======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  |  {{ :courses:2022:cs551:01_intro_to_dl.pdf |pdf}}  |  {{ :courses:2022:cs551:01_intro_to_dl_ann.pdf | pdf}}  | 
|  Linear algebra   |  {{ :courses:2022:cs551:02_linear_algebra.pdf|pdf }}  |  {{ :courses:2022:cs551:02_linear_algebra_ann.pdf|pdf}}  |  
|  Feature engineering   |  {{ :courses:2022:cs551:03_feature_engg.pdf|pdf }}  |  {{ :courses:2022:cs551:03_feature_engg_ann.pdf|pdf}}  |
|  Neural Networks   |  {{ :courses:2022:cs551:04_neural_network.pdf|pdf }}  |  {{ :courses:2022:cs551:04_neural_network_ann.pdf|pdf}}  |  
|  Deep feed forward networks   |  {{ :courses:2022:cs551:05_dl_network.pdf|pdf }}  |  {{ :courses:2022:cs551:05_dl_network_ann.pdf|pdf}}  |
|  Introduction to Keras   |  {{ :courses:2022:cs551:keras.pdf|pdf }}  |  NA  |
|  Back propagation   |  {{ :courses:2022:cs551:05_dl_network_02_bp.pdf|pdf }}  |  {{ :courses:2022:cs551:05_dl_network_02_bp_ann.pdf|pdf}}  |
|  Regularization   |  {{ :courses:2022:cs551:06_regularization.pdf|pdf }}  |  {{ :courses:2022:cs551:06_regularization_ann.pdf|pdf}}  |
|  Optimization   |  {{ :courses:2022:cs551:07_optimization.pdf|pdf }}  |  {{ :courses:2022:cs551:07_optimization_ann.pdf|pdf}}  |
|  CNN   |  {{ :courses:2022:cs551:08_cnn.pdf|pdf }}  |  {{ :courses:2022:cs551:08_cnn_ann.pdf|pdf}}  |
|   RNN    |  {{ :courses:2022:cs551:09_rnn.pdf|pdf }}  |  {{ :courses:2022:cs551:09_rnn_ann.pdf|pdf}}  |
|  Practical methodologies   |  {{ :courses:2022:cs551:10_practical_methods.pdf|pdf }}  |  {{ :courses:2022:cs551:10_practical_methods_ann.pdf|pdf}}  |
|  DRL   |  {{ :courses:2022:cs551:11_drl.pdf|pdf }}  |  {{ :courses:2022:cs551:11_drl_ann.pdf|pdf}}  |