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Deep Learning with TensorFlow and Keras - Third Edition

Build and deploy supervised, unsupervised, deep, and reinforcement learning models

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  • 698 pages
  • 25 hours of reading

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Learn to build advanced machine and deep learning systems for various environments with this comprehensive guide. It covers neural networks and deep learning techniques using TensorFlow and Keras, focusing on writing applications within the powerful and scalable machine learning framework. The latest version, TensorFlow 2.x, emphasizes simplicity and user-friendliness, featuring updates like eager execution, intuitive APIs based on Keras, and flexible model building across platforms. The content provides an overview of supervised and unsupervised machine learning models, along with an in-depth analysis of deep learning and reinforcement learning through practical examples applicable to cloud, mobile, and large-scale production settings. The book details the creation of neural networks with TensorFlow and explores popular algorithms, including regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs). Additionally, it includes working example applications and discusses TensorFlow in production, mobile, and AutoML contexts. Aimed at Python developers and data scientists, this hands-on resource equips readers with both the theoretical knowledge and practical skills necessary to develop machine learning systems using Keras, TensorFlow, and AutoML, with some prior machine learning k

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Deep Learning with TensorFlow and Keras - Third Edition, François Chollet, Amita Kapoor, Sujit Pal, Antonio Gullì

Language
Released
2022
Binding
(Paperback)
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Title
Deep Learning with TensorFlow and Keras - Third Edition
Subtitle
Build and deploy supervised, unsupervised, deep, and reinforcement learning models
Language
English
Released
2022
Format
Paperback
Pages
698
ISBN10
1803232919
ISBN13
9781803232911
Series
Tags
Description
Learn to build advanced machine and deep learning systems for various environments with this comprehensive guide. It covers neural networks and deep learning techniques using TensorFlow and Keras, focusing on writing applications within the powerful and scalable machine learning framework. The latest version, TensorFlow 2.x, emphasizes simplicity and user-friendliness, featuring updates like eager execution, intuitive APIs based on Keras, and flexible model building across platforms. The content provides an overview of supervised and unsupervised machine learning models, along with an in-depth analysis of deep learning and reinforcement learning through practical examples applicable to cloud, mobile, and large-scale production settings. The book details the creation of neural networks with TensorFlow and explores popular algorithms, including regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs). Additionally, it includes working example applications and discusses TensorFlow in production, mobile, and AutoML contexts. Aimed at Python developers and data scientists, this hands-on resource equips readers with both the theoretical knowledge and practical skills necessary to develop machine learning systems using Keras, TensorFlow, and AutoML, with some prior machine learning k