Detailed Syllabus and Lectures


Lecture 12: Self-supervised Learning (slides)

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

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Lecture 11: Variational Autoencoders (slides)

motivation for variational autoencoders (VAEs), mechanics of VAEs, separatibility of VAEs, training of VAEs,evaluating representations, vector Quantized Variational Autoencoders (VQ-VAEs)

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Lecture 10: Generative Adversarial Networks, Flow-Based Generative Model (slides)

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

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Lecture 9: Autoencoders and Autoregressive Models (slides)

unsupervised representation learning, sparse coding, autoencoders, autoregressive models

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Lecture 8: Attention and Memory (slides)

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

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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)

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

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

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Lecture 5: Convolutional Neural Networks (slides)

convolution layer, pooling layer, evolution of depth, design guidelines, residual connections, semantic segmentation networks, object detection networks, backpropagation in CNNs

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Lecture 4: Training Deep Neural Networks (slides)

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

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Lecture 3: Multi-layer Perceptrons (slides)

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

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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

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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

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