and we'll be back next year ready to go. It's gonna be a big big year on tour


2018: Thank you


3 Benefits of Cold Showers

What's going on everyone? Welcome back to day 88 of the Daniel Burke Show. Two eights!


3 Benefits of Cold Showers

That's awesome. What an awesome number. Thank you so much for tuning in on YouTube and the podcast.


3 Benefits of Cold Showers

Hope whatever you're up to, you're having an amazing day. I'm feeling incredible.


3 Benefits of Cold Showers

You know why? Because I just had a cold shower. Now, if you've never had a cold shower,


3 Benefits of Cold Showers

I highly recommend trying it. That's actually today's challenge. I'm going to do the challenge right up the front.


3 Benefits of Cold Showers

So the challenge is, have a cold shower. Now, even if you go, I don't know, your normal temperature


3 Benefits of Cold Showers

for the whole shower in the last 30 seconds, turn it on cold. Now, over the past couple of days,


3 Benefits of Cold Showers

I haven't had the greatest sleep quality. I was at a friend's place the other night and


3 Benefits of Cold Showers

I was a bit late here last night because of a party and whatnot. But, oh, by the way, I'm still in Noosa.


3 Benefits of Cold Showers

I'm here for a wedding today. It's my first wedding. I've never been to a wedding before.


3 Benefits of Cold Showers

I've DJ'd at weddings and stuff like that. But today, in a few hours' time, some of my good family friends


3 Benefits of Cold Showers

are going to get married. So congratulations to SJ and Paul. But nonetheless, today's episode is going to be on the...


3 Benefits of Cold Showers

I found some benefits of cold showers and I've been taking them for, I don't know, the majority of this year


3 Benefits of Cold Showers

and some of last year. And I would say 80% of my showers are cold now, like fully cold.


3 Benefits of Cold Showers

I don't turn on any hot water. I get in there and it's cold. And the reason being is because I...


3 Benefits of Cold Showers

Regardless of the facts, I genuinely feel better after them, just the way I feel, regardless of the health benefits


3 Benefits of Cold Showers

and the science behind it and whatnot. But after the first few times, you do not like it.


3 Benefits of Cold Showers

But I'd say after you do it a half a dozen times, the feeling starts to get addictive.


3 Benefits of Cold Showers

Like when you... Even if it's only for like 30 seconds or a minute, it starts to get really addictive.


3 Benefits of Cold Showers

Like when you get in a cold shower and you come out and you've got all this energy.


3 Benefits of Cold Showers

It's like you've just had a cup of coffee. Like last night, I probably had maybe six... under six hours of sleep.


3 Benefits of Cold Showers

But now I'm feeling great. I'm sure it's going to catch up with me later.


3 Benefits of Cold Showers

And I'm not advocating to replace sleep with cold showers. But let's just dive straight into science.


3 Benefits of Cold Showers

What's the science say? So I picked out three points that stand out to me out of the hundreds of things


3 Benefits of Cold Showers

that you'll find online about cold showers. But number one is that it stimulates brown fat.


3 Benefits of Cold Showers

And now why? Why is that good? Like what? What's the difference?


3 Benefits of Cold Showers

Brown fat... So your body's got two types of fat. Brown fat, white fat.


3 Benefits of Cold Showers

You think of it like this. Brown fat is good. White fat is bad.


3 Benefits of Cold Showers

White fat is that fat you get around your gut and your stomach and the fat that people generally see as bad.


3 Benefits of Cold Showers

Whereas brown fat is more... It's actually up in the back of your neck.


3 Benefits of Cold Showers

It's in different locations of the body, but it's good.


3 Benefits of Cold Showers

Stimulating brown fat like helps to burn white fat.


3 Benefits of Cold Showers

And I don't know the full science behind it, but from what I've read is that taking cold showers,


3 Benefits of Cold Showers

there's been studies done just taking a cold shower can stimulate brown fat that much


3 Benefits of Cold Showers

that you can lose up to nine pounds over the course of a year just by how many calories brown fat burns


3 Benefits of Cold Showers

because of the stimulation you're giving it from taking cold, from cold exposure.


3 Benefits of Cold Showers

That's what we're looking for. Not necessarily cold showers.


