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:

Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Deep Learning with Hadoop - Dipayan Dev
Amazon Machine Learning Developer Guild Version Latest
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Coding Theory - Algorithms, Architectures and Application
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Intelligent Projects Using Python - Santanu Pattanayak
Medical Image Segmentation Using Artificial Neural Networks
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Python Machine Learning Eqution Reference - Sebastian Raschka
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Deep Learning with PyTorch - Vishnu Subramanian
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Neural Networks and Deep Learning - Charu C.Aggarwal
Deep Learning and Neural Networks - Jeff Heaton
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Deep Learning with Python - Francois Cholletf
Deep Learning with Python - Francois Chollet
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Neural Networks - A visual introduction for beginners - Michael Taylor
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Java Deep Learning Essentials - Yusuke Sugomori
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Deep Learning with Theano - Christopher Bourez
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda