This section contains tutorials demonstrating how to do specific tasks in TensorFlow. If you are new to TensorFlow, we recommend reading the documents in the "Get Started" section before reading these tutorials. These tutorials focus on machine learning problems dealing with sequence data. These tutorials demonstrate various data representations that can be used in TensorFlow. Although TensorFlow specializes in machine learning, the core of TensorFlow is a powerful numeric computation system which you can also use to solve other kinds of math problems.
Developed by Google Brain, TensorFlow is one of the most popular open-source libraries for numerical computation. This library helps in building and training deep neural network applications and offers APIs for beginners and experts to develop for desktop, mobile, web, and cloud. In this article, we list down 9 free tutorials to become a pro in the open-source machine learning framework, TensorFlow. In this official documentation, you will learn how to use machine learning techniques, utilise machine learning at production scale, creating and deploying TensorFlow models on the web and mobile, understanding TensorFlow's High-Level APIs and much more. In this tutorial, you will learn the basics and advance machine learning topics like Linear Regression, Classifiers, create, train and evaluate a neural network like CNN, RNN, autoencoders, etc.
TensorFlow tutorials written in pyhton (of course) with Jupyter Notebook. Tried to explain as kindly as possible, as these tutorials are intended for TensorFlow beginners. Hope these tutorials to be a useful recipe book for your deep learning projects. Most of the codes are simple refactorings of Aymeric Damien's Tutorial or Nathan Lintz's Tutorial. There could be missing credits.
UPD (April 20, 2016): Scikit Flow has been merged into TensorFlow since version 0.8 and now called TensorFlow Learn. Google released a machine learning framework called TensorFlow and it's taking the world by storm. Now, but how you to use it for something regular problem Data Scientist may have? A reasonable question, why as a Data Scientist, who already has a number of tools in your toolbox (R, Scikit Learn, etc), you care about yet another framework? Let's start with simple example -- take Titanic dataset from Kaggle.