STAT479: Deep Learning (Spring 2019)

Instructor: Sebastian Raschka

Lecture material for the STAT 479 Deep Learning course at University Wisconsin-Madison. For details, please see the course website at http://pages.stat.wisc.edu/~sraschka/teaching/stat479-ss2019/

Course Calendar

History of neural networks and what makes deep learning different from “classic machine learning”
Introduction to the concept of neural networks by connecting it to familiar concepts such as logistic regression and multinomial logistic regression (which can be seen as special cases: single-layer neural nets)
Modeling and deriving non-convex loss function through computation graphs
Introduction to automatic differentiation and PyTorch for efficient data manipulation using GPUs
Convolutional neural networks for image analysis
1D convolutions for sequence analysis
Sequence analysis with recurrent neural networks
Generative models to sample from input distributions
- Autoencoders
- Variational autoencoders
- Generative Adversarial Networks

L01: What are Machine Learning and Deep Learning? An Overview. [Slides]
L02: A Brief Summary of the History of Neural Networks and Deep Learning. [Slides]
L03: The Perceptron. [Slides] [Code]
L04: Linear Algebra for Deep Learning. [Slides]
L05: Fitting Neurons with Gradient Descent. [Slides] [Code]
L06: Automatic Differentiation with PyTorch. [Slides] [Code]
L07: Cloud Computing. [Slides]
L08: Logistic Regression and Multi-class Classification [Slides] [Code]
L09: Multilayer Perceptrons [Slides] [Code]
L10: Regularization [Slides] [Code]
L11: Normalization and Weight Initialization [Slides]
L12: Learning Rates and Optimization Algorithms [Slides]
L13: Introduction to Convolutional Neural Networks [Slides (part 1)] [Slides (part 2)] [Slides (part 3)]
L14: Introduction to Recurrent Neural Networks [Slides (part 1) Slides (part 2)]
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