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Introduction to deep learning: history and resources

Difficulty level

This is the Introductory Module to the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this course include: Introduction to Data Science or a Graduate Level Machine Learning.

Topics covered in this lesson


00:00 – Welcome to NYU Deep Learning, Spring 2021
00:43 – History of Deep Learning, 1943–1969
08:14 – The McCulloch-Pitts binary neuron
08:54 – History of Deep Learning, 1970s–2010 1
– History of Deep Learning, 2012–2016
21:15 – Q. How about the future?
25:20 – Q. What are limitation? How about quantum computing?
27:12 – Practicum starts
24:35 – NYU-DLSP20 and '21 websites
37:56 – Remote teaching and learning: Twitter and resources (William Strang, @3Blue1Brown)
42:11 – Classification, decision boundaries, and warping the “space fabric” with a neural net