Advanced Deep Learning with TensorFlow 2 and Keras (2nd Edition)

Introduction

Compiled source code from the second edition of Advanced Deep Learning with TensorFlow 2 and Keras book.

IMO, this is a really good introductory book that give simple and superficial explanation for a widely used machine learning models (feels kinda like For Dummies series reference books, but skipping most of the steps).

You will need a supplementary free resources available on YouTube, Medium, or from the research paper itself to understand more on the content.

TLDR; a good book for a programmer majoring in machine learing, challenging book for a newcomer.

Setup

1. Install Anaconda or Miniconda and create a new environment conda create --name <your new environment name>.
2. Activate the new environment conda activate <your new environment name>.
3. Install the required packages conda install matplotlib pydot graphviz tensorflow-gpu.
4. Add conda's Graphviz to the system PATH.
5. cd to the 01_testTensorFlowInstallation.py directory and run sanity program to test if the installation is properly configured or not.

Disclaimer

The original source code repository can be located here. But I have modified some of the code structure to suit my own convenience (i.e. code styling, eliminating spaghetti code, comments, optimizing for my low end hardware, etc).

After I have completed this book, this repo will be changed to a public archive (no more future update).

Chapters

1 - Introduction

1. Test TensorFlow installation
2. MNIST sampler
3. MLP on MNIST
4. CNN on MNIST
5. RNN on MNIST

2 - Deep Networks

6. Functional API on MNIST
7. Y-Network on MNIST
8. Shallow Y-Network on MNIST
9. ResNet v1 and v2 on CIFAR10
10. DenseNet on CIFAR10

3 - Autoencoders

11. Autoencoder on MNIST
12. Autoencoders 2-dim latent vector data
13. Autoencoder and classification on MNIST
14. Autoencoders for denoising image
15. Autoencoder colorization on monochrome CIFAR10

4 - Generative Adversarial Network

16. DCGAN on MNIST
17. CGAN on MNIST

5 - Improved GAN

18. WGAN on MNIST
19. LSGAN on MNIST
20. ACGAN on MNIST

6 - GAN with Disentangled Latent Representations

21. INFOGAN on MNIST