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.
DS-GA 1008 ยท SPRING 2021
Instructorsโ: Lectures โ Yann LeCun | Practicum โ Alfredo Canziani
Lecturesโ: โMondays, 9:30 โ 11:30am EST, Zoom
Practica: โTuesdays, 9:30 โ 10:30am EST
Forumโ: โr/NYU_DeepLearning
Discord: โNYU DL
Material: 2021 Repo
Please note we’re officially supporting direct communication with students taking this course online via our Reddit and Discord platforms.
2021 edition disclaimer
Check the repoโs README.md
and learn about:
- Content new organisation
- The semesterโs second half intellectual dilemma
- This semester repository
- Previous releases
Lectures
Most of the lectures, labs, and notebooks are similar to the previous edition, nevertheless, some are brand new. I will try to make clear which is which.
Legend: ๐ฅ slides, ๐ notes, ๐ Jupyter notebook, ๐ฅ YouTube video.
Translations
๐ฌ๐ง English | ๐ซ๐ท French
If you’re interested in assisting the Deep Learning team with translation, please contact Alfredo Canziani at canziani@nyu.edu.
Theme 1: Introduction
- History and resources ๐ฅ ๐ฅ
- Gradient descent and the backpropagation algorithm ๐ฅ ๐ฅ
- Neural nets inference ๐ฅ ๐
- Modules and architectures ๐ฅ
- Neural nets training ๐ฅ ๐ฅ ๐๐
- Homework 1: backprop
Theme 2: Parameters sharing
- Recurrent and convolutional nets ๐ฅ ๐ฅ ๐
- ConvNets in practice ๐ฅ ๐ฅ ๐
- Natural signals properties and the convolution ๐ฅ ๐ฅ ๐
- Recurrent neural networks, vanilla and gated (LSTM) ๐ฅ ๐ฅ ๐๐
- Homework 2: RNN & CNN
Theme 3: Energy based models, foundations
- Energy based models (I) ๐ฅ ๐ฅ
- Inference for LV-EBMs ๐ฅ ๐ฅ
- What are EBMs good for? ๐ฅ
- Energy based models (II) ๐ฅ ๐ฅ ๐
- Training LV-EBMs ๐ฅ ๐ฅ
- Homework 3: structured prediction
Theme 4: Energy based models, advanced
- Energy based models (III) ๐ฅ ๐ฅ
- Unsup learning and autoencoders ๐ฅ ๐ฅ
- Energy based models (VI) ๐ฅ ๐ฅ
- From LV-EBM to target prop to (any) autoencoder ๐ฅ ๐ฅ
- Energy based models (V) ๐ฅ ๐ฅ
- AEs with PyTorch and GANs ๐ฅ ๐ฅ ๐๐
Theme 5: Associative memories
Theme 6: Graphs
- Graph transformer nets [A][B] ๐ฅ ๐ฅ
- Graph convolutional nets (I) [from last year] ๐ฅ ๐ฅ
- Graph convolutional nets (II) ๐ฅ ๐ฅ ๐
Theme 7: Control
- Planning and control ๐ฅ ๐ฅ
- The Truck Backer-Upper ๐ฅ ๐ฅ ๐
- Prediction and Planning Under Uncertainty ๐ฅ ๐ฅ
Theme 8: Optimisation
Miscellaneous
- SSL for vision [A][B] ๐ฅ ๐ฅ
- Low resource machine translation [A][B] ๐ฅ ๐ฅ
- Lagrangian backprop, final project, and Q&A ๐ฅ ๐ฅ ๐
DS-GA 1008 ยท SPRING 2020 ยท CDS
Instructorsโ: Lectures โ Yann LeCun | Practicum โ Alfredo Canziani
Lecturesโ: โMondays, 16:55 โ 18:35
Practica: โTuesdays, 19:10 โ 20:00
Materialโ: Google Drive, Notebooks
NYU Deep Learning Reddit
Lectures
Legend: ๐ฅ slides, ๐ Jupyter notebook, ๐ฅ YouTube video.
Translations
๐ฌ๐ง English | ๐ธ๐ฆ Arabic | ๐ง๐ฉ Bengali, Bangla | ๐จ๐ณ Chinese | ๐ซ๐ท French | ๐ญ๐บ Hungarian | ๐ฎ๐น Italian | ๐ฏ๐ต Japanese | ๐ฐ๐ท Korean | ๐ฎ๐ท Persian | ๐ต๐น Portuguese | ๐ท๐บ Russian | ๐น๐ท Turkish | ๐ท๐ธ Serbian | ๐ช๐ธ Spanish | ๐ป๐ณ Vietnamese
People
— Yann
Deep Learning by New York University, Yann LeCun, Alfredo Canziani is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Based on a work at https://cds.nyu.edu/deep-learning/