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Deep Learning
Course Notes
Overview
Course Topics
Fundamentals · CNNs · Generative Models · Hyperparameter Tuning · Project
Lectures
Lecture 1
Introduction & Regression
Neuron, least squares, ridge regression
Lecture 2
Logistic Regression & MLP
Cross-entropy, gradient descent, back-propagation
Lecture 3
Optimization
SGD, Adam, regularization, batch norm, initialization
Lecture 4
Convolutional Neural Networks
Convolution, stride, padding, pooling, LeNet / VGG
Labs
Lab 1
NumPy & Ridge Regression
NumPy essentials, scikit-learn Ridge
Lab 2
PyTorch Introduction
Training loop, tensors, XOR from scratch
Lab 3
MLP in PyTorch
Softmax, dropout, batch norm, Iris & MNIST
Lab 4
CNNs in PyTorch
Conv2d, pooling, MNIST & Fashion-MNIST