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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 - 1700-1800
- Thursday - 1500-1600
- Friday - 1600-1700
Instructors
- Arijit Mondal
TAs
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.