Would you like to learn the application of deep neural networks with Keras and Python? I've placed
my complete course from Washington University on YouTube, GitHub, and even a book! It's all free.
Deep neural networks are fantastic because they can accept input and produce output in just
about any format. They can take in tabular data, images, text, and even audio. They then process
this information, perform mathematical magic, and output numbers, images, words, and even audio.
In my class, Applications of Deep Neural Networks, I'll show you
how to apply these neural networks to everyday problems. I teach everything in
this course with Jupyter Notebooks and Python. Jupyter allows you to look at
your code intermixed with information from my book on Keras deep learning.
When you run the code, you see the actual results from your programs as you run them.
All of the code and text for this course is contained in this book.
Applications of Deep Neural Networks with Keras. The PDF of this book is always free;
however, I greatly appreciate your support if you purchase the paperback or Kindle.
I ensure that everything is compatible with Google CoLab so that you can run it with a
free GPU if needed. Believe me; the GPU can save hours and hours on your training times.
The GAN is one type of neural network that we'll look at in this course. GAN stands for Generative
Adversarial Neural Network. I typically use GANs to generate faces; I can train them to create any
"fake data." A GAN works by having two neural networks. The first neural network is the
generator. It essentially takes random numbers and generates faces. The second neural network
is the discriminator. It essentially tells if the generator is producing correct faces or not. These
two go back and forth and wage an adversarial war. (crash) Once the algorithm completes, you have a
generator that can take lots of random numbers and keeps generating pretty realistic-looking faces.
We also look at reinforcement learning, which we can implement with deep learning. Deep
reinforcement learning is a powerful technique that allowed Google AlphaGoZero to master the game
of Go in only 8 hours, gaining the ability to beat top computer and human players.
In this course, we'll take a look at Atari video games, using the AI gym and create reinforcement
programs that learn to play these games by looking at the actual screen of
what's going on in the games and of the real video game.
Reinforcement learning is fundamentally like training your pet. You provide rewards to the
neural network for performing a good job or doing something that leads to a favorable outcome.
Hickory, lay down. Good boy Hickory!
Neural networks are also very
good at generating subliminal messages for advertising... okay, well, sorry, I had to try.
Transformers are revolutionizing the world of Natural Language processing.
No, not those transformers. Neural network transformers can accept variable-length
text and usually also output variable-length text. They learn from examples of language.
No more trying to codify complex and inconsistent grammar rules.
We will use the Hugging Face transformers to perform a variety of NLP tasks.
YOLO, "you only LOOK once." This algorithm is an amazing technology that you can see running all
around me. It's classifying multiple objects and doing it very efficiently because it only needs
to look once. We'll see how you can transfer this technology into your Python program and receive
the actual coordinates and classifications of what is happening around you. YOLO can be fooled;
in this case, it can't tell the difference between my dog and a stuffed animal.
It also has trouble telling billboards and things that have people on it and thinking
they're people. This billboard might be very concerning to a
a self-driving car, who thinks it's about to get broadsided.
But wait... there's more. This video was only able to show you the highlights from
this course. Here's a complete list of some of the other topics that we'll cover.
Thanks for watching this video; if you have any questions about this course,
leave something in the comments. All the information is available in the Playlist and on
GitHub. Please subscribe to this channel to update you whenever I add new videos to this course.