3 Benefits of Cold Showers

You could take a cold bath. You could swim in a lake or whatever, but cold exposure is the key here.


3 Benefits of Cold Showers

So that's number one. It stimulates brown fat.


3 Benefits of Cold Showers

Number two, it improves immunity and circulation.


3 Benefits of Cold Showers

So the deep breathing to keep you warm improves your blood circulation.


3 Benefits of Cold Showers

So when you breathe in heavily from the shower, that is essentially your body trying to take in more oxygen to keep it warm.


3 Benefits of Cold Showers

And how does it improve your circulation?


3 Benefits of Cold Showers

So you have increased blood flow around your major organs to keep them warm.


3 Benefits of Cold Showers

And with increased circulation, you release more white blood cells, which is good for immunity


3 Benefits of Cold Showers

and just general circulation, much better.


3 Benefits of Cold Showers

And so again, it's from the cold exposure. It's your body trying to adapt to the cold.


3 Benefits of Cold Showers

And we'll go off on a little bit of a tangent here.


3 Benefits of Cold Showers

Hot showers are totally not necessary. They're a complete luxury, right?


3 Benefits of Cold Showers

And I'm guilty. I've had hot showers my whole life.


3 Benefits of Cold Showers

You sort of take it for granted sometimes.


3 Benefits of Cold Showers

Whenever the cold water, the hot water doesn't work in your house or your apartment or whatever, it's a burden.


3 Benefits of Cold Showers

But in the past, cold showers were all people had.


3 Benefits of Cold Showers

And they had to bathe wherever they would find water.


3 Benefits of Cold Showers

And most of the time, if you have a slum in a lake or a pond or whatever, it's not warm often. It's cold.


3 Benefits of Cold Showers

And so the fact that we have hot showers is something to be incredibly grateful for.


3 Benefits of Cold Showers

What's number three?


3 Benefits of Cold Showers

So number two was it improves immunity and circulation through more blood flow around the organs and increased white cell count.


3 Benefits of Cold Showers

So number three is keeps the skin and the hair in good condition.


3 Benefits of Cold Showers

Now, how does it do this?


3 Benefits of Cold Showers

So hot water usually is dries out the skin really quickly.


3 Benefits of Cold Showers

It washes away all of your skin's natural oils because it's so hot.


3 Benefits of Cold Showers

It expose your pores expand and the hot water because it's the molecules in the hot water are moving faster.


3 Benefits of Cold Showers

They wash off all your natural oils in your skin.


3 Benefits of Cold Showers

And cold water temporarily tightens pores.


3 Benefits of Cold Showers

So all the dirt that's on your skin and whatnot on your face, et cetera, it temporarily tightens those pores.


3 Benefits of Cold Showers

So it can't seep into it and grow into something that you don't want.


3 Benefits of Cold Showers

And so tighter pores prevents the skin from getting clogged.


3 Benefits of Cold Showers

And cold water, as I said, helps to maintain the natural oils of the skin.


3 Benefits of Cold Showers

And so there are my three main points.


3 Benefits of Cold Showers

There's hundreds more on the internet, but they sort of appeal to me because I've had skin problems in the past, fat problems in the past, and immune system and circulation problems in the past.


3 Benefits of Cold Showers

So if cold showers can help to stimulate brown fat and burn some calories and it can improve my blood flow and it can keep my skin and hair in good condition, well, I can do 30 seconds a day of cold showers.


3 Benefits of Cold Showers

But what I'm up to for us today is a wedding in a few hours.


3 Benefits of Cold Showers

I've got to go down. There's a breakfast. I'm going to upload this podcast and then, well, I'm going to upload the podcast.


3 Benefits of Cold Showers

I'll upload the YouTube video in a few days, but then go down to breakfast, go to a wedding.


3 Benefits of Cold Showers

Don't know what to expect from a wedding.


3 Benefits of Cold Showers

I've got to get dressed up in a suit, but I think it should be a good day.


3 Benefits of Cold Showers

It's going to be good fun. I'm going to be dancing.


3 Benefits of Cold Showers

And yeah, thought I'd do the podcast nice and early.


3 Benefits of Cold Showers

It is 8.44 a.m. So this is probably one of the earliest podcasts I've ever done.


