Machine Learning with Python - TensorFlow Basics - Deep Learning with Neural Networks p. 2

Welcome to part two of Deep Learning with Neural Networks and TensorFlow, and part 44 of the Machine Learning tutorial series. In this tutorial, we are going to be covering some basics on what TensorFlow is, and how to begin using it. Libraries like TensorFlow and Theano are not simply deep learning libraries, they are libraries *for* deep learning. They are actually just number-crunching libraries, much like Numpy is. The difference is, however, a package like TensorFlow allows us to perform specific machine learning number-crunching operations like derivatives on huge matricies with large efficiency. We can also easily distribute this processing across our CPU cores, GPU cores, or even multiple devices like multiple GPUs. But that's not all! We can even distribute computations across a distributed network of computers with TensorFlow. So, while TensorFlow is mainly being used with machine learning right now, it actually stands to have uses in other fields, since really it is just a massive array manipulation library. What is a tensor? Up to this point in the machine learning series, we've been working mainly with vectors (numpy arrays), and a tensor can be a vector. Most simply, a tensor is an array-like object, and, as you've seen, an array can hold your matrix, your vector, and really even a scalar. Installing TensorFlow via Ubuntu and virtualbox: https://pythonprogramming.net/installing-tensorflow-machine-learning-tutorial/ https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex