Practical Deep Learning for Coders

Mon, 29 Jan 2018, by Wayne Radinsky

Practical Deep Learning for Coders 2018 from "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, following the completion of a preview version of the course by 600 students through our diversity fellowship, international fellowship, and Data Institute in-person programs."

"About 80% of the material is new this year, including: All models train much faster than last year's equivalents, are much more accurate, and require fewer lines of code, greatly simplified access to cloud-based GPU servers, including Crestle, Paperspace, and AWS, shows how to surpass all previous academic benchmarks in text classification, and how to match the state of the art in collaborative filtering, and time series and structured data analysis, leverages the dynamic compilation features of Pytorch to provide deeper understanding of the internals of designing and training models, and covers recent network architectures such as Resnet and ResNeXt, including building a Resnet with batch normalization from scratch."