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Getting ready
Course Information
Lectures
Lecture 1: Markov Models
Lecture 2: Applications of Markov Models and Text Preprocessing
Lecture 3: Introduction to Hidden Markov Models (HMMs)
Lecture 4: Decoding and Learning in HMMs
Lecture 5: Topic Modeling
Lecture 6: Introduction to Recurrent Neural Networks (RNNs)
Lecture 7: Introduction to self-attention and transformers
Lecture 8: More transformers
Class demos
Class Demo: Recipe generator
Appendices
PageRank as a Markov model
HMM supervised POS tagging
Baum-Welch (BW) algorithm
LDA details
AppendixD: More RNNs, LSTMs
Attribution
Attributions
Index
