In this lecture I'm going to give you an outline for the rest of this course.
First I want to give you a brief summary of what pi torture is all about.
In case you've had previous experience with tensor flow or other deep learning libraries such as the
a..
So in the beginning there was V.A. V.A. was a significant improvement over what was being done previously
for two major reasons.
Number one for reasons you'll learn about later writing neuron that works from scratch involves doing
a lot of Matrix calculus by hand and then copying those equations into code for those of you who took
my first deep learning course.
You know how difficult this can be.
The Vienna library was the first to innovate in this area using automatic differentiation or auto diff
for sure what that means is you don't have to write down Calculus Equations since the computer will
do that for you.
And number two there's a lot of math that has to happen in a neuron that work.
And this takes a lot of time meaning you'd have to wait hours or even days or weeks to train your neural
network the V.A. library was the first to innovate in this area by making use of GP use which were originally
designed to improve the performance of P.C. games.
One of the downsides of older libraries such as the piano is that you have to build everything by yourself.
This can really slow you down especially if it's your first time writing certain components.
You not only have to write each component on your own you'll also have to worry about them being right.
If any single component you wrote is wrong then it could make your entire program fail.
On top of that in the previous years it was announced that the V.A. library would no longer be maintained.
What about tensor flow tensor flow is a pretty popular library thanks to the fact that it's backed by
Google who at this point kind of run the Internet tensor flow used to be really messy.
In fact probably more so than V.A. but now in version 2.0 it uses the caris API which is the total opposite.
It's very high level and very simple but there is a downside to very high level and very simple API
eyes.
They make it very easy to do common things but hard to do uncommon things
pi talks on the other hand has been slowly gaining adoption in the field of deep learning thanks to
the fact that it's relatively easy to do common things and still easy to do uncommon things.
For this reason it's been extremely popular in the research community who by definition do lots of uncommon
things.
That's their job after all.
Pi torch is developed mainly by another Internet giant Facebook specifically the Facebook API research
lab.
As with all deep learning libraries pi talk is supported by GPA acceleration and has automatic differentiation
so there's no need in this course to use tools like calculus and linear algebra to derive back propagation.
If you hate math then you should feel very lucky.
In fact we won't discuss back propagation at all despite the fact that it's really the main ingredient
that makes everything work.
This combined with the fact that a lot of the basic building blocks are already built for you are some
of the major advantages which are going to allow us to blast through each section very quickly.
In the past it was necessary to focus on each architecture one at a time with Oren being the most complex
to implement these days and ends CNN and aren't ends can be implemented in just a few lines of code.
All right so how is this code structured.
First before we even begin discussing deep learning or any type of statistical modeling we are going
to look at a new coding environment which I really like called Google collab.
Google collab is basically Japan notebook hosted by Google but with a lot more bells and whistles.
Personally I have never been a fan of Japan a notebook since it seemed to have more disadvantages than
advantages.
However Google collab is a different beast.
It's hosted by Google so you don't have to use your own computing resources.
It's free so it doesn't matter if you have a slow computer or a fast computer.
Everyone has the same access it gives you free access to the GP you A.P. you for orders of magnitude
faster training and inference and finally most of what we need is already installed so you don't have
to waste any time installing libraries yourself.
Once you've done that we are going to go through the fundamental architectures involved in deep learning.
Believe it or not this all starts with linear regression the line of best fit you learned about in high
school physics.
We'll see that with just one little change adding the logistic function on top of a linear model.
We will get a neuron this covers the two major types of supervised learning classification and regression.
Once you know the basics it's time to get started with deep learning.
The first deep learning architecture you'll learn about is the artificial neural network also known
these days as the deep neural network.
Although neither of these names really do a good job of differentiating it with other architectures.
Next we'll dive into image processing with convolution on their own that works.
You'll learn about convolution and how this works to create neural networks that are specially designed
to achieve better performance on images and other physical signals next you'll learn about recurrent
neural networks which specialize at working with sequence data.
Unlike my previous courses where we used RNA is mostly for natural language processing we are going
to start with time series forecasting.
This is going to let us cover a lot of ground that many other courses simply skip over entirely
in particular.
We're going to cover the difference between the wrong way to do a time series forecast and the right
way we're going to look at several time series that simple models like linear regression can and can't
solve as well as several time series that slightly more complex models like the simple are in can and
can't solve for the more difficult problems we'll see how the Elysium achieves superior performance
and so you'll see first hand why Alice teams are useful rather than me just telling you and you accepting
it as fact without observing it for yourself.
