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 Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Python Machine Learning Cookbook - Practical solutions from preprocessing to Deep Learning - Chris A...
Deep Learning with Python - Francois Cholletf
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Fundamentals of Deep Learning - Nikhil Bubuma
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Deep Learning with Keras - Antonio Gulli & Sujit Pal
Pattern recognition and machine learning - Christopher M.Bishop
Machine Learning Mastery with Python - Understand your data, create accurate models and work project...
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Data Science and Big Data Analytics - EMC Education Services
Coding Theory - Algorithms, Architectures and Application
The hundred-page Machine Learning Book - Andriy Burkov
Deep Learning with PyTorch - Vishnu Subramanian
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
An introduction to neural networks - Kevin Gurney & University of Sheffield
Introduction to the Math of Neural Networks - Jeff Heaton
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Python Machine Learning Eqution Reference - Sebastian Raschka
Deep Learning and Neural Networks - Jeff Heaton
Python 3 for Absolute Beginners - Tim Hall & J.P Stacey
Deep Learning for Natural Language Processing - Jason Brownlee
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda