Below a list of free resources to learn TensorFlow:
- TensorFlow website: www.tensorflow.org
- Udacity free course: www.udacity.com
- Google Cloud Platform: cloud.google.com
- Coursera free course: www.coursera.org
- Machine Learning with TensorFlow by Nishant Shukla : www.tensorflowbook.com
- ‘First Contact With TensorFlow’ by Prof. JORDI TORRES: jorditorres.org or you can order from Amazon: First Contact With Tensorflow
- Kadenze Academy: www.kadenze.com
- OpenShift: blog.openshift.com
- Tutorial by pkmital : github.com
- Tutorial by HyunsuLee : github.com
- Tutorial by orcaman : github.com
- Stanford CS224d: Lecture 7
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If you want to look for more information, check some free online courses available at coursera.org, edx.org or udemy.com.
Recommended reading list:
  | Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details |
  | TensorFlow Machine Learning Cookbook This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production. What you will learn Become familiar with the basics of the TensorFlow machine learning library Get to know Linear Regression techniques with TensorFlow Learn SVMs with hands-on recipes Implement neural networks and improve predictions Apply NLP and sentiment analysis to your data Master CNN and RNN through practical recipes Take TensorFlow into production |
  | Learning TensorFlow: A Guide to Building Deep Learning Systems TensorFlow is currently the leading open-source software for deep learning, used by a rapidly growing number of practitioners working on computer vision, Natural Language Processing (NLP), speech recognition, and general predictive analytics. This book is an end-to-end guide to TensorFlow designed for data scientists, engineers, students and researchers. With this book you will learn how to: Get up and running with TensorFlow, rapidly and painlessly Build and train popular deep learning models for computer vision and NLP Apply your advanced understanding of the TensorFlow framework to build and adapt models for your specific needs Train models at scale, and deploy TensorFlow in a production setting |
  | TensorFlow for Machine Intelligence: A Hands-On Introduction to Learning Algorithms TensorFlow, a popular library for machine learning, embraces the innovation and community-engagement of open source, but has the support, guidance, and stability of a large corporation. Because of its multitude of strengths, TensorFlow is appropriate for individuals and businesses ranging from startups to companies as large as, well, Google. TensorFlow is currently being used for natural language processing, artificial intelligence, computer vision, and predictive analytics. TensorFlow, open sourced to the public by Google in November 2015, was made to be flexible, efficient, extensible, and portable. Computers of any shape and size can run it, from smartphones all the way up to huge computing clusters. This book is for anyone who knows a little machine learning (or not) and who has heard about TensorFlow, but found the documentation too daunting to approach. It introduces the TensorFlow framework and the underlying machine learning concepts that are important to harness machine intelligence. After reading this book, you should have a deep understanding of the core TensorFlow API. |
  | Machine Learning with TensorFlow Being able to make near-real-time decisions is becoming increasingly crucial. To succeed, we need machine learning systems that can turn massive amounts of data into valuable insights. But when you're just starting out in the data science field, how do you get started creating machine learning applications? The answer is TensorFlow, a new open source machine learning library from Google. The TensorFlow library can take your high level designs and turn them into the low level mathematical operations required by machine learning algorithms. Machine Learning with TensorFlow teaches readers about machine learning algorithms and how to implement solutions with TensorFlow. It starts with an overview of machine learning concepts and moves on to the essentials needed to begin using TensorFlow. Each chapter zooms into a prominent example of machine learning. Readers can cover them all to master the basics or skip around to cater to their needs. By the end of this book, readers will be able to solve classification, clustering, regression, and prediction problems in the real world. |