Book Image

Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Rowel Atienza

Book Image

Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

By: Rowel Atienza

Overview of this book

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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Introducing Advanced Deep Learning with Keras

Introducing Advanced Deep Learning with Keras

1. Why is Keras the perfect deep learning library?

2. MLP, CNN, and RNN

3. Multilayer Perceptron (MLP)

4. Convolutional Neural Network (CNN)

5. Recurrent Neural Network (RNN)

Free Chapter

Deep Neural Networks

Deep Neural Networks

1. Functional API

2. Deep Residual Network (ResNet)

4. Densely Connected Convolutional Network (DenseNet)

Autoencoders

1. Principles of autoencoders

2. Building an autoencoder using Keras

3. Denoising autoencoders (DAEs)

4. Automatic colorization autoencoder

Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs)

1. An Overview of GANs

2. Implementing DCGAN in Keras

3. Conditional GAN

Improved GANs

1. Wasserstein GAN

2. Least-squares GAN (LSGAN)

3. Auxiliary Classifier GAN (ACGAN)

Disentangled Representation GANs

Disentangled Representation GANs

1. Disentangled representations

Cross-Domain GANs

Cross-Domain GANs

1. Principles of CycleGAN

Variational Autoencoders (VAEs)

Variational Autoencoders (VAEs)

1. Principles of VAE

2. Conditional VAE (CVAE)

3. 𝛽-VAE – VAE with disentangled latent representations

Deep Reinforcement Learning

Deep Reinforcement Learning

1. Principles of Reinforcement Learning (RL)

3. Q-learning example

4. Nondeterministic environment

5. Temporal-difference learning

6. Deep Q-Network (DQN)

Policy Gradient Methods

Policy Gradient Methods

1. Policy gradient theorem

2. Monte Carlo policy gradient (REINFORCE) method

3. REINFORCE with baseline method

4. Actor-Critic method

5. Advantage Actor-Critic (A2C) method

6. Policy Gradient methods using Keras

7. Performance evaluation of policy gradient methods

Object Detection

Object Detection

1. Object detection

2. Anchor boxes

3. Ground truth anchor boxes

4. Loss functions

5. SSD model architecture

6. SSD model architecture in Keras

7. SSD objects in Keras

8. SSD model in Keras

9. Data generator model in Keras

10. Example dataset

11. SSD model training

12. Non-Maximum Suppression (NMS) algorithm

13. SSD model validation

Semantic Segmentation

Semantic Segmentation

1. Segmentation

2. Semantic segmentation network

3. Semantic segmentation network in Keras

4. Example dataset

5. Semantic segmentation validation

Unsupervised Learning Using Mutual Information

Unsupervised Learning Using Mutual Information

1. Mutual Information

2. Mutual Information and Entropy

3. Unsupervised learning by maximizing the Mutual Information of discrete random variables

4. Encoder network for unsupervised clustering

5. Unsupervised clustering implementation in Keras

6. Validation using MNIST

7. Unsupervised learning by maximizing the Mutual Information of continuous random variables

8. Estimating the Mutual Information of a bivariate Gaussian

9. Unsupervised clustering using continuous random variables in Keras

Other Books You May Enjoy

Other Books You May Enjoy

Index

Customer Reviews

5 star

0

4 star

0

3 star

0

2 star

0

1 star

0

2. Implementing DCGAN in Keras

Figure 4.2.1 shows DCGAN that is used to generate fake MNIST images:

Figure 4.2.1: A DCGAN model

DCGAN implements the following design principles:

Use strides > 1, and a convolution instead of MaxPooling2D or UpSampling2D. With strides > 1, the CNN learns how to resize the feature maps.
Avoid using Dense layers. Use CNN in all layers. The Dense layer is utilized only as the first layer of the generator to accept the z-vector. The output of the Dense layer is resized and becomes the input of the succeeding CNN layers.
Use Batch Normalization (BN) to stabilize learning by normalizing the input to each layer to have zero mean and unit variance. There is no BN in the generator output layer and discriminator input layer. In the implementation example to be presented here, no batch normalization is used in the discriminator.
Rectified Linear Unit (ReLU) is used in all layers of the generator except...