3 Benefits of Cold Showers

So whatever you're up to, I hope you're having an awesome day.


3 Benefits of Cold Showers

I love you all.


3 Benefits of Cold Showers

Remember today's challenge. Have a cold shower.


3 Benefits of Cold Showers

You can have a warm shower, 30 seconds. Try it for one day.


3 Benefits of Cold Showers

If you want to find out a bit more about cold showers, I'd say look up Wim Hof, W-I-M space, H-O-F.


3 Benefits of Cold Showers

Awesome, dude. You'll love all this stuff.


3 Benefits of Cold Showers


[Own work] VALSE 💃: Benchmark for Vision and Language Models Centered on Linguistic Phenomena

Hello and welcome to this unusual AI Coffee Break! Today we'll share with you some of our own work,


which was accepted at ACL 2022. I know what you could think, it might not seem like much,


especially when we compare our work with papers that we have previously covered on the channel,


coming from big companies and many authors, but you know, we are proud of our paper,


it is honest work. We'll be presenting this at ACL 2022 in the last week of May,


so in case you will be attending the ACL conference too, I guess we'll see you in


Dublin. Don't hesitate to say hi if you're around, otherwise you can have a look at what


we have been working on recently in this video. Enjoy! Hello, we are happy to present our work


on VAUS, a task-independent benchmark meant for testing vision and language models with


specific linguistic phenomena. Why did we do the trouble in setting up this benchmark?


Well, during the last years we have seen an explosion of transformer-based vision and


language models, pre-trained on large amounts of image text pairs. Each one of these models


pushes state-of-the-art on vision and language tasks, such as visual question answering,


visual commonsense reasoning, image retrieval, phrase grounding. So we got a message, the


accuracies on these task-specific benchmarks are increasing steadily, but do we know what


the models have actually learned? What is it that a vision and language transformer


understood when assigning a high score for this image and this sentence to match,


and a low score for this one? Do vision and language models focus on the right thing,


or do they focus on biases as shown by previous work? To shed more light on this aspect,


we propose a more task-agnostic direction and introduce VAUS that tests the sensitivity of


vision and language models to specific linguistic phenomena that affect both the linguistic and the


visual modalities. We target existence, plurality, counting, spatial relations, actions and entity


coreference. But how do we test whether the vision and language models have captured these phenomena?


By FOILing, a method previously applied for vision and language models only for noun phrases


by Ravi Shekhar and collaborators, and on counting by us in previous work. FOILing basically means


that we take the caption of an image and produce a FOIL by altering the caption such that it does


not describe the image anymore. And we do these phrase alterations by focusing on six specific


pieces, such as existence, plurality, counting, spatial relations, actions and entity coreference,


where each piece can consist of one or more instruments, in case we found more than one


interesting way to create FOIL instances. For example, in the case of the actions piece,


we have two instruments, one in which the action verb is changed with a different action,


and one in which actants are swapped. Counting and coreference also are pieces that have more


than one instrument. And we create these FOILs by making sure that they fail to describe the image,


that they are grammatical and otherwise valid sentences. This is not easy to do,


because a FOILed caption may be less likely than the original caption. For example,


though it's not impossible, it is statistically less likely for plants to cut a man than a man to


cut plants, and large vision and language models could pick up on this. Therefore, to obtain valid


FOILs, we must take action. First, we make use of strong language models to propose FOILs.


Second, we use natural language inference, or short NLI, to filter out FOILs that could be


still describing the image, since when constructing FOILs, we need to ensure that they fail to


describe the image. To test this automatically, we apply natural language inference with the


following rationale. We consider an image to be the premise, and its caption its entailed hypothesis.


In addition, we consider the caption to be the premise, and the FOIL is its hypothesis.


If an NLI model predicts the FOIL to contradict or to be neutral with respect to the caption,


we take this as an indicator of a valid FOIL. If an NLI predicts the FOIL to be entailed by


the caption, it cannot be a good FOIL, since by transitivity, it will give a truthful description


of the image, and we filter these FOILs out. But this procedure is not perfect. It is just


an indicator for valid FOILs, therefore as a third measure for generating valid FOILs,


we employ human annotators to validate the data used in VALS.


So, after filtering and human evaluation, we have as many test instances as described in this table.


