DEEP LEARNING
DS-GA 1008 · SPRING 2020 · NYU CENTER FOR DATA SCIENCE
| INSTRUCTORS | Yann LeCun & Alfredo Canziani |
| LECTURES | Mondays 16:55 – 18:35, GCASL C95 |
| PRACTICA | Tuesdays 19:10 – 20:00, GCASL C95 |
| FORUM | r/NYU_DeepLearning |
| DISCORD | NYU DL |
| 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: 🖥 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 !