Machine Learning

A framework for thinking about neural networks, and supervised machine learning.

A mathematical inquiry into kernel functions and their use.

Using neural networks to detect elementary particles.

Deriving the mathematics behind backpropagation.

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.

A post mortem analysis of a Data Science approach for determining the existence and decay patterns of the Higgs boson.

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.