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.
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!
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.
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.
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
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.
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”.
Grow your business with data analytics
Looking to improve your business through data analytics? Are you interested in implementing data mining, automation or artificial intelligence?This book is the ultimate guide to getting started with using data in your business, with a non-technical view and focusing on achieving good outcomes. We don't get bogged down by technical detail or complex algorithms.
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You can also support LearningTensorFlow.com by becoming a patron at Patreon. If we have saved you trawling through heavy documentation, or given you a pointer on where to go next, help us to create new lessons and keep the site running.
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Note: despite the title, this book has no relationship to LearningTensorFlow.com