Advanced Deep Learning with TensorFlow 2 and Keras Apply DL GANs VAEs deep RL unsupervised learning, object detection and segmentation, and more 2nd Edition by Rowel Atienza 1800568274 9781800568273

Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more 2nd Edition by Rowel Atienza – Ebook PDF Instant Download/Delivery: 1800568274, 978-1800568273

 

 

Product details:

ISBN 10: 1800568274
ISBN 13: 978-1800568273
Author: Rowel Atienza

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.

Table of contents:

1. Introducing Advanced Deep Learning with Keras
2. Deep Neural Networks
3. Autoencoders
4. Generative Adversarial Networks (GANs)
5. Improved GANs
6. Disentangled Representation GANs
7. Cross-Domain GANs
8. Variational Autoencoders (VAEs)
9. Deep Reinforcement Learning
10. Policy Gradient Methods
11. Object Detection
12. Semantic Segmentation
13. Unsupervised Learning Using Mutual Information

People also search:

advanced deep learning with keras by rowel atienza

advanced deep learning with keras

tensorflow advanced

advanced tensorflow

advanced deep learning with python