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Week 1
Introduction to Deep Learning
Course overview, what deep learning is, and where it is used
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Week 2
Basics of Neural Networks
Artificial neurons, the perceptron, Adaline and gradient descent
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Week 3
PyTorch for Deep Learning
Tensors, autograd, and a fully-connected network on MNIST
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Week 4
Deep Convolutional Neural Networks
Locality and weight sharing, the convolution operation, and CNNs in PyTorch
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Week 6
Recurrent Neural Networks
Sequence data, the recurrent layer, LSTM and GRU, and an RNN in PyTorch
Week 7
Transformers
Query, key and value; the original architecture, and what modern transformers changed
Week 7
Transformer v2: The Illustrated Transformer
Self-attention by hand, many heads, the decoder and training — after Jay Alammar’s The Illustrated Transformer
Week 9
Generative Models and GANs
Autoencoders, VAEs, the adversarial game — and how diffusion took over
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Week 11
Deep Reinforcement Learning
Learning to act from a score: returns, values, Q-learning and deep Q-networks