qlwang25/stat479-deep-learning-ss19

Course material for STAT 479: Deep Learning (SS 2019) at University Wisconsin-Madison

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

Please see http://pages.stat.wisc.edu/~sraschka/teaching/stat479-ss2019/#calendar.

Topic Outline

• 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

Material

• 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]
• ...