DEEP LEARNING

DS-GA 1008 ยท SPRING 2020 ยท NYU CENTER FOR DATA SCIENCE

INSTRUCTORS Yann LeCun & Alfredo Canziani
LECTURES Mondays 16:55 โ€“ 18:35, GCASL C95
PRACTICA Tuesdays 19:10 โ€“ 20:00, GCASL C95
PIAZZA Access code: DLSP20
MATERIAL Google Drive, Notebooks

Description

This course concerns 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. The prerequisites include: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.

Lectures

Legend: ๐Ÿ–ฅ slides, ๐Ÿ““ Jupyter notebook, ๐ŸŽฅ YouTube video.

Week Format Title Resources
โ‘  Lecture History and motivation ๐Ÿ–ฅ๏ธ ๐ŸŽฅ
Evolution and DL
Practicum Neural nets (NN) ๐Ÿ““ ๐Ÿ““ ๐ŸŽฅ
โ‘ก Lecture SGD and backprop ๐Ÿ–ฅ๏ธ ๐ŸŽฅ
Backprop in practice
Practicum NN training ๐Ÿ–ฅ ๐Ÿ““ ๐Ÿ““ ๐ŸŽฅ
โ‘ข Lecture Parameter transformation ๐Ÿ–ฅ๏ธ ๐ŸŽฅ
CNN
Practicum Natural signals' properties ๐Ÿ–ฅ ๐Ÿ““ ๐ŸŽฅ
โ‘ฃ Practicum 1D convolutions ๐Ÿ““ ๐ŸŽฅ
โ‘ค Lecture Optimisation I ๐Ÿ–ฅ๏ธ ๐ŸŽฅ
Optimisation II
Practicum CNN, autograd ๐Ÿ““ ๐Ÿ““ ๐ŸŽฅ
โ‘ฅ Lecture CNN applications ๐Ÿ–ฅ๏ธ ๐Ÿ–ฅ๏ธ ๐ŸŽฅ
RNNs and attention
Practicum Training RNNs ๐Ÿ““ ๐Ÿ““ ๐Ÿ–ฅ๏ธ ๐ŸŽฅ
โ‘ฆ Lecture Energy-Based Models ๐Ÿ–ฅ๏ธ ๐ŸŽฅ
SSL, EBM
Practicum Autoencoders ๐Ÿ–ฅ๏ธ ๐Ÿ““ ๐ŸŽฅ
โ‘ง Lecture Contrastive methods ๐Ÿ–ฅ๏ธ ๐ŸŽฅ
Regularised latent
Practicum Training VAEs ๐Ÿ–ฅ๏ธ ๐Ÿ““ ๐ŸŽฅ
โ‘จ Lecture Sparsity ๐Ÿ–ฅ๏ธ ๐ŸŽฅ
World model, GANs
Practicum Training GANs ๐Ÿ–ฅ๏ธ ๐Ÿ““ ๐ŸŽฅ
โ‘ฉ Lecture CV SSL I ๐Ÿ–ฅ๏ธ ๐ŸŽฅ
CV SSL II
Practicum Predictive Control ๐Ÿ–ฅ๏ธ ๐Ÿ““ ๐ŸŽฅ
โ‘ช Lecture Activations ๐Ÿ–ฅ๏ธ ๐Ÿ–ฅ๏ธ ๐Ÿ–ฅ๏ธ ๐ŸŽฅ
Losses
Practicum PPUU ๐Ÿ–ฅ๏ธ ๐Ÿ““ ๐ŸŽฅ
โ‘ซ Lecture DL for NLP I ๐Ÿ–ฅ๏ธ ๐ŸŽฅ
DL for NLP II
Practicum Attention & transformer ๐Ÿ–ฅ๏ธ ๐Ÿ““ ๐ŸŽฅ
โ‘ฌ Lecture GCNs I ๐Ÿ–ฅ๏ธ ๐ŸŽฅ
GCNs II
Practicum GCNs III ๐Ÿ–ฅ๏ธ ๐Ÿ““ ๐ŸŽฅ
โ‘ญ Lecture Structured Prediction ๐Ÿ–ฅ๏ธ ๐ŸŽฅ
Graphical methods
Practicum Regularisation and Bayesian ๐Ÿ–ฅ๏ธ ๐Ÿ““ ๐Ÿ–ฅ๏ธ ๐Ÿ““ ๐ŸŽฅ

People

Role Photo Contact About
Instructor Yann LeCun
yann@cs.nyu.edu
Silver Professor in CS at NYU
and Turing Award winner
Instructor Alfredo Canziani
canziani@nyu.edu
Asst. Prof. in CS at NYU
Assistant Mark Goldstein
goldstein@nyu.edu
PhD student in CS at NYU
Webmaster Zeming Lin
zl2799@nyu.edu
PhD student in CS at NYU

Disclaimer

All other texts found on this site are lecture notes taken by students of the New York University during lectures given by Yann Le Cun, Alfredo Canziani, Ishan Misra, Mike Lewis and Xavier Bresson. Thus the texts in English were written by about 130 people, which has an impact on the homogeneity of the texts (some write in the past tense, others in the present tense; the abbreviations used are not always the same; some write short sentences, while others write sentences of up to 5 or 6 lines, etc.). It is possible that there may be some omissions: typing errors, spelling mistakes, etc. If you notice any, we invite you to submit a PR on the GitHub directory of the site specifying with an [EN] that it concerns the English translation.

Wishing you a deep reading !