Note that VALS does not deliver any training data, but only test data,


since it is a zero-shot testing benchmark only. It is designed to leverage the existing capabilities


of vision and language models after pre-training. Fine tuning would only enable models to exploit


artifacts or statistical biases in the data. And we all know that these models like to cheat


and take shortcuts. And as we said, we are interested in assessing what capabilities


the vision and language models have after pre-training. We experiment with five vision


and language models on VALS, namely with CLIP, AlexMert, Vilbert, Vilbert12in1 and VisualBert.


Two of our most important evaluation metrics are the accuracy of the models in classifying


image-sentence pairs into captions and FOILs. Perhaps more relevant for this video, we will


showcase our more permissive metric, the pairwise accuracy, which measures whether the image-sentence


alignment score is greater for the correct image-text pair than for its FOILed pair.


For more metrics and results on them, do check out our paper. The results with pairwise accuracy


are shown here and they are consistent with the results we got from the other metrics.


It's that the best zero-shot performance is achieved by Vilbert12in1, followed by Vilbert,


AlexMert, CLIP and finally VisualBert. It's notable how instruments centered on the individual


objects like existence and noun phrases are almost solved by Vilbert12in1, highlighting that models


are capable of identifying named objects and their presence in images. However, none of the remaining


pieces can be reliably solved in our adversarial FOILing settings. We see from the plurality and


counting instruments that vision and language models have trouble distinguishing references


to single versus multiple objects or counting them in an image. The relation piece shows that


they have difficulties in correctly classifying a named spatial relation between objects in an


image. They also have trouble distinguishing actions and identifying their participants,


even if supported by plausibility biases as we see in the actions piece. From the


coreference piece we find out that tracing multiple references to the same object in an image by using


pronouns is also difficult for vision and language models. As a sanity check and because it's an


interesting experiment, we also benchmark two text-only models, GPT1 and GPT2, to assess whether


VALS is solvable by these unimodal models by computing the perplexity of the correct and the


FOILed caption, no image here, and predicting the entry with the lowest perplexity. If the perplexity


is higher for the FOIL, we take this as an indication that the FOILed caption may suffer


from plausibility bias or other linguistic biases. And it's interesting to see that in some cases


the text-only GPT models have captured the plausibility of the world better than the vision


and language models. So to sum up, VALS is a benchmark that uses the lens of linguistic


constructs to help the community improve vision and language models by hard testing their visual


grounding capabilities. Our experiments show that vision and language models identify named objects


in their presence in images well, as shown by the existence piece, but struggle to ground their


interdependence and relationships in visual scenes when forced to respect linguistic indicators.


We would really like to encourage the community to use VALS for measuring progress towards language


grounding with vision and language models. And even more, VALS could be used as an indirect


assessment of datasets, as models could be evaluated before and after training or fine


tuning to see whether a dataset helps models improve on any of the aspects tested by VALS.


If you're interested, do check out the VALS data on GitHub and if you have any questions,


Functional Friday | Raw Training Australia

Check it out!


It's a sick setup!


My Experience at CodeCamp, Intro to Keras and Failing Hard | Learning Intelligence 17

What's going on guys? Welcome back to Learning Intelligence episode 17. I'm speaking quietly


this morning because it's nice and early. I've got up, I'm teaching some kids how to


code this morning. We're gonna, I'm going to a, it's called code camp and it's like


a three-day camp and the kids come each day and we learn how to build games and we learn


some fundamental principles of coding. So I'm really excited for that. I won't be able


to film anything there of course because they're young kids but when I get back this afternoon


I'll let you know how it went and I've got a story about yesterday. I failed really hard


but I'll share that with you when I'm back. From my videos you can probably gather that


I'm naturally high energy. Like I'm excited to do a lot of things but the kids at the


coding camp they were unreal. I've never met a group of kids so keen to do something. They


were just, every time we went on a break they were like, oh we don't want to go on a break,


we don't want to, except when they were hungry they were really keen to go on a break then.


After that they were so keen to get back into it and even at the end of the day we had kids


that we sort of had to almost drag away from the computers to stop them from building their


games and we're like, you can continue building this at home and their eyes were like, can


we? Can we keep building it? They were so excited. So if you didn't get that at all


I just spent the day at a code camp. So I was instructing all, I was a teacher's aide


actually, there was a head instructor and she's doing an amazing job running the fort.


