TensorFlow for Deep Learning – Bharath Ramsundar & Reza Bosagh Zadeh

This book will introduce you to the fundamentals of machine learning through TensorFlow. TensorFlow is Google’s new software library for deep learning that makes it straightforward for engineers to design and deploy sophisticated deep learning architectures. You will learn how to use TensorFlow to build systems capable of detecting objects in images, understanding human text, and predicting the properties of potential medicines. Furthermore, you will gain an intuitive understanding of TensorFlow’s potential as a system for performing tensor calculus and will learn how to use TensorFlow for tasks outside the traditional purview of machine learning.

Importantly, TensorFlow for Deep Learning is one of the first deep learning books written for practitioners. It teaches fundamental concepts through practical examples and builds understanding of machine learning foundations from the ground up. The target audience for this book is practicing developers, who are comfortable with designing software systems, but not necessarily with creating learning systems. At times we use some basic linear algebra and calculus, but we will review all necessary fundamentals. We also anticipate that our book will prove useful for scientists and other professionals who are comfortable with scripting, but not necessarily with designing learning algorithms.

Related posts:

Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Amazon Machine Learning Developer Guild Version Latest
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Deep Learning with Theano - Christopher Bourez
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Deep Learning with Hadoop - Dipayan Dev
Machine Learning with Python for everyone - Mark E.Fenner
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
An introduction to neural networks - Kevin Gurney & University of Sheffield
Python 3 for Absolute Beginners - Tim Hall & J.P Stacey
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Python Machine Learning - Sebastian Raschka
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Deep Learning with Python - Francois Cholletf
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Java Deep Learning Essentials - Yusuke Sugomori
Introduction to Deep Learning - Eugene Charniak
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Introduction to the Math of Neural Networks - Jeff Heaton
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Intelligent Projects Using Python - Santanu Pattanayak
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Deep Learning and Neural Networks - Jeff Heaton
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach