Detailed Syllabus and Lectures

Lecture 14: Massive Models and Scaling Laws (slides)

scaling laws, massive text models, applications of massive models

Please study the following material in preparation for the class:

Required Reading:

• [Blog post] AI and Compute, OpenAI.
• Scaling Laws for Neural Language Models, Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, Dario Amodei, 2020.
• Training Compute-Optimal Large Language Models, Jordan Hoffmann et al., 2022.

Suggested Video Material:

• Mohit Iyyer's lecture on Scaling Laws for Large LMs

Additional Resources:

• [Blog post] What Language Model to Train if You Have One Million GPU Hours?, Julien Launay.

Lecture 13: Self-supervised Learning (slides)

what is self-supervised learning, self-supervised learning in NLP, self-supervised learning in vision, multimodal self-supervised learning

Please study the following material in preparation for the class:

Required Reading:

• [Blog post] Self-supervised learning: The dark matter of intelligence, Yann LeCun and Ishan Misra.
• [Blog post] Self-Supervised Representation Learning, Lilian Weng.

Suggested Video Material:

• Yann LeCun's PAISS 2019 talk on Self-Supervised Learning
• Ishan Misra's lecture on Self-supervised learning (SSL) in computer vision
• Andrei Bursuc, Spyros Gidaris, Aäron van den Oord, Spyros Gidaris, Andrei Bursuc, Mathilde Caron, Jean-Baptiste Alayrac and Adrià Recasen's tutorial titled Leave Those Nets Alone: Advances in Self-Supervised Learning at CVPR 2021.

Additional Resources:

• Distributed Representations of Words and Phrases and their Compositionality, Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, Jeffrey Dean, NIPS 2013.
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, NAACL 2019.
• Context Encoders: Feature Learning by Inpainting, Deepak Pathak, Philipp Krähenbühl, Jeff Donahue, Trevor Darrell, Alexei A. Efros, CVPR 2016.
• Unsupervised Visual Representation Learning by Context Prediction, Carl Doersch, Abhinav Gupta, Alexei A. Efros
• Unsupervised Representation Learning by Predicting Image Rotations, Spyros Gidaris, Praveer Singh, Nikos Komodakis, ICLR 2018.
• Representation Learning with Contrastive Predictive Learning, Aaron van den Oord, Yazhe Li, Oriol Vinyals, ICLR 2018.
• A Simple Framework for Contrastive Learning of Visual Representations, Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey Hinton, arXiv preprint arXiv:2002.05709, 2020.
• Revisiting Self-Supervised Visual Representation Learning , Alexander Kolesnikov, Xiaohua Zhai, Lucas Beyer, CVPR 2019.

Lecture 12: Variational Autoencoders, Denoising Diffusion Models (slides)

variational autoencoders (VAEs), vector quantized variational autoencoders (VQ-VAEs), denoising diffusion models

Please study the following material in preparation for the class:

Required Reading:

• Tutorial on Variational Autoencoders, Carl Doersch.
• An Introduction to Variational Autoencoders, Diederik P. Kingma, Max Welling.
• Diffusion Models: A Comprehensive Survey of Methods and Applications, Ling Yang et al.

Suggested Video Material:

• Generative networks (variational autoencoders and GANs), Pascal Poupart
• Denoising Diffusion-based Generative Modeling: Foundations and Applications, Karsten Kreis, Ruiqi Gao and Arash Vahdat

Additional Resources:

• [Blog post] Intuitively Understanding Variational Autoencoders, Irhum Shafkat.
• [Blog post] A Beginner's Guide to Variational Methods: Mean-Field Approximation, Eric Jang.
• [Blog post] Tutorial - What is a variational autoencoder?, Jaan Altosaar
• beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework, Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, Alexander Lerchner, ICLR 2017.
• Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations, Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem.
• Generating Diverse High-Fidelity Images with VQ-VAE-2, Ali Razavi, Aaron van den Oord, Oriol Vinyals.
• [Blog post] What are Diffusion Models?, Lilian Weng.
• High-Resolution Image Synthesis with Latent Diffusion Models, Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer, CVPR 2022.

