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AMVA4NewPhysics

A Marie Sklodowska-Curie ITN funded by the Horizon2020 program of the European Commission

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fast.ai

Journey through Fast.AI: II – Columnar data

by Giles Strong

Welcome back to the second part of my journey through the Fast.AI deep-learning course; beginning section here. Last time I gave an example of analysing images, now I’ll move on to working with columnar data.

Columnar data is a form of structured data, meaning that the features of the data are already extracted (in this case into columns), unlike in images or audio where features must be learnt or carefully constructed by hand. Continue reading “Journey through Fast.AI: II – Columnar data”

Journey through Fast.AI: I – Introduction and image data

by Giles Strong

For the past few months I’ve been following the Fast.AI Deep-Learning for Coders course. An online series of lectures accompanied with Jupyter notebooks and python library built around PyTorch. The course itself is split into two halves: the first uses a top-down approach to teach state of the art techniques and best practices for deep learning in order to achieve top results on well established problems and datasets, with later lessons delving deeper into the code and mathematics; the second half deals with more with the cutting edge of deep learning, and focuses on less-well-founded problems, such as generative modelling, and recent experimental technologies which are still be developed. Continue reading “Journey through Fast.AI: I – Introduction and image data”

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