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|a 1803232919
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|a 9781803245713
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|a 1803245719
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|a Q325.5
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|a Kapoor, Amita
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|a Deep learning with TensorFlow and Keras
|c Amita Kapoor, Antonio Gulli, Sujit Pal, François Chollet
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| 250 |
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|a Third edition
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| 260 |
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|a Birmingham, UK
|b Packt Publishing Ltd.
|c 2022
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| 300 |
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|a 698 pages
|b illustrations
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| 505 |
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|a Includes bibliographical references and index
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| 505 |
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|a Table of Contents Neural Networks Foundations with TF Regression and Classification Convolutional Neural Networks Word Embeddings Recurrent Neural Network Transformers Unsupervised Learning Autoencoders Generative Models Self-Supervised Learning Reinforcement Learning Probabilistic TensorFlow An Introduction to AutoML The Math Behind Deep Learning Tensor Processing Unit Other Useful Deep Learning Libraries Graph Neural Networks Machine Learning Best Practices TensorFlow 2 Ecosystem Advanced Convolutional Neural Networks
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| 653 |
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|a Réseaux neuronaux (Informatique)
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| 653 |
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|a Neural networks (Computer science) / fast
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| 653 |
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|a Machine learning / http://id.loc.gov/authorities/subjects/sh85079324
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| 653 |
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|a Python (Computer program language) / fast
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|a Python (Computer program language) / http://id.loc.gov/authorities/subjects/sh96008834
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| 653 |
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|a Artificial intelligence / fast
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| 653 |
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|a Artificial intelligence / http://id.loc.gov/authorities/subjects/sh85008180
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| 653 |
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|a Intelligence artificielle
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| 653 |
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|a Neural networks (Computer science) / http://id.loc.gov/authorities/subjects/sh90001937
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|a Machine learning / fast
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|a Apprentissage automatique
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|a artificial intelligence / aat
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|a Python (Langage de programmation)
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| 653 |
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|a TensorFlow / http://id.loc.gov/authorities/names/n2019020612
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| 700 |
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|a Gulli, Antonio
|e author
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| 700 |
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|a Pal, Sujit
|e author
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| 700 |
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|a Chollet, François
|e writer of foreword
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|a eng
|2 ISO 639-2
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|b OREILLY
|a O'Reilly
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|a Expert insight
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|z 1803245719
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|z 9781803232911
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|z 9781803245713
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|u https://learning.oreilly.com/library/view/~/9781803232911/?ar
|x Verlag
|3 Volltext
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|a 331
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|a 005.13/3
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|a Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using 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 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments. This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML.
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