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Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more

Book Specification

Binding Paperback
Language English
Number Of Pages 368
Author Rowel Atienza
Publisher Packt Publishing Limited
Isbn-10 1788629418
Isbn-13 9781788629416
Dimension 19.05*2.11*23.5

Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more

Rowel Atienza's Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more

A comprehensive guide to advanced deep learning techniques, including Autoencoders, GANs, VAEs, and Deep Reinforcement Learning, that drive today's most impressive AI results Key Features Explore the most advanced deep learning techniques that drive modern AI results Implement Deep Neural Networks, Autoencoders, GANs, VAEs, and Deep Reinforcement Learning A wide study of GANs, including Improved GANs, Cross-Domain GANs and Disentangled Representation GANs Book DescriptionRecent developments in deep learning, including GANs, Variational Autoencoders, and Deep Reinforcement Learning, are creating impressive AI results in our News headlines - such as AlphaGo Zero beating World Chess champions, and generative AI that can create art Paintings that sell for over $400k because they are so human-like. Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques. The journey begins with an overview of MLPs, CNNs, and RNNs, which are the Building blocks for the more advanced techniques in the book. You'll learn how to implement deep learning Models with Keras and Tensorflow, and move forwards to advanced techniques, as you explore deep neural network architectures, including ResNet and DenseNet, and how to create Autoencoders. You then learn all about Generative Adversarial Networks (GANs), and how they can open new levels of AI performance. Variational AutoEncoders (VAEs) are implemented, and you'll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of advanced techniques, you'll learn how to implement Deep Reinforcement Learning (DRL) such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI. What you will learn Cutting-edge techniques in human-like AI performance Implement advanced deep learning Models using Keras The Building blocks for advanced techniques - MLPs, CNNs, and RNNs Deep neural networks - ResNet and DenseNet Autoencoders and Variational AutoEncoders (VAEs) Generative Adversarial Networks (GANs) and creative AI techniques Disentangled Representation GANs, and Cross-Domain GANs Deep Reinforcement Learning (DRL) methods and implementation Produce industry-standard applications using OpenAI gym Deep Q-Learning and Policy Gradient Methods Who this book is forSome fluency with Python is assumed. As an advanced book, you'll be familiar with some machine learning approaches, and some practical experience with DL will be helpful. Knowledge of Keras or TensorFlow is not required but would be helpful.
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Amazon, Paperback Rs. 934.0

Why you should read Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more by Rowel Atienza

This book has been written by Rowel Atienza, who has written books like Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more. The books are written in Computer Science category. This book is read by people who are interested in reading books in category : Computer Science. So, if you want to explore books similar to This book, you must read and buy this book.

How long would it take for you to read Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more

Depending on your reading style, this is how much time you would take to complete reading this book.

Reading Style Time To Finish The Book
Slow 73 hours
Average 36 hours
Good 24 hours
Excellent 12 hours
So if you are a Reader belonging in the Good category, and you read it daily for 1 hour, it will take you 24 days.
Note: A slow reader usually reads 100 words per minute, an average reader 200 words per minute, an average reader 300 words per minute and an excellent leader reads about 600-1000 words per minute, however the comprehension may vary.

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