Deep Learning with TensorFlow 2 and Keras

This book PDF is perfect for those who love Computers genre, written by Antonio Gulli and published by Packt Publishing Ltd which was released on 27 December 2019 with total hardcover pages 647. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Deep Learning with TensorFlow 2 and Keras books below.

Deep Learning with TensorFlow 2 and Keras
Author : Antonio Gulli
File Size : 51,7 Mb
Publisher : Packt Publishing Ltd
Language : English
Release Date : 27 December 2019
ISBN : 9781838827724
Pages : 647 pages
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Deep Learning with TensorFlow 2 and Keras by Antonio Gulli Book PDF Summary

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 extensive code samplesBook Description Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML. What you will learnBuild machine learning and deep learning systems with TensorFlow 2 and the Keras APIUse Regression analysis, the most popular approach to machine learningUnderstand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiersUse GANs (generative adversarial networks) to create new data that fits with existing patternsDiscover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret anotherApply deep learning to natural human language and interpret natural language texts to produce an appropriate responseTrain your models on the cloud and put TF to work in real environmentsExplore how Google tools can automate simple ML workflows without the need for complex modelingWho this book is for This book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow 2, and AutoML to build machine learning systems. Some knowledge of machine learning is expected.

Deep Learning with TensorFlow 2 and Keras

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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Deep Learning with TensorFlow 2 and Keras   Second Edition

Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key Features Introduces and then uses TensorFlow 2 and Keras right from the start Teaches key machine and deep learning techniques Understand the fundamentals of deep learning and machine learning through

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Advanced Deep Learning with TensorFlow 2 and Keras   Second Edition

Download or read online Advanced Deep Learning with TensorFlow 2 and Keras Second Edition written by ROWEL. ATIENZA, published by Unknown which was released on 2020-02-28. Get Advanced Deep Learning with TensorFlow 2 and Keras Second Edition Books now! Available in PDF, ePub and Kindle.

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Deep Learning with TensorFlow and Keras

Build cutting edge machine and deep learning systems for the lab, production, and mobile devices Key FeaturesUnderstand the fundamentals of deep learning and machine learning through clear explanations and extensive code samplesImplement graph neural networks, transformers using Hugging Face and TensorFlow Hub, and joint and contrastive learningLearn cutting-edge machine and

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Advanced Deep Learning with TensorFlow 2 and Keras

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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Deep Learning Projects Using TensorFlow 2

Work through engaging and practical deep learning projects using TensorFlow 2.0. Using a hands-on approach, the projects in this book will lead new programmers through the basics into developing practical deep learning applications. Deep learning is quickly integrating itself into the technology landscape. Its applications range from applicable data science to

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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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Deep Learning with Keras

Get to grips with the basics of Keras to implement fast and efficient deep-learning models About This Book Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games See how various deep-learning models and practical use-cases can be implemented using Keras A practical, hands-on guide

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