Yann LeCun’s Deep Learning Course at CDS is Now Fully Online & Accessible to All

Original article was published by NYU Center for Data Science on Deep Learning on Medium


Yann LeCun’s Deep Learning Course at CDS is Now Fully Online & Accessible to All

CDS is excited to announce the release of all materials for Yann LeCun’s Deep Learning, DS-GA 1008, co-taught in Spring 2020 with Alfredo Canziani. This unique course material consists of a mix of close captioned lecture videos, detailed written overviews, and executable Jupyter Notebooks with PyTorch implementations. The course covers the latest techniques in both deep learning and representation learning, focusing on supervised/self-supervised learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Course prerequisites include DS-GA 1001 Intro to Data Science OR a machine learning course.

The 14 week course begins by covering topics such as the history, motivation, and inspiration of deep learning. It subsequently delves deeper into subjects such as optimisation techniques, energy-based models, world models, generative adversarial networks, and model predictive policy learning. Toward its conclusion, the course will explore aspects such as deep learning for NLP, graph convolutional networks, and deep learning for structured prediction.

Additionally, we present multiple translations of course materials in the following languages: English, Arabic, Spanish, Italian, Japanese, Korean, Turkish, Chinese, French, Persian, Russian. And, at a later time, Portuguese, Bengali, and Vietnamese will be added. The translation has been carried out by 470+ volunteers from 17 time zones around the world.

About the Instructors:

Yann LeCun is Silver Professor of Data Science, Computer Science, Neural Science, and Electrical and Computer Engineering at New York University. Concurrently, Yann is also VP and Chief AI Scientist, at Facebook.

He is an ACM Turing Award Laureate and Member of the National Academy of Engineering. In 2018, he won the Harold Pender Award. He also holds the Innovative Research Interchange Medal.

His research interests surround AI, machine learning, computer vision, robotics, computational neuroscience as well as physics of computation.

Some of his notable affiliations are as follows: CILVR Lab, Computer Science Department, part of the Courant Institute of Mathematical Sciences, Center for Data Science, Center for Neural Science, Department of Electrical and Computer Engineering, and Facebook AI Research.

Alfredo Canziani is a Research Assistant Professor of Computer Science and a Deep Learning Research Scientist at NYU Courant Institute of Mathematical Sciences, under the supervision of professors Kyunghyun Cho and Yann LeCun. His research primarily focuses on machine learning for autonomous driving. Alfredo has been exploring deep policy networks actions, uncertainty estimation, and failure detection, as well as long term planning based on latent forward models, which nicely deal with the stochasticity and multimodality of the surrounding environment.

Alfredo holds both a Bachelors and Masters degree in Electrical Engineering cum laude at Trieste University. He also earned his MSc in 2012 at Cranfield University, and his PhD 2017 at Purdue University.

Outside of academics, Alfredo is a professional musician, dancer, and cook.

Both instructors are active on the scientific Twitter social media, interacting with researchers and the general public, through their accounts @ylecun and @alfcnz. They also reply to all the questions left on the comment section on the YouTube videos.

For more information on the Deep Learning course, please visit the Deep Learning course page.

Additional Resources:

Github Page

Reddit Forum

YouTube playlist

By Ashley C. McDonald