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