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Lectures

1. Introduction to Neural Networks
2. Lecture Notes on Perceptrons and Feedforward Neural Networks
3. Lecture Notes on Vector Spaces and Subspaces
4. Lecture Notes on Analytic Geometry, Norms, and Inner Products
5. Lecture Notes on the Perceptron Algorithm
6. Lecture Notes on Multilayer Perceptrons and Backpropagation
7. Lecture Notes on Eigenvalues, Eigenvectors, and Matrix Decompositions
8. Lecture Notes on Convolutional Neural Networks
9. Lecture Notes on Word Embeddings and Recurrent Neural Networks
10. Lecture Notes on Sequence-to-Sequence Learning and Transformers
11. Deep Reinforcement Learning: Foundations and Frontiers
12. Lecture Notes on Graph Representation Learning