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 |