And essentially we're teaching these kids some basics of coding and it's drag and drop


block coding for now but tomorrow we're going into line coding so we're going to be actually


writing JavaScript and we've got a, Anna and I were teaching a group of about nine kids


and they're about seven, eight, nine, I think ten is the oldest in our group, maybe eleven


I'm not entirely sure, so around year four to six and the projects for them is building


games, small little games. So the stuff these kids have come up with is incredible. They


get a blank canvas when they come in and okay, we give them some guidelines over what type


of game they have and what should be the logic behind it but as for everything else they


can implement it, they can work on their own rules and whatnot and then the best thing


about it is at the end of the camp they get to export it and it's all touchscreen compatible


so they can play it on their iPads or play it on their phones and whatnot. A funny thing


actually, they were all working on laptops or most of them were working on laptops but


the kids, it's incredible to see how they interact with the computer. Like I had one


girl, she doesn't call computers computers, she calls them iPads and she was trying to


touch the computer monitor. It sort of seemed natural the way she was interacting with it


but of course the monitor wasn't touchscreen. I don't know, I think that's the future of


computing, like all computers, I know Apple, they haven't built a touchscreen Mac yet but


I think in the future they're going to combine these two devices in some way or another.


I don't know, Apple are, there are touchscreen laptops but you know how Apple are, they're


usually late to the party with something ridiculous but that's, I digress. So teaching kids is


amazing, it's quite exhausting, I get back, so this is a hat tip to all the teachers out


there. You guys do an incredible job and I'm very grateful, I have a newfound respect for


my teachers and I definitely, I know I caused a ruckus in all of my classes so apologies


to you, thank you for your patience for putting up with me. Outside of that, I've got a couple


of days left of Code Camp so I'm really excited to finish that up. I'm having so much fun,


really excited actually to see what the kids final games look like and have a play of them.


It's good to be back as well, I had an amazing holiday, I'm well rested, I'm ready for 2018.


I cannot actually, I cannot tell you how excited I am for this year. First plan, first call


of duty, is that what it's called maybe? Call of duty. Well first thing I'm going to knock


off for this year is the deeplearning.ai course. I know I wanted to finish that before Christmas


but you know how these things happen, they get in the way. I'm still well and truly ahead


of the actual course schedule. I believe if I did it at the normal course pace it would


take about four months. I'm going to complete it within about one month so I'm really excited


for that and we're on convolutional neural networks in course four of it I believe, I'm


about halfway through that and it is, it's swaying me. I thought I was most interested


in natural language processing in terms of the field of AI but computer vision also seems


very interesting so we'll see how that goes. I might sort of see which one I prefer but


I haven't really tried either of them in depth but getting a good taste of computer vision


through convolutional neural networks and I don't know, computers need to see right?


It'd be very handled. Second thing that's going to be happening after I finish the deeplearning.ai


specialisation and actually starting very soon is term two of the artificial intelligence


standard degree. I believe by the time this video lands I will be well and truly into


that. I'll start that, I'll probably be a couple of days into that and so that's, I'm


really excited for that. So that's going to be hardcore deep learning, working on building


projects in computer vision, natural language processing and speech recognition systems.


So of course I'm going to bring you all along on the journey but that's the two major plans


for the start of 2018. Otherwise I'm going to keep working through my artificial intelligence


master's degree, the link will be in the description as always. And one more thing before we get


into the rest of the video, MIT just announced a artificial general intelligence course and


a few of the details, it's going to be some lectures on virtually the state of the union,


what's known in terms of artificial intelligence as well as how it's going to affect different


fields and it's free, it's open to everyone. So I'm really excited for that, that starts


January 22nd I believe, if not the link will be in the description as well, I'll put something


maybe on screen or something like that, I don't know. I'm really excited for that.


