Machine Learning 101

Overview and introduction to feed forward neural networks. Forward propagation is discussed in detail, and we see how we might train a network.

A collection of commonly used machine learning terms, and what they mean.

A practical application of convolutional neural networks.

We examine Google's open source library Tensorflow, and go through its components to understand how it can be used to create scalable machine learning models.

We examine 'genius' internet memes and consider how we might apply techniques from statistical learning to build an abstract solver.

We examine how the popular framework sklearn can be used with the iris dataset to classify species of flowers. We go through all the steps required to make a machine learning model from start to end.