introduction to deep learning

AI Research

Why AI is Harder Than We Think

Yannic Kilcher  explains how the AI community has gone through regular cycles of AI Springs, where rapid progress gave rise to massive overconfidence, high funding, and overpromise, followed by these promises being unfulfilled, subsequently diving into periods of disenfranchisement and underfunding, called AI Winters. This video he explores a paper which examines the reasons for […]

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AI Research

GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton’s Paper Explained)

Yannic Kilcher covers a paper where Geoffrey Hinton describes GLOM, a Computer Vision model that combines transformers, neural fields, contrastive learning, capsule networks, denoising autoencoders and RNNs. GLOM decomposes an image into a parse tree of objects and their parts. However, unlike previous systems, the parse tree is constructed dynamically and differently for each input, […]

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AI Robotics

Efficient Computing for Deep Learning, Robotics, and AI

Lex Fridman shared this lecture by Vivienne Sze in January 2020 as part of the MIT Deep Learning Lecture Series. Website: https://deeplearning.mit.edu Slides: http://bit.ly/2Rm7Gi1 Playlist: http://bit.ly/deep-learning-playlist LECTURE LINKS: Twitter: https://twitter.com/eems_mit YouTube: https://www.youtube.com/channel/UC8cviSAQrtD8IpzXdE6dyug MIT professional course: http://bit.ly/36ncGam NeurIPS 2019 tutorial: http://bit.ly/2RhVleO Tutorial and survey paper: https://arxiv.org/abs/1703.09039 Book coming out in Spring 2020! OUTLINE: 0:00 – Introduction […]

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