Gain insights into basic and intermediate deep learning concepts, including CNNs, RNNs, GANs, and transformers. Delve into fundamental architectures to enhance your machine learning model training skills.

docker.tar.gz

Pytorch

GANS

This course is an accumulation of well-grounded knowledge and experience in deep learning. It provides you with the basic concepts you need in order to start working with and training various machine learning models. 

You will cover both basic and intermediate concepts including but not limited to: convolutional neural networks, recurrent neural networks, generative adversarial networks as well as transformers.

After completing this course, you will have a comprehensive understanding of the fundamental architectural components of deep learning. Whether you’re a data and computer scientist, computer and big data engineer, solution architect, or software engineer, you will benefit from this course.

Introduction to Deep Learning & Neural Networks

Another way to explain GANs is through the probabilistic formulation we used on variational autoencoders.  

GANs follow a different approach in finding the probability distribution of the data $p_{data}(x)$. 

>Instead of computing or the approximate $p_{data}(x)$, we only care about the ability to sample data from the distribution.

But what does that actually mean?

If we assume that our data $x_i$ follow a probability distribution $p_{data}(x)$, we will want to build a model that allows us to draw samples from $p_{data}(x)$. 

As we did with VAE, we again introduce a latent variable $z$ with a prior distribution $p(z)$.  $p(z)$ is usually a simple random distribution such as the uniform or a Gaussian (normal distribution). 

We then sample $z$ from $p(z)$  and pass the sample to the generator network $G(z)$, which will output a sample of data $x$ with $x=G(z)$.

$x$ can be thought of as a sample from a third distribution, the generator's distribution $p_G$. The generator will be trained to convert random $z$ into fake data $x$ or, in other words, to force  $p_G$ to be as close as possible to $p_{data}(x)$.

This is where the discriminator network D comes into play. The discriminator is simply a classifier that produces a single probability, wherein 0 corresponds to a fake generated $x$ and 1 to the real sample from our distribution. 

These two networks are trained using this minimax game. Let's take a closer look.


# Training

> One key insight is the **indirect training**: this basically means that the generator is not trained to minimize the distance to a specific image, but just to fool the discriminator!

The loss that occurs in this training is called **adversarial loss.**

The adversarial loss enables the model to learn in an **_unsupervised_** manner. 

> When we train D, real images are labeled as 1 and fake generated images as 0. On the other hand, the ground truth label, when training the generator, is 1 for fake images (like a real image), even though the examples are fake. 

This happens because our objective is just to fool D. The image below illustrates this process:  

Understand the basic concepts behind GANs and their training process.

Learn Deep Learning

Neural Networks

Training Neural Networks

Convolutional Neural Networks

Recurrent Neural Networks

Autoencoders

Generative Adversarial Networks

Attention and Transformers

Graph Neural Networks

Conclusion

Final Quiz

Generative Adversarial Networks in Detail