In November, 2015, Google open-sourced its numerical computation library called TensorFlow using data flow graphs. Its flexible implementation and architecture enables you to focus on building the computation graph and deploy the model with little efforts on heterogeous platforms such as mobile devices, hundreds of machines, or thousands of computational devices. TensorFlow is generally very straightforward to use in a sense that most of the researchers in the research area without experience of using this library could understand what's happening behind the code blocks. TensorFlow provides a good backbone for building different shapes of machine learning applications. However, there's a large number of potential users, including some researchers, data scientists, and students who may be familiar with many data science concepts/algorithms already but who never get involved in deep learning research/applications, may found it really hard to start hacking.

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.

Google's TensorFlow has been publicly available since November, 2015, and there is no disputing that, in a few short months, it has made an impact on machine learning in general, and on deep learning specifically. There is evidence of widespread acceptance via blog posts, academic papers, and tutorials all over the web. It is, of course, difficult to estimate true adoption rates, but TensorFlow's Github repository has nearly twice the number of stars of both the next most-starred machine learning project, Scikit-learn, and closest deep learning project, Berkeley Vision and Learning Center's Caffe. While not concretely indicative of TensorFlow having become the leader in the space, it is fairly easy to surmise that, given its fairly recent release, there has been considerable interest in, and use of, Google's deep learning library. For the most part, TensorFlow is relatively straightforward to use, and neural network afficianados without experience using the library could look at a given network's code and get an intuititive sense of what is going on.

In November, 2015, Google open-sourced its numerical computation library called TensorFlow using data flow graphs. Its flexible implementation and architecture enables you to focus on building the computation graph and deploy the model with little efforts on heterogeous platforms such as mobile devices, hundreds of machines, or thousands of computational devices. TensorFlow is generally very straightforward to use in a sense that most of the researchers in the research area without experience of using this library could understand what's happening behind the code blocks. TensorFlow provides a good backbone for building different shapes of machine learning applications. However, there's a large number of potential users, including some researchers, data scientists, and students who may be familiar with many data science concepts/algorithms already but who never get involved in deep learning research/applications, may found it really hard to start hacking.