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

Let's see all of that in action and build a GAN from scratch.

After importing our standard Pytorch modules, we will define the generator and the discriminator. Both of them are simple multi-layer networks.


## Generator
- Receives as input 100-length noise vector $z$ sampled from a distribution
- Outputs a flattened image in the dimensions of the cifar10 dataset: $32\times32\times3 = 3072$

## Discriminator 
- The discriminator receives an input of size = 3072, which is again the flattened cifar10 image or the output of the generator. 
- It outputs a scalar in [0,1].
- The binary cross-entropy loss is computed based on the output of the discriminator and the data label.

Note that when we train the discriminator, the generated images have the label of 0, and the real images have the label of 1.

Let's see all of that in action and build a GAN from scratch.

After importing our standard Pytorch modules, we will define the generator and the discriminator. Both of them are simple multi-layer networks.


# Generator
- Receives as input 100-length noise vector $z$ sampled from a distribution
- Outputs a flattened image in the dimensions of the cifar10 dataset: $32\times32\times3 = 3072$

# Discriminator 
- The discriminator receives an input of size = 3072, which is again the flattened cifar10 image or the output of the generator. 
- It outputs a scalar in [0,1].
- The binary cross-entropy loss is computed based on the output of the discriminator and the data label.

Note that when we train the discriminator, the generated images have the label of 0, and the real images have the label of 1.

Implement and train GAN in Python and Pytorch.

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

Develop a GAN with Pytorch

Generator