From Solving Equations to Deep Learning: A TensorFlow Python Tutorial

There have been some remarkable developments lately in the world of artificial intelligence, from much publicized progress with self-driving cars to machines now composing Chopin imitations or just being really good at video games. Central to these advances are a number of tools around to help derive deep learning and other machine learning models, with Torch, Caffe, and Theano amongst those at the fore. However, since Google Brain went open source in November 2015 with their own framework, TensorFlow, we have seen the popularity of this software library skyrocket to be the most popular deep learning framework. Reasons include the wealth of support and documentation available, its production readiness, the ease of distributing calculations across a range of devices, and an excellent visualization tool: TensorBoard. Ultimately, TensorFlow manages to combine a comprehensive and flexible set of technical features with great ease of use.