• Title Machine Learning with TensorFlow, 2nd Edition
• Author(s): Mattmann A. Chris
• Publisher: Manning; 2nd edition (February 2, 2021)
• Permission: Free to read entire book online by the publisher (Manning), limited time every day.
• Paperback: 456 pages
• eBook: HTML
• Language: English
• ISBN-10: 1617297712
• ISBN-13: 978-1617297717
• Share This:  

Book Description

TensorFlow, Google's library for large-scale machine learning, simplifies often-complex computations by representing them as graphs and efficiently mapping parts of the graphs to machines in a cluster or to the processors of a single machine.

This book gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms. Then, you'll move on to the money chapters: exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning.

Digest this book and you will be ready to use TensorFlow for machine-learning and deep-learning applications of your own.

Updated with new code, new projects, and new chapters, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library.

• Matching your tasks to the right machine-learning and deep-learning approaches
• Visualizing algorithms with TensorBoard
• Understanding and using neural networks

• Chris Mattmann is the Division Manager of the Artificial Intelligence, Analytics, and Innovation Organization at NASA Jet Propulsion Lab. The first edition of this book was written by Nishant Shukla with Kenneth Fricklas.

• Amazon

• Machine Learning
• Neural Networks and Deep Learning
• Python Programming
• Artificial Intelligence
• Data Science

• Machine Learning with TensorFlow, 2nd Edition (Nishant Shukla)
• The Book Homepage

• Mathematical Introduction to Deep Learning (Arnulf Jentzen, et al)

  This book aims to provide an introduction to the topic of deep learning algorithms, coverss essential components of deep learning algorithms in full mathematical detail including different Artificial Neural Network (ANN) architectures and algorithms.

• The Shallow and the Deep: Introduction to Neural Networks

  This book is a collection of lecture notes that offers an accessible introduction to Neural Networks and machine learning in general. The focus lies on classical machine learning techniques, with a bias towards classification and regression.

• Gradient Expectations: Structure of Predictive Neural Networks

  An insightful investigation into the mechanisms underlying the predictive functions of neural networks - and their ability to chart a new path for AI. Delve into the known neural architecture of the mammalian brain to illuminate the structure of predictive networks.

• Deep Learning (Ian Goodfellow, et al)

  Written by three experts, this is the only comprehensive book on the subject. It offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning.

• Neural Networks and Deep Learning (Michael Nielsen)

  Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning.

• Deep Neural Networks and Data for Automated Driving

  This open access book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving and artificial intelligence.

• Deep Learning on Graphs (Yao Ma, et al)

  The book is a self-contained, comprehensive text on foundations and techniques of Graph Neural Networks with applications in NLP, data mining, vision and healthcare. Accessible to who want to use graph neural networks to advance their disciplines.

• Physics-Based Deep Learning (Nils Thuerey, et al.)

  This book contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations. All topics come with hands-on code examples in the form of Jupyter notebooks to quickly get started.

• Hyperparameter Tuning for Deep Learning: A Practical Guide

  This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods.

• Deep Learning with Python, 2nd Edition (Francois Chollet)

  This book introduces the field of deep learning using Python and the powerful Keras library. It offers insights for both novice and experienced machine learning practitioners, and builds your understanding through intuitive explanations and practical examples.

• Dive into Deep Learning (Aston Zhang, et al.)

  This is an open source, interactive book provided in a unique form factor that integrates text, mathematics and code, now supports the TensorFlow, PyTorch, and Apache MXNet programming frameworks, drafted entirely through Jupyter notebooks.

• Deep Learning for Coders with Fastai and PyTorch

  This book show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.

• Deep Learning with PyTorch (Eli Stevens, et al.)

  This book teaches you to create deep learning and neural network systems with PyTorch. It gets you to work right away building a tumor image classifier from scratch. You'll learn best practices for the entire deep learning pipeline, tackling advanced projects.