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

Home Page: http://pages.stat.wisc.edu/~sraschka/teaching/stat479-ss2019/

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stat479-deep-learning-ss19's Introduction

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]
  • ...

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