Machine Learning with PyTroch

Welcome! This is a directory of resources for a training tutorial to be given at the O’Reilly Strata Conference in San Jose on March 5th and 6th, 2018.

General Information

Prerequisites:

  • A working knowledge of Python.
  • Familiarity with precalc math (multiply matrices, dot products of vectors, etc.) and derivatives of simple functions.

Agenda

Machine Learning with PyTorch is split into two days. One the first day, we will first provide an introduction to the supervised training paradigm and the basics of the PyTorch library. Then, we will cover fundamental Deep Learning models and how they relate to the kinds of tasks for which Machine Learning is typically deployed.

On the second day, we will discuss how projects should be structured to avoid common pitfalls and traps. Then, we talk about how models can typically be improved using regularization and other techniques. Next, we will you through two more advanced models—generating surnames using a Recurrent Neural Network and generating captions for images. Then, we provide a substantial amount of time for going through some in-class exercises at your own pace so that you can practice what we’ve taught. Finally, we end the day with a brief overview of other topics in Machine Learning and provide pointers for further learning.