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Lecture 12.4 Scaling up (Mixed precision, Data-parallelism, FSDP)

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Lecture 6.3 variational autoencoders

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Lecture 6.2 Latent variable model

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Lecture 1.3 Autoencoders

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Lecture 1.2 Regression, classification and loss functions

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Lecture 1.1 Neural networks

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Lecture 2.4 Automatic Differentiation (DLVU)

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Lecture 2.3 Backpropagation, a tensor view (DLVU)

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Lecture 2.2 Backpropagation, scalar perspective (DLVU)

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Lecture 1.1 Organization of the course

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Lecture 12.3 Famous transformers (BERT, GPT-2, GPT-3)

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Lecture 12.1 Self-attention

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Lecture 12.2 Transformers

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Lecture 11.3 World Models

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Lecture 11.2 Variance Reduction for Policy Gradient (Actor-Critic)

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Lecture 11.1 Deep Q-Learning

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Lecture 10.3 ARM & Flows

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Lecture 10.2 ARM & Flows

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Lecture 10.1 ARM & Flows

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Lecture 9.1 Introduction to Reinforcement Learning

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Lecture 9.2 The REINFORCE algorithm

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Lecture 9.3 Gradient Estimation

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Lecture 8.4 Application - query embedding

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Lecture 8.3 Graph Neural Networks

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Lecture 8.2 Graph and node embedding

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Lecture 8.1a Introduction - Graphs

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Lecture 8.1b Introduction - Embeddings

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Lecture 7.2 Implicit models GANs

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Lecture 7.1 Implicit models Density Networks

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Lecture 5.5 ELMo, Word2Vec

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