Advanced Deep Learning with Keras

This book PDF is perfect for those who love Computers genre, written by Rowel Atienza and published by Packt Publishing Ltd which was released on 31 October 2018 with total hardcover pages 368. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Advanced Deep Learning with Keras books below.

Advanced Deep Learning with Keras
Author : Rowel Atienza
File Size : 54,5 Mb
Publisher : Packt Publishing Ltd
Language : English
Release Date : 31 October 2018
ISBN : 9781788624534
Pages : 368 pages
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Advanced Deep Learning with Keras by Rowel Atienza Book PDF Summary

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 FeaturesExplore the most advanced deep learning techniques that drive modern AI resultsImplement Deep Neural Networks, Autoencoders, GANs, VAEs, and Deep Reinforcement LearningA wide study of GANs, including Improved GANs, Cross-Domain GANs and Disentangled Representation GANsBook Description Recent 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 learnCutting-edge techniques in human-like AI performanceImplement advanced deep learning models using KerasThe building blocks for advanced techniques - MLPs, CNNs, and RNNsDeep neural networks – ResNet and DenseNetAutoencoders and Variational AutoEncoders (VAEs)Generative Adversarial Networks (GANs) and creative AI techniquesDisentangled Representation GANs, and Cross-Domain GANsDeep Reinforcement Learning (DRL) methods and implementationProduce industry-standard applications using OpenAI gymDeep Q-Learning and Policy Gradient MethodsWho this book is for Some 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.

Advanced Deep Learning with Keras

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 FeaturesExplore the most advanced deep learning techniques that drive modern AI resultsImplement Deep Neural Networks, Autoencoders, GANs, VAEs, and Deep Reinforcement LearningA wide study of GANs,

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Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras Key FeaturesExplore the most advanced deep learning techniques that drive modern AI resultsNew coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentationCompletely updated for TensorFlow 2.xBook Description Advanced Deep

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Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key FeaturesIntroduces and then uses TensorFlow 2 and Keras right from the startTeaches key machine and deep learning techniquesUnderstand the fundamentals of deep learning and machine learning through clear explanations and

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Build, scale, and deploy deep neural network models using the star libraries in Python Key Features Delve into advanced machine learning and deep learning use cases using Tensorflow and Keras Build, deploy, and scale end-to-end deep neural network models in a production environment Learn to deploy TensorFlow on mobile, and

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Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks Key Features Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision Combine the power of Python, Keras, and TensorFlow to build deep learning models for object

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