Lecture 11: Generative Adversarial Networks, Flow-Based Models (slides)

generative adversarial networks (GANs), conditional GANs, applications of GANs, normalizing flows

Please study the following material in preparation for the class:

Required Reading:

• NIPS 2016 Tutorial: Generative Adversarial Networks, Ian Goodfellow
• Generative Adversarial Networks: An Overview, Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, Anil A Bharath
• How to Train a GAN? Tips and tricks to make GANs work, Soumith Chintala, Emily Denton, Martin Arjovsky, Michael Mathieu
• [Blog post] Normalizing Flows Tutorial, Part 1: Distributions and Determinants, Eric Jang
• [Blog post] Normalizing Flows Tutorial, Part 2: Modern Normalizing Flows, Eric Jang
• [Blog post] Flow-based Deep Generative Models, Lilian Weng

Suggested Video Material:

• NIPS 2016 Tutorial: Generative Adversarial Networks, Ian Goodfellow
• A primer on normalizing flows, Laurent Dinh

Additional Resources:

• [Blog post] How to Train a GAN? Tips and tricks to make GANs work, Soumith Chintala, Emily Denton, Martin Arjovsky and Michael Mathieu.
• [Blog post] The GAN Zoo, Avinash Hindupur
• [Blog post] GAN Playground, Reiichiro Nakano
• [Blog post] GANs comparison without cherry-picking, Junbum Cha
• [Twitter thread] Thread on how to review papers about generic improvements to GANs, Ian Goodfellow
• Normalizing Flows: An Introduction and Review of Current Methods, Ivan Kobyzev, Simon J.D. Prince, and Marcus A. Brubaker, arXiv preprint, arXiv:1908.09257, 2020.
• Normalizing Flows for Probabilistic Modeling and Inference, George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan, arXiv preprint, arXiv:1912.02762, 2019
• [Blog post] Glow: Better Reversible Generative Models, OpenAI
• Density estimation using Real NVP, Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio, ICLR 2017.

Lecture 10: Autoencoders and Autoregressive Models (slides)

unsupervised representation learning, sparse coding, autoencoders, autoregressive models

Please study the following material in preparation for the class:

Required Reading:

• Chapter #13 of the Deep Learning text book.
• Chapter #14 of the Deep Learning text book.

Suggested Video Material:

• Foundations of Unsupervised Deep Learning, Ruslan Salakhutdinov
• Autoregressive Generative Models with Deep Learning, Hugo Larochelle

Additional Resources:

• Pixel Recurrent Neural Networks, Aaron van den Oord, Nal Kalchbrenner, Koray Kavukcuoglum ICML2016.
• Conditional Image Generation with PixelCNN Decoders, Aaron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, Koray Kavukcuoglu, NIPS2016.
• Unsupervised Feature Learning and Deep Learning, Andrew Ng.
• [Blog post] Unsupervised Sentiment Neuron, Alec Radford, Ilya Sutskever, Rafal Jozefowicz, Jack Clark and Greg.

Lecture 9: Graph Neural Networks (slides)

graph structured data, graph neural nets (GNNs), GNNs for ”classical” network problems

Please study the following material in preparation for the class:

Required Reading:

• Semi-Supervised Classification with Graph Convolutional Networks, Thomas Kipf, Max Welling, ICLR 2017
• Relational inductive biases, deep learning, and graph networks, Peter W. Battaglia et al., arXiv Preprint arXiv:1806.01261, 2018

Suggested Video Material:

• Principles and applications of relational inductive biases in deep learning, Kelsey Allen, MIT CBMM Talks

Additional Resources:

• A Practical Tutorial on Graph Neural Networks, Isaac Ronald Ward, Jack Joyner, Casey Lickfold, Yulan Guo, Mohammed Bennamoun, ACM Computing Surveys, Vol. 54, No: 10, September 2022.
• A Gentle Introduction to Graph Neural Networks, Benjamin Sanchez-Lengeling, Emily Reif, Adam Pearce, Alexander B. Wiltschko, Distill, 2021
• [Blog post] Graph Convolutional Networks, Thomas Kipf

Lecture 8: Attention and Transformers (slides)

content-based attention, location-based attention, soft vs. hard attention, self-attention, attention for image captioning, transformer networks, vision transformers

Please study the following material in preparation for the class:

Required Reading:

• Attention and Augmented Recurrent Neural Networks, Chris Olah and Shan Carter. Distill, 2016
• [Blog post] The Illustrated Transformer, Jay Alammar
• [Blog post] Transformers for Image Recognition at Scale, Neil Houlsby and Dirk Weissenborn

