CS551: Introduction to Deep Learning
This course will provide a basic understanding of deep learning and how to solve problems from varied domains. Open source tools will be used to demonstrate different applications.
Class timings
- Monday - 1600-1700
- Tuesday - 1700-1800
- Wednesday - 1800-1700
- All classes will be held in MS Teams.
TAs
- Jyoti Kumari
- Sandeep Patel
- Divya Singh
- Surbhi Raj
- Fazail Amin
Syllabus
Brief introduction of big data problem. Overview of linear algebra, probability, numerical computation. Basics of Machine learning/Feature engineering. Neural network. Tutorial for Tools. Deep learning network - Shallow vs Deep network, Deep feedforward network, Gradient based learning - Cost function, soft max, sigmoid function, Hidden unit - ReLU, Logistic sigmoid, hyperbolic tangent Architecture design, SGD, Unsupervised learning - Deep Belief Network, Deep Boltzmann Machine, Factor analysis, Autoencoders. Regularization. Optimization for training deep model. Advanced topics - Convolutional Neural Network, Recurrent Neural Network/ Sequence modeling, LSTM, Reinforcement learning. Practical applications – Vision, speech, NLP, etc.
Books
- Ian Goodfellow, Yoshua Bengio and Aaron Courville, “Deep Learning”, Book in preparation for MIT Press, 2016. (available online)
- Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie, “The elements of statistical learning”, Springer Series in Statistics, 2009.
- Charu C Aggarwal, “Neural Networks and Deep Learning”, Springer.
Slides
Topics | Slides | Annotated Slides |
---|---|---|
Introduction | ||
Linear algebra | ||
Feature engineering | ||
Neural Networks | ||
Deep feed forward networks | ||
Introduction to Keras | NA | |
Back propagation | ||
Regularization | ||
Optimization | ||
CNN | ||
RNN | ||
Practical methodologies | ||
DRL |