Hey you probably gathered, I'm dressed for bed, I've just had a shower, my hair's probably


all wet, my face is probably all red from the hot water. These things happen, I'm going


to jump into bed, I need some rest for the kids tomorrow and then yeah we'll be back


into coding soon and learning about AI. Let's do it. Have any of you seen this meme? I love


it, I love this scene in Inception 2 by the way, or maybe not this scene, I can't really


remember this scene but this meme is everywhere, we need to go deeper and this is actually


part of the Coursera deep learning specialisation, see Andrew Ng and this meme was the inspiration


for Inception Networks. So essentially Inception Network is just layers upon layers upon layers


upon layers upon layers of convolutional neural network layers and the whole model itself


was inspired by that meme. I think that's really cool and just because that meme is


amazing and the network, a full paper was published on it and it was by some researchers


at Google and they named it GoogleNet and they spelt it a funny way, I'll let you look


up the paper, to pay tribute to LeNet which is, I think it's Yann LeCun's Net which was


the original ImageNet way back in the day if I'm correct. But if you want to check out


that paper, that's what it's by, I'm not going to try to pronounce this name but it's 2014


Going Deeper With Convolutions and I'll leave a link to the description in that. Second


of all, I forgot yesterday that I'm all, in a previous clip of this video, let me just


put you down here, I forgot to mention what I completely failed at. So I applied for an


internship at Landing.ai which is Andrew Ong's new AI company to help manufacturing companies


or manufacturing businesses with incorporate artificial intelligence into their workflow.


And so I applied for an internship there which is based over in the US and I had to do a


coding challenge for it and I failed miserably at the coding challenge and I felt pretty


down about it after I first did it but after reflecting back, I think it was a blessing


because, well, blessing, whatever you want to call it really, because it taught me what


I need to work on, it showed me where my weaknesses are and that's what I think is a valuable


lesson is to, if you fail at something or if you try something that's out of your reach,


rather than being down by it, which I was and I think it's inevitable to be down by


a failure or to feel down even just a little bit but if you look at it a different way,


you can flip it and go, okay, I need to work on these two things. And so I took screenshots


and whatnot of the coding challenge so I can come back to it and try it in the future if


I need to. And I wrote down the two specific topics that I should work on in Python. And


so first of all, it's algorithms or one of them, algorithms is everywhere so that's important


to work on. And number two is data structures. And number three, I wrote down as a follow


on from that is coding from scratch. And the challenge, the internship challenge was on


HackerRank. And HackerRank provides some great opportunities to practice those things, algorithms,


data structures and coding from scratch. Amongst whatever else I'm learning, I think I might


need to update my AI Masters curriculum to incorporate some HackerRank challenges. I


know I've been doing a lot of Python on there, but I think I should start to branch out into,


rather than just doing Python based challenges into applied challenges. So the algorithms


and data structures and whatnot. Onto the next thing. I'm currently building a model


for a programming assignment of the Coursera convolutional neural networks course. And


this clip is going to be a bit long, but that's all right. So it's in Keras. And essentially


what Keras is, is if you imagine Python as being the lowest level programming foundation


or programming language that you can use or framework, and then TensorFlow is just one


step above Python. And then Keras is even one step above TensorFlow. And what the purpose


of Keras is, is so deep learning engineers like you and me can build a deep learning


model quickly, implemented, try it out, get some feedback on it. And then if we need to


go deeper, if we need to follow this meme, if we need to go deeper, we can use the basis


of the model that we've built in Keras and improve upon it in TensorFlow or something


custom coded in Python. And so just check this out. That's, you can build a model in


Keras. Look how quickly you can def model. And then you go define your inputs, define


your padding, define your convolutional layers, convolutional 2d, define batch normalization


or add batch normalization to your previous layer, apply activation layer, apply a max


pooling layer, flatten it, and then define your model as the whole thing and then return


model. And that is your entire model built in Keras. How cool is that? I'm going to finish


off this week's programming assignments and then I'll check back in with you guys. Oh


yeah. Check it out. A hundred out of a hundred. You know what that means. We just passed the


programming assignment for residual networks. Let me show you what we did. A residual network


is essentially a combination of different blocks and each of these blocks are called


a residual block. And then we go up here and I'll show you what it's a combination of.


This one was a convolutional neural network or, or ResNet and we use convolutional blocks.


So if you can see in there, I don't know if you can, but these are individual convolutional


neural networks. Let me see if I can zoom in. There we go. So we've got a conv, a 2d


convolutional neural network there, some batch normalization, some Relo activation. And what


we do here is we pass the inputs all the way up to here and add it back into this main


chain here. And what that does is it allows you to create really, really deep networks.


