[Tensorflow2, Keras] Advanced Deep Learning with Keras

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

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This is the code repository for Advanced Deep Learning with TensorFlow 2 and Keras, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

Please note that the code examples have been updated to support TensorFlow 2.0 Keras API only.

 

 

About the Book

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Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects.

Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques.

Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance.

Next, you’ll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.

 

 

Chapter 1 - Introduction

1. MLP on MNIST
2. CNN on MNIST
3. RNN on MNIST

Chapter 2 - Deep Networks

1. Functional API on MNIST
2. Y-Network on MNIST
3. ResNet v1 and v2 on CIFAR10
4. DenseNet on CIFAR10

Chapter 3 - AutoEncoders

1. Denoising AutoEncoders
2. Colorization AutoEncoder

Chapter 4 - Generative Adversarial Network (GAN)

1. Deep Convolutional GAN (DCGAN)
2. Conditional (GAN)

Chapter 5 - Improved GAN

1. Wasserstein GAN (WGAN)
2. Least Squares GAN (LSGAN)
3. Auxiliary Classifier GAN (ACGAN)

Chapter 6 - GAN with Disentangled Latent Representations

1. Information Maximizing GAN (InfoGAN)
2. Stacked GAN

Chapter 7 - Cross-Domain GAN

1. CycleGAN

Chapter 8 - Variational Autoencoders (VAE)

1. VAE MLP MNIST
2. VAE CNN MNIST
3. Conditional VAE and Beta VAE

Chapter 9 - Deep Reinforcement Learning

1. Q-Learning
2. Q-Learning on Frozen Lake Environment
3. DQN and DDQN on Cartpole Environment

Chapter 10 - Policy Gradient Methods

1. REINFORCE, REINFORCE with Baseline, Actor-Critic, A2C

Chapter 11 - Object Detection

1. Single-Shot Detection

Chapter 12 - Semantic Segmentation

1. FCN
2. PSPNet

Chapter 13 - Unsupervised Learning using Mutual Information

1. Invariant Information Clustering
2. MINE: Mutual Information Estimation

 

 

ref.

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras

GitHub - PacktPublishing/Advanced-Deep-Learning-with-Keras: Advanced Deep Learning with Keras, published by Packt