UPD (April 20, 2016): Scikit Flow has been merged into TensorFlow since version 0.8 and now called TensorFlow Learn or tf.learn.Google released a machine learning framework called TensorFlow and it’s taking the world by storm. 10k+ stars on Github, a lot of publicity and general excitement in between AI researchers.Now, but how you to use it for something regular problem Data Scientist may have? (and if you are AI researcher — we will build up to interesting problems over time).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?The answer is two part:Let’s start with simple example — take Titanic dataset from Kaggle.First, make sure you have installed TensorFlow and Scikit Learn with few helpful libs, including Scikit Flow that is simplifying a lot of work with TensorFlow:You can get dataset and the code from http://github.com/ilblackdragon/tf_examplesQuick look at the data (use iPython or iPython notebook for ease of interactive exploration):Let’s test how we can predict Survived class, based on float variables in Scikit Learn:We separate dataset into features and target, fill in N/A in the data with zeros and build a logistic regression. Predicting on the training data gives us some measure of accuracy (of cause it doesn’t properly evaluate the model quality and test dataset should be used, but for simplicity we will look at train only for now).Now using tf.learn (previously Scikit Flow):Congratulations, you just built your first TensorFlow model!TF.Learn is a library that wraps a lot of new APIs by TensorFlow with nice and familiar Scikit Learn API.TensorFlow is all about a building and executing graph. This is a very powerful concept, but it is also cumbersome to start with.Looking under the hood of TF.Learn, we just used three parts:Even as you get more familiar with TensorFlow, pieces of Scikit Flow will be useful (like graph_actions and layers and host of other ops and tools). See future posts for examples of handling categorical variables, text and images.Part 2 — Deep Neural Networks, Custom TensorFlow models with Scikit Flow and Digit recognition with Convolutional Networks.From a quick cheer to a standing ovation, clap to show how much you enjoyed this story.Co-Founder @ NEAR.AI — teaching machines to code. I’m tweeting as @ilblackdragon.