Hey everyone and welcome to pie torch.
Deep learning and artificial intelligence.
Now this is one of the most exciting courses I've ever made.
I've been making these deep learning courses for a long time before pi talks was even invented.
So it's been quite a journey for me to see how this field has evolved in the past few years.
And guys it's been moving quick.
The best practices and guidelines you'll learn in this course are the latest and greatest.
This stuff did not even exist when some of my previous deep learning courses came out.
Things like the best way to train your neural network to architecture decisions.
Thanks to the hard work of deep learning researchers all around the world.
I've been able to take what they've learned and bring that information to you.
This course is designed to be a beginner to advanced course so you don't need a lot of math and background
knowledge but of course if you're into that sort of thing I certainly have resources for you.
We're going to start by looking at basic machine learning in the form of a neuron.
The fundamental building block of neural networks from there we are going to jump right into neural
networks.
The thing that started it all.
Luckily with PI talk you don't need a lot of heavy theory unless you wanted the PI talk API already
implements a set of composed all building blocks so you can focus on building cool things rather than
debugging and mathematical equations.
Next we're going to look at convolution on their own networks which are specialized neural networks
for computer vision.
Then we'll look at recurrent neural networks which are specialized neural networks for sequence data
such as Time series text speech and DNA.
We'll even apply our own ends to stock prediction.
This is one of my favorite exercises from this course because it teaches you what 90 percent of other
people are doing wrong.
If you've ever used an LSD GM for stock prediction before or taking a class that promised to teach you
you'll definitely want to watch this because pi talk has a modern and easy to use interface.
It makes building extremely complex things much easier than they used to be.
Here is some example applications first we have Ganz which stands for generative adversarial networks.
These are a new training paradigm which allow you to use deep learning to generate beautiful photo realistic
images.
What's absolutely crazy about this is that the images you are looking at now are people that do not
actually exist but it doesn't stop there.
Deep Learning has been applied in the field of reinforcement learning which excels at tasks that take
multiple steps to complete.
Like playing a video game.
Deep reinforcement learning agents have beat world champions in the strategy game go.
And in modern video games such as CSO and Dota 2 These are things we wouldn't have dreamed of just 10
years ago.
Of course if that's not enough we still have more deep learning powers.
State of the art natural language processing applications such as speech recognition and machine translation.
So whenever you talk to your phone and it figures out that you want to say order a pizza or send in
a line tomorrow for 6 a.m. That's speech recognition using deep learning and action neural machine translation
has significantly enhanced the capabilities of language translation in the recent years with students
in 200 countries around the world.
I've made use of this great technology more than once.
And guys I could go on there's self-driving vehicles.
Object detection facial recognition deep fakes synthesizing speech so you can have a phone conversation
with a robot.
So if you want to learn how to use the world's fastest rising Deep Learning library.
This course is for you.
Thanks for listening and I'll see you in the next lecture.