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:

Artificial Intelligence by example - Denis Rothman
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Python Data Structures and Algorithms - Benjamin Baka
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Python Deep Learning Cookbook - Indra den Bakker
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Medical Image Segmentation Using Artificial Neural Networks
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Neural Networks and Deep Learning - Charu C.Aggarwal
Python Machine Learning Cookbook - Practical solutions from preprocessing to Deep Learning - Chris A...
Introduction to Scientific Programming with Python - Joakim Sundnes
Pattern recognition and machine learning - Christopher M.Bishop
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Deep Learning for Natural Language Processing - Jason Brownlee
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
The hundred-page Machine Learning Book - Andriy Burkov
Deep Learning with Keras - Antonio Gulli & Sujit Pal
Deep Learning in Python - LazyProgrammer
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...
Java Deep Learning Essentials - Yusuke Sugomori
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Python Machine Learning Second Edition - Sebastian Raschka & Vahid Mirjalili
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Deep Learning with Applications Using Python - Navin Kumar Manaswi