NOC:Applied Natural Language Processing


Lecture 1 - Introduction


Lecture 2 - Operations on a Corpus


Lecture 3 - Probability and NLP


Lecture 4 - Vector Space models


Lecture 5 - Sequence Learning


Lecture 6 - Machine Translation


Lecture 7 - Preprocessing


Lecture 8 - Statistical Properties of Words - Part 1


Lecture 9 - Statistical Properties of Words - Part 2


Lecture 10 - Statistical Properties of Words - Part 3


Lecture 11 - Vector Space Models for NLP


Lecture 12 - Document Similarity - Demo, Inverted index, Exercise


Lecture 13 - Vector Representation of words


Lecture 14 - Contextual understanding of text


Lecture 15 - Co-occurence matrix, n-grams


Lecture 16 - Collocations, Dense word Vectors


Lecture 17 - SVD, Dimensionality reduction, Demo


Lecture 18 - Query Processing


Lecture 19 - Topic Modeling


Lecture 20 - Examples for word prediction


Lecture 21 - Introduction to Probability in the context of NLP


Lecture 22 - Joint and conditional probabilities, independence with examples


Lecture 23 - The definition of probabilistic language model


Lecture 24 - Chain rule and Markov assumption


Lecture 25 - Generative Models


Lecture 26 - Bigram and Trigram Language models - peeking indide the model building


Lecture 27 - Out of vocabulary words and curse of dimensionality


Lecture 28 - Exercise


Lecture 29 - Naive-Bayes, classification


Lecture 30 - Machine learning, perceptron, linearly separable


Lecture 31 - Linear Models for Claassification


Lecture 32 - Biological Neural Network


Lecture 33 - Perceptron


Lecture 34 - Perceptron Learning


Lecture 35 - Logical XOR


Lecture 36 - Activation Functions


Lecture 37 - Gradient Descent


Lecture 38 - Feedforward and Backpropagation Neural Network


Lecture 39 - Why Word2Vec?


Lecture 40 - What are CBOW and Skip-Gram Models?


Lecture 41 - One word learning architecture


Lecture 42 - Forward pass for Word2Vec


Lecture 43 - Matrix Operations Explained


Lecture 44 - CBOW and Skip Gram Models


Lecture 45 - Building Skip-gram model using Python


Lecture 46 - Reduction of complexity - sub-sampling, negative sampling


Lecture 47 - Binay tree, Hierarchical softmax


Lecture 48 - