DEEP LEARNING · Deep Learning


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

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: 🖥 slides, 📓 Jupyter notebook, 🎥 YouTube video.

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 !