book

Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

Name: Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition
Author: Rowel Atienza
ISBN: 9781838821654

by Rowel Atienza

February 2020

Intermediate to advanced

512 pages

11h 47m

English

Packt Publishing

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Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedGet in touchReviews
Introducing Advanced Deep Learning with Keras
1. Why is Keras the perfect deep learning library?Installing Keras and TensorFlow2. MLP, CNN, and RNNThe differences between MLP, CNN, and RNN3. Multilayer Perceptron (MLP)The MNIST datasetThe MNIST digit classifier modelBuilding a model using MLP and KerasRegularizationOutput activation and loss functionOptimizationPerformance evaluationModel summary4. Convolutional Neural Network (CNN)ConvolutionPooling operationsPerformance evaluation and model summary5. Recurrent Neural Network (RNN)6. Conclusion7. References
Deep Neural Networks
1. Functional APICreating a two-input and one-output model2. Deep Residual Network (ResNet)3. ResNet v24. Densely Connected Convolutional Network (DenseNet)Building a 100-layer DenseNet-BC for CIFAR105. Conclusion6. References
Autoencoders
1. Principles of autoencoders2. Building an autoencoder using Keras3. Denoising autoencoders (DAEs)4. Automatic colorization autoencoder5. Conclusion6. References
Generative Adversarial Networks (GANs)
1. An Overview of GANsPrinciples of GANs2. Implementing DCGAN in Keras3. Conditional GAN4. Conclusion5. References
Improved GANs
1. Wasserstein GANDistance functionsDistance function in GANsUse of Wasserstein lossWGAN implementation using Keras2. Least-squares GAN (LSGAN)3. Auxiliary Classifier GAN (ACGAN)4. Conclusion5. References
Disentangled Representation GANs
1. Disentangled representationsInfoGANImplementation of InfoGAN in KerasGenerator outputs of InfoGAN2. StackedGANImplementation of StackedGAN in KerasGenerator outputs of StackedGAN4. Conclusion5. References
Cross-Domain GANs
1. Principles of CycleGANThe CycleGAN modelImplementing CycleGAN using KerasGenerator outputs of CycleGANCycleGAN on MNIST and SVHN datasets2. Conclusion3. References
Variational Autoencoders (VAEs)
1. Principles of VAEVariational inferenceCore equationOptimizationReparameterization trickDecoder testingVAE in KerasUsing CNN for AE2. Conditional VAE (CVAE)3. 𝛽-VAE – VAE with disentangled latent representations4. Conclusion5. References
Deep Reinforcement Learning
1. Principles of Reinforcement Learning (RL)2. The Q value3. Q-learning exampleQ-Learning in Python4. Nondeterministic environment5. Temporal-difference learningQ-learning on OpenAI Gym6. Deep Q-Network (DQN)DQN on KerasDouble Q-learning (DDQN)7. Conclusion8. References

Policy Gradient Methods
1. Policy gradient theorem2. Monte Carlo policy gradient (REINFORCE) method3. REINFORCE with baseline method4. Actor-Critic method5. Advantage Actor-Critic (A2C) method6. Policy Gradient methods using Keras7. Performance evaluation of policy gradient methods8. Conclusion9. References
Object Detection
1. Object detection2. Anchor boxes3. Ground truth anchor boxes4. Loss functions5. SSD model architecture6. SSD model architecture in Keras7. SSD objects in Keras8. SSD model in Keras9. Data generator model in Keras10. Example dataset11. SSD model training12. Non-Maximum Suppression (NMS) algorithm13. SSD model validation14. Conclusion15. References
Semantic Segmentation
1. Segmentation2. Semantic segmentation network3. Semantic segmentation network in Keras4. Example dataset5. Semantic segmentation validation6. Conclusion7. References
Unsupervised Learning Using Mutual Information
1. Mutual Information2. Mutual Information and Entropy3. Unsupervised learning by maximizing the Mutual Information of discrete random variables4. Encoder network for unsupervised clustering5. Unsupervised clustering implementation in Keras6. Validation using MNIST7. Unsupervised learning by maximizing the Mutual Information of continuous random variables8. Estimating the Mutual Information of a bivariate Gaussian9. Unsupervised clustering using continuous random variables in Keras10. Conclusion11. References
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Index

Content preview from Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

Preface

In recent years, Deep Learning has made unprecedented success stories in difficult problems in vision, speech, natural language processing and understanding, and all other areas with abundance of data. The interest in this field from companies, universities, governments, and research organizations has accelerated the advances in the field. This book covers select important topics in Deep Learning with three new chapters, Object Detection, Semantic Segmentation, and Unsupervised Learning using Mutual Information. The advanced theories are explained by giving a background of the principles, digging into the intuition behind the concepts, implementing the equations and algorithms using Keras, and examining the results.

Artificial Intelligence ...

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Deep Learning with TensorFlow 2 and Keras - Second Edition

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Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

by Rowel Atienza

Preface

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.