Suggested Video Material:

• Attention and Memory in Deep Learning, Alex Graves
• Attention is all you need attentional neural network models, Łukasz Kaiser
• Vision Transformers explained, AI Coffee Break with Letitia

Additional Resources:

• Neural Machine Translation by Jointly Learning to Align and Translate, D. Bahdanau, K. Cho, Y. Bengio, ICLR 2015
• Sequence Modeling with CTC, Awni Hannun, Distill, 2017
• Recurrent Models of Visual Attention, V. Mnih, N. Heess, A. Graves, K. Kavukcuoglu, NIPS 2014
• DRAW: a Recurrent Neural Network for Image Generation, K. Gregor, I. Danihelka, A. Graves, DJ Rezende, D. Wierstra, ICML 2015
• Attention Is All You Need, Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin, NIPS 2017
• [Blog post] What is DRAW (Deep Recurrent Attentive Writer)?, Kevin Frans
• [Blog post] The Transformer Family, Lilian Weng

Lecture 7: Recurrent Neural Networks (slides)

sequence modeling, recurrent neural networks (RNNs), RNN applications, vanilla RNN, training RNNs, long short-term memory (LSTM), LSTM variants, gated recurrent unit (GRU)

Please study the following material in preparation for the class:

Required Reading:

• Chapter #10 of the Deep Learning text book.
• Section 5 of Generating Sequence with Recurrent Neural Networks, A. Graves, ArXiV

Suggested Video Material:

• Efstratios Gavves and Max Welling's Lecture 8
• Recurrent Neural Networks and Language Models, Richard Socher

Additional Resources:

• [Blog post] Understanding LSTM Networks, Chris Olah.
• [Blog post] The Unreasonable Effectiveness of Recurrent Neural Networks, Andrej Karpathy.
• Learning Long-Term Dependencies with Gradient Descest is Difficult, Yoshua Bengio, Patrice Simard, and Paolo Frasconi.
• Long Short-Term Memory, Sepp Hochreiter and Jürgen Schmidhuber.

Lecture 6: Understanding and Visualizing Convolutional Neural Networks (slides)

transfer learning, interpretability, visualizing neuron activations, visualizing class activations, pre-images, adversarial examples, adversarial training

Please study the following material in preparation for the class:

Required Reading:

• Matthew D Zeiler and Rob Fergus, Visualizing and Understanding Convolutional Networks, ECCV 2014.
• Christian Szegedy et al. Intriguing properties of neural networks, arXiv preprint arXiv:1312.6199v4

Suggested Video Material:

• Andrej Karpathy's Stanford CS231n Lecture 9

Additional Resources:

• [Blog post] Understanding Neural Networks Through Deep Visualization, Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson.
• [Blog post] The Building Blocks of Interpretability, Chris Olah, Arvind Satyanarayan, Ian Johnson, Shan Carter, Ludwig Schubert, Katherine Ye and Alexander Mordvintsev.
• [Blog post] Feature Visualization, Chris Olah, Alexander Mordvintsev and Ludwin Schubert.
• [Blog post] An Overview of Early Vision in InceptionV1, Chris Olah, Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, Shan Carter.
• [Blog post] OpenAI Microscope.
• [Blog post] Breaking Linear Classifiers on ImageNet, Andrej Karpathy.
• [Blog post] Attacking machine learning with adversarial examples, OpenAI.

Lecture 5: Convolutional Neural Networks (slides)

convolution layer, pooling layer, cnn architectures, design guidelines, semantic segmentation networks, addressing other tasks

Please study the following material in preparation for the class:

Required Reading:

• Chapter #9 of the Deep Learning textbook.

Suggested Video Material:

• Andrej Karpathy's Stanford CS231n Lecture 7
• Justin Johnson's Stanford CS231n Lecture 8
• Kaiming He's tutorial on Deep Residual Networks

Additional Resources:

• Andrej Karpathy's CS231n notes on Convolutional Networks.
• Hiroshi Kuwajima’s Memo on Backpropagation in Convolutional Neural Networks.
• A guide to convolution arithmetic for deep learning, Vincent Dumoulin and Francesco Visin.
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review, Waseem Rawat and Zenghui Wang.
• [Blog post] Understanding Convolutions, Christopher Olah.
• [Blog post] Deconvolution and Checkerboard Artifacts, Augustus Odena, Vincent Dumoulin, Chris Olah.
• [Blog post] Deep Learning for Object Detection: A Comprehensive Review, Joyce Xu.
• [Blog post] A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN, Dhruv Parthasarathy

Lecture 4: Training Deep Neural Networks (slides)

data preprocessing, weight initialization, normalization, regularization, model ensembles, dropout, optimization methods

Please study the following material in preparation for the class:

Required Reading:

• Chapter #7 and Chapter #8 of the Deep Learning text book.

