release new courses and more

04 September 2020

share this:
Online course

Have you been thinking about getting up to speed with deep learning or applied data ethics? Well, look no further than the latest free courses from recently announced some exciting new releases. Here is their full list of available courses:

Practical deep learning for coders

This course covers the following topics:

  • How to train models that achieve state-of-the-art results in computer vision, natural language processing (NLP), tabular data and collaborative filtering.
  • How to turn models into web applications, and deploy them
  • Why and how deep learning models work, and how to use that knowledge to improve the accuracy, speed, and reliability of models
  • The latest deep learning techniques that really matter in practice
  • How to implement stochastic gradient descent and a complete training loop from scratch
  • How to think about the ethical implications of deep learning and its implementation

Part 2: deep learning from the foundations

Part 2 of the deep learning course shows how to build a state of the art deep learning model from scratch. It covers many topics from the foundations of implementing matrix multiplication and back-propagation, through to high performance mixed-precision training, and the latest neural network architectures and learning techniques.

Applied data ethics

This course focusses on ethics issues that are both urgent and practical. It covers the following topics:

  1. Disinformation
  2. Bias & fairness
  3. Ethical foundations & practical tools
  4. Privacy and surveillance
  5. How did we get here? Our ecosystem
  6. Algorithmic colonialism, and next steps

Computational linear algebra

In this course you can learn how to do matrix computations with acceptable speed and acceptable accuracy.

Code-first introduction to natural language processing

This course teaches a blend of traditional NLP topics (including regex, SVD, naive bayes, tokenization) and recent neural network approaches (including RNNs, seq2seq, attention, and the transformer architecture). It also addresses urgent ethical issues, such as bias and disinformation.

In addition to the courses, also released four libraries:
fastai v2

About is a self-funded research, software development, and teaching lab, focused on making deep learning more accessible. It was founded by Jeremy Howard and Rachel Thomas in 2016, with Sylvain Gugger completing the core team. They provide free courses, software libraries and research papers (with no ads), and pay all of the costs out of their own pockets. Jeremy and Sylvain have recently published a book entitled: “Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD”.

Lucy Smith , Managing Editor for AIhub.
Lucy Smith , Managing Editor for AIhub.

            AIhub is supported by:

Related posts :

Hot papers on arXiv from the past month: July 2021

What’s hot on arXiv? Here are the most tweeted papers that were uploaded onto arXiv during July 2021.
02 August 2021, by

Eleven new NSF artificial intelligence research institutes announced

USA National Science Foundation (NSF) partnerships expand National AI Research Institutes to 40 states.
30 July 2021, by

AIhub monthly digest: July 2021 – ICML, protein folding for all, and AI Song Contest winner announced

Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.
29 July 2021, by

©2021 - Association for the Understanding of Artificial Intelligence

©2021 - Association for the Understanding of Artificial Intelligence-