So for example, the one we just built in this assignment was 50 layers deep. What that passing


of what that, that shortcut is what it's called here is passing the inputs over to here. And


what that does is it helps prevent your gradient from diminishing. And if that doesn't make


sense, I'm not going to go too in depth to it, but I'll put a link in the description


of where you can check out something, something about what diminishing gradient and what exploding


grading is trying to put those two words together, exploding gradient means. And essentially


it's a problem of losing, losing valuable parameters as your neural network gets deeper


and deeper. And so what this sort of step does is help prevent that and helps make your


models better. Essentially, I want to show you another thing is Kerris and did the tutorial


here and what we built was a neural network that detects whether someone is happy or not.


And so this is a very pixelated picture of me and you can see here it outputs one for


happy and zero for not happy. And that's, that says I'm happy there. And then there's


my friend, not such a happy face. How can we make you smile? And there's another one


of him smiling there. What a happy chap. And it didn't really work for these two. So they


both got unhappy, which shows that there's some improvement. There's an improvement that


can be done in that Kerris network there. But I really want to emphasize how amazing


Kerris is. And I was just reading their website. I'll put a link in the description here. Kerris


is used by Netflix, Uber, Yelp, Instacart, Zudoc, Square and many others. And look at


this archive mentions in 2017. It's very popular. So TensorFlow is probably the number one deep


learning library and Kerris is a close, well actually not very close second, but definitely


well above these other networks, other libraries here. I'm finding it incredibly simple. Like


if you want to use Kerris to start building a deep learning model to get something that


you want off the ground, I would, I would 100% start here. If you're familiar with anything


to do with Python, Kerris is where you should start. That's it for this, this week of coding.


And next week I'll show you what I'm going to get onto. It's still on computer vision.


We're going to wrap up course four of Coursera next week of the deep learning.a specialization,


which is all about computer vision. And this week three is on object detection. Week four


is on facial recognition. So I'm very excited for both of those. Object detection, if you


haven't heard before, it's, it's used in say for example, you're designing a self driving


car, a self driving car needs very good object detection because if you imagine when you're


driving, you've got a lot of objects you have to avoid. You have other cars in the road,


you have cyclists, you have pedestrians, cats, dogs, everything. So object detection is something


that's going to explode in the coming years. And facial recognition, well, if you're watching


this on an iPhone X, I don't need to, or iPhone 10, it's really hard to say 10 when they put


an X there. Anyway, if you're using an iPhone 10, you know what facial recognition is, or


if you've ever used a camera, all cameras these days pretty much have facial detection.


So they can make sure they focus on on your face and not something else. That's going


to wrap up this week's episode of Learning Intelligence 17. I got that one right this


time. I really hope I did. It's time for some shout outs of the week. Alrighty then, it's


time for some shout outs of the week. All these people either reached out to me via


email, Twitter, social media, or whatever commented on YouTube. And guys, thank you


so much for reaching out. I really appreciate it. I hope I answered all of your questions


that you have. And I really appreciate the kind words that you give me, the feedback.


It really makes me excited to keep making these videos. So thank you so much to all


these guys. And as well, if you're watching, thank you so much to you. And don't forget,


if you want to reach out to me anytime, my email is Daniel at MrDberg.com. But nonetheless,


in no particular order, we have Barney, Alex, Rogue, Jao, Aaron, Sarab, Gregory, Lewis,


and Dimitrios. And I apologize if I pronounce any of those names wrong. But thank you so


guys. Thank you so guys. Thank you so guys. Thank you so guys much for thank you guys


so much for reaching out for. Wow, I can't even talk anymore. But you know what I'm trying


to say. Next week's video, you heard about it. Otherwise, I'm going to go for a walk


now. I think it's about to storm where I'm at. So I'm going to be excited for that. I


can read a book in the rain. Who else loves reading a book in the rain? That's it for


this week's episode. Thank you so much for watching and we'll catch you next week. And


Building my first Amazon Alexa Skill! | Learning Intelligence 28

We are on to a brand new section of the AI Nanodegree, voice user interfaces.


Before I show you what I've been getting into, I haven't heard back yet from the internship that