Resources


Jump to: SoftwareConferences & WorkshopsRelated CoursesPrereq CatchupDeep Learning Self-study Resources

Software

For this course, we strongly recommend using a custom environment of Python packages all installed and maintained via the free 'conda' environment manager from Anaconda, Inc.: https://conda.io/docs/user-guide/getting-started.html

For detailed instructions, see the [Python Setup Instructions page]

High Performance Computing

For your final project, you have two options:

  • use the Tufts High performance computing cluster
  • use Amazon Web Services (AWS)

If you are looking for basic workshops to get help on things like using Linux or the Tufts High-performance computing cluster, checkout the programs in the Tufts Data Lab: https://sites.tufts.edu/datalab/workshops/

Conferences and Workshops

Bayesian Deep Learning workshop at NIPS

Good place to browse for potential project ideas:

Related Courses

Harvard COMPSCI 282R: Topics in Machine Learning - Deep Bayesian Models

Taught by Prof. Finale Doshi-Velez, Fall 2018

Stanford CS 236: Deep Generative Models

Taught by Prof. Stefano Ermon, Fall 2019

https://deepgenerativemodels.github.io/

Summer School on Deep Learning and Bayesian Methods

August 27th – September 1st 2018, Moscow, Russia

Prereq Catchup Resources

Here are some useful resources to help you catch up if you are missing some of the pre-requisite knowledge. Please contribute new resources by starting a topic on the class discussion forum.

To achieve this objective, we expect students to be familiar with:

Probability

First-order gradient-based optimization

Linear algebra

Basic supervised machine learning methods

  • Key concepts:
    • Linear regression
    • Logistic regression

Deep Learning Self-study Resources

Here are some related free online courses which would be good introductions to standard deep learning methods: