Top 10 Free Books And Resources For Learning TensorFlow

TensorFlow, the open source software library developed by the Brain team, is a framework for building deep-learning neural networks. It is also considered one of the best ways to build in-depth learning models by people learning by machine, around the world. In deep learning models, which rely on a lot of data and resources, is used considerably.

Given its flexible architecture for easy deployment on different platforms such as CPUs, GPUs, and TPUs, remains one of the favorite libraries to get into ML. The huge popularity also means that tech enthusiasts are constantly looking to learn more and work more with this library. Although many tutorials, books, projects, videos, white papers, and other resources are available, we offer you these 10 free resources to get started with TensorFlow and get your concepts clear. The list is not in a specific order.

1 – By TensorFlow (Website):
What a better source than the makers themselves! These tutorials offered by on their website are the perfect tools to get hands-on training. The starts with training your first neural network based on image classification and progresses further with the use of tf.keras, a high-level API used to build and train models. It also contains advanced of text classification, regression and other concepts. You can also learn to save, restore, share and recreate your work. Click here to take a tutorial.

2 – White Paper (paper):
This preliminary white paper by researchers talks about models and basic concepts of TensorFlow. Titled large-scale machine learning on a heterogeneous distributed system, the paper starts with a brief introduction to the concept and starts to talk extensively about examples of operation types, implementation, implementation in a single device and multiple devices. Along with other important concepts, this document also has a detailed schematic explanation of the concepts. Click here to read it.

3 – Stanford course on For Deep Learning Research (PPT):
With this course from Stanford University, you can download notes and slides that are completely focused on for in-depth research. The entire course is based on TensorFlow, which makes it very easy for the user to get a thorough basic understanding of TensorFlow. It also contains course material on setting up the TensorFlow, basic operations, TensorFlow optimizers, examples of image classification, reinforcement learning and much more. Click here to read it.

4 – First Contact With TensorFlow: Getting started with Deep Learning by Jordi Torres (EBook):
This book by Jordi Torres, a professor and researcher at UPC and BSC, was written during a holiday to share his of with his students. It includes a practical approach to learning TensorFlow, starting with the basics, to understand multi-layer neural networks. It deals in detail with concepts such as linear regression, clustering and single-layer neural networks. Although it was launched with the intention of equipping its students with the basic principles of TensorFlow, it has now gone viral because it was of great value to many students and practitioners. Although it is based on the old TensorFlow release (TensorFlow-0.5.0), it is a good reading method for introduction to the topic.  Click here to read it.

5 – Getting started with by Giancarlo Zaccone (EBook):
This is one of the best sources to help you get started with engine, a robust, user-friendly and customizable ML-code software library for deep learning and neural networks. It starts with an introduction to the basics, followed by details about creating programs with TensorFlow. It would help you solve mathematical concepts, ML and in-depth learning concepts along the way.  Claim your free book here.

6 – Learning by Itay Lieder, Tom Hope, Yehezkel S. Resheff (Ebook):
This book provides an end-to-end to TensorFlow, which allows you to train and build neural networks for vision, NLP, speech recognition, general predictive analysis and others. The book emphasizes practical and practical approaches to TrumpFlow principles before you delve into deeper concepts. After reading the book you could get a thorough detail from TensorFlow, build in-depth learning models, scale up TF and implement TF in the production setting.  Click here to read.

7 – self-study by Bharath Ramsundar (slides):
This reading slides by B Ramasundar is an excellent introduction to that draws many parallels between NumPy and TensorFlow codes. He has given details such as NumPy to TensorFlow dictionary, linear regression in TF, gradient calculation and others in his descriptive slides. Get to know the basics of TensorFlow with these slides. Click here to read.

8 – Deep learning with By Cognitive Class (online course):
Cognitive Class, an initiative of IBM, aims to democratize to learning data and cognitive computing. This Cognitive Class course focuses on this that is free for ML enthusiasts. Something at an advanced level is suitable for anyone who is interested in improving his skills in machine learning, deep learning, and TensorFlow. It includes in-depth sources, from introduction to to CNN, RNN and other areas. This course with your own pace can be taken at any time.  Click here to read.

9 – TensorFlow: a large-scale machine learning system (paper):
This article by brain researchers is a good tool to get understanding and work in TensorFlow. With different use cases and implementation of different models, this article attempts to describe the data stream model as opposed to existing systems. It also explains image classification, modeling and others using TensorFlow. Click here to read.

10 – Free Github resources:
There are many sources available on Github that explain how works. These are from novice to advanced ML enthusiasts who want to explore TensorFlow skills. This course on Github covers, for example, the basic principles, regression, classification, clustering and other details of TensorFlow.  Whereas, another Github course talks details about the simple linear model, CNN, C Keras API, and others. Here is another instance of resources on Github.

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