Suggested Video Material:

• Efstratios Gavves' Lecture 3.
• Andrej Karpathy's video lecture titled Building makemore Part 3: Activations & Gradients, BatchNorm

Additional Resources:

• Stochastic Gradient Descent Tricks, Leon Bottou.
• Section 3 of Practical Recommendations for Gradient-Based Training of Deep Architectures, Yoshua Bengio.
• Troubleshooting Deep Neural Networks: A Field Guide to Fixing Your Model, Josh Tobin.
• [Blog post] Initializing neural networks, Katanforoosh & Kunin, deeplearning.ai.
• [Blog post] Parameter optimization in neural networks, Katanforoosh et al., deeplearning.ai.
• [Blog post] The Black Magic of Deep Learning - Tips and Tricks for the practitioner, Nikolas Markou.
• [Blog post] An overview of gradient descent optimization algorithms, Sebastian Ruder.
• [Blog post] Why Momentum Really Works, Gabriel Goh

Lecture 3: Multi-layer Perceptrons (slides)

feed-forward neural networks, activation functions, chain rule, backpropagation, computational graph, automatic differentiation, distributed word representations

Please study the following material in preparation for the class:

Required Reading:

• Chapter 6 of the Deep Learning text book.
• Yoav Goldberg's A Primer on Neural Network Models for Natural Language Processing, 3 to 6
• Mathieu Blondel's presentation on Automatic differentiation

Suggested Video Material:

• Hugo Larochelle’s video lectures, 1.1 to 1.6, 2.1 to 2.7
• Andrej Karpathy's video lecture titled The spelled-out intro to neural networks and backpropagation: building micrograd

Additional Resources:

• Hinton's Coursera class on Neural Networks, Lecture 1 to 3.
• [Blog post] Neural Networks, Manifolds, and Topology, Christopher Olah.
• [Blog post] Calculus on Computational Graphs: Backpropagation, Christopher Olah.
• Chapter 16 of Jurafsky and Martin's Speech and Language Processing book (3rd Edition draft)

Lecture 2: Machine Learning Overview (slides)

types of machine learning problems, linear models, loss functions, linear regression, gradient descent, overfitting and generalization, regularization, cross-validation, bias-variance tradeoff, maximum likelihood estimation

Please study the following material in preparation for the class:

Required Reading:

• Chapter 5 of the Deep Learning text book.

Suggested Video Material:

• Machine Learning, Doina Precup (Deep Learning Summer School, Montreal 2016)

Additional Resources:

• A few useful things to know about machine learning, P. Domingos. Communications of the ACM, 55 (10), 78-87, 2012.
• The uneasy relationship between deep learning and (classical) statistics, Boaz Barak, June 2022.

Lecture 1: Introduction to Deep Learning (slides)

course information, what is deep learning, a brief history of deep learning, compositionality, end-to-end learning, distributed representations

Please study the following material in preparation for the class:

Required Reading:

• Chapter 1 of the Deep Learning text book.
• [Blog post] AI Winter. How Canadians contributed to end it?, Pavan Mirla.
• The Bandwagon, Claude E. Shannon. IRE Transactions on Information Theory, Vol. 2, Issue 3, 1956
• Chapter 1: The Philosophy and the Approach of David Marr's Vision, 1982.

Additional Resources:

• The unreasonable effectiveness of deep learning in artificial intelligence, Terrence J. Sejnowski, PNAS, 2020.
• Deep Learning, Yann LeCun, Yoshio Bengio, Geoffrey Hinton. Nature, Vol. 521, 2015.
• Deep Learning in Neural Networks: An Overview, Juergen Schmidhuber. Neural Networks, Vol. 61, pp. 85–117, 2015.
• On the Origin of Deep Learning, Haohan Wang and Bhiksha Raj, arXiv preprint arXiv:1702.07800v4, 2017