The next example is one of my favorites in the chorus which is on stock prediction with LSD MS and Arne
ends.
I think most of you will find the contents of these lectures very surprising.
If you've ever learned about stock prediction with Ellis teams in the past be warned most other instructors
are doing this the wrong way.
In this course I'm going to teach you why it's wrong how to correct it and what some of the real obstacles
are in stock prediction.
The first major part of this course is about the fundamental architectures and deep learning while the
second major part of this course is focused on applications.
As a side note if you want to just skip the rest of this lecture and move on with the rest of the course
that's fine too.
The first application we will look at is natural language processing.
Specifically with text documents you'll see how we can use deep learning for text classification which
is the type of task you would use for spam detection sentiment analysis and so on.
We'll also look at embedding which are deep learnings method of dealing with categorical data
embedding will lead us to our next application on recommender systems.
Now it might seem weird to think that natural language processing would be somehow related to recommender
systems but hopefully in this course you will learn about the hidden connection between these two different
fields recommender systems is all about how to optimize the products or items that you recommend to
your users.
Facebook Amazon and Google have been using recommender systems to improve their profits by the billions.
When you go to Amazon.com it is full of recommended products for you.
Facebook news feed and your Instagram feed are both recommendation lists.
Google's search results advertisements and the YouTube sidebar are all examples of how Google uses recommendations
ensure recommender systems are one of the most practical business applications of deep learning and
one that can be used in a pretty straightforward manner to improve your profits.
This is in contrast to say natural language where it's not exactly clear how that would directly tie
in to how much money your business makes.
Although it's already a fundamental part of our day to day lives.
So don't discount it just yet.
The next application we'll discuss was a game changer for deep learning.
If you've taken any of my deep learning courses in the past you know that training a deep neural network
takes time lots of time.
You might end up waiting hours or even days or possibly even weeks.
Luckily machine learning engineers have found new ways of allowing us to build on top of the work of
others.
The idea is companies like Google or Facebook or university research groups will train large neural
networks on humongous datasets such as image net which contains over a million images.
That's not the kind of thing you can feasibly do on your home computer using transfer learning what
we can do is take just a part of their neural network and combine it with our own neural network designed
for a specific task that we want to do.
Results have shown that this is an easy.
And more importantly fast method of building state of the art deep learning models.
In just a few seconds you can integrate the power of bleeding edge known that works into your own applications.
Next we have Ganz or generative adversarial networks.
This was named as the most interesting invention in machine learning in the past 10 years.
By Yann Le -- a deep learning pioneer who is now the chief A.I. scientists at Facebook.
Ganz are all about generating images using a system of two neural networks one that generates the images
and one whose job it is to discriminate between a real and fake images.
Using this pair of neural networks they both help each other improve so the discriminator becomes better
and better at recognizing fakes.
While the generator becomes better and better at creating realistic images that cannot be differentiated
from fakes when people think about deep learning they don't often think about how neuron that West can
be used to generate things but rather just how neural networks can make predictions on things.
Predictions are nice but generating things opens up a whole new world.
Recently Google announced a conversational A.I. where you could have a personal assistant do something
like call a restaurant and make a reservation for you.
But that personal assistant is not a person.
It is a robot.
All of its speech is generated by a computer
another exciting application of deep learning is in a reinforcement learning usually deep learning is
used to create models with a static input and output.
For example you put in an image and it tells you what this image is of like a car or a truck or you
put in an email and you're known that work tells you if it's spam or not spam.
But some tasks require multiple steps which requires having some long term strategy and keeping track
of state.
For example if you are playing a game like Super Mario you can't just look at a still image of the game
and decide what to do.
Instead you need to have a long term strategy.
You have to know that you should walk forward to reach the flag for that level.
You have to know that if an enemy is walking towards you you have to avoid it or attack it.
And also you have to have proper timing in doing so.
This is what reinforcement learning is all about.
To summarize the outline of the Course is like this.
We can split it into two parts Part 1 architectures and part 2 applications.
In part one we discuss the fundamental architectures such as linear models and ends.
CNN is an aunt it's in part two.
We discuss applications such as an AP recommender systems Ganz transfer learning reinforcement learning
and possibly more.
Thanks for listening and I'll see you in the next lecture.