Online course: Practical Deep Learning for Coders (Pytorch), by fast.ai,
Michael Zibulevsky
"Last year we announced that we were developing a new deep learning course based on Pytorch (and a new library we have built, called fastai), with the goal of allowing more students to be able to achieve world-class results with deep learning. Today, we are making this course, Practical Deep Learning for Coders 2018, generally available for the first time"
The new course is 80% new material, reflecting the major developments that have occurred in the last 12 months in the field. However, the key educational approach is unchanged: a top-down, code-first approach to teaching deep learning that is unique. We teach “the whole game”–starting off by showing how to use a complete, working, very usable, state of the art deep learning network to solve real world problems, by using simple, expressive tools. And then gradually digging deeper and deeper into understanding how those tools are made, and how the tools that make those tools are made, and so on… We always teach through examples: ensuring that there is a context and a purpose that you can understand intuitively, rather than starting with algebraic symbol manipulation. For more information, have a look at Providing a Good Education in Deep Learning, one of the first things we wrote after fast.ai was launched.
A good example of this approach is shown in the article Fun with small image data-sets, where a fast.ai student shows how to use the teachings from lesson 1 to build near perfect classifiers with <20 training images.