Resources

Recommended reading:

Learning Data Mining with Python

Learning Data Mining with Python (second revision) is the followup to the well received first revision. The new version introduces data mining for those with programming experience, and contains updated chapters with modern libraries including TensorFlow.

In this book, you will gain an understanding of data mining essentials, as well as complete 12 practical and interesting applications. Applications range for image analysis and text understanding to sports predictions.

Buy it today to get a practical and detailed introduction to data mining concepts.

Extended Tutorials

I gave a talk at PyCon AU 2016 on TensorFlow, covering many of the first tutorials on this site.

This talk covers what TensorFlow is, why/when you should use it, and cover the basics surrounding Variables, Placeholders, and Custom Functions. Importantly, there are several use cases *not* focused on data analytics - TensorFlow is more than just a machine learning library!

Get updates

Sign up here to receive infrequent emails from us about updates to the site and when new lessons are released.



* indicates required

Learning

On this page you will find a whole range of extra resources, split up by the topic you want to learn more about.

Official TensorFlow Documentation

The official documentation provided by Google is very Machine Learning focused (as most tutorials are at this stage). That said, they are quite good from that point of view, but they do assume quite a high level of machine learning knowledge.

You can find them at TensorFlow.org. On this site are tutorials, but also background information on how TensorFlow works “under the hood” and academic work.

TensorFlow

Github user aymericdamien has some tutorials on TensorFlow, starting with a basic example before moving into standard machine learning algorithms. You can find them here.

Another Github user nlintz has more tutorials of a similar nature on their page.

Python

There are many great resources to learn the Python programming language more effectively. The official documentation is a great resources, and comes with several tutorials.

Learn Python the hard way is a great book to start with as a beginner. For businesses, Python Charmers is a training organisation for Australia and abroad.

Other Libraries

prettytensor Pretty Tensor provides a high level builder API for TensorFlow. It provides thin wrappers on Tensors so that you can easily build multi-layer neural networks.

tensorflow-white-paper-notes Annotated notes and summaries of the TensorFlow white paper, along with SVG figures and links to documentation

Deep-Q learning Pong with Tensorflow and PyGame

NTM-tensorflow Tensorflow implementation of Neural Turing Machine. This implementation uses an LSTM controller. NTM models with multiple read/write heads are supported.

tensorlayer is designed to provide a higher-level API to TensorFlow in order to speed-up experimentations and developments.

skflow is a simplified interface to TensorFlow that uses a similar API to the popular scikit-learn library for machine learning in python.

Theano and Lasagne are libraries that do similar things with computation as TensorFlow. Their documentation would provide more learning opportunity for TensorFlow – while the code is different, translating the code from Lasagne to TensorFlow would provide a great “extra resource”.

Keep going!

We have an increasing set of lessons that we hope guides you through learning this powerful library. Follow these links to keep going to our next lesson.

You can also use the nav menu at the top of the page to go directly to a specific lesson.

Coming soon (although not written by us):

Get updates

Sign up here to receive infrequent emails from us about updates to the site and when new lessons are released.



* indicates required

If you have any feedback, please see our page here. If you spot any errors with our lessons, please direct them to our Github page with the name of the lesson in which the error resides, so that we can resolve them and close them off there.

If you have larger questions that may involve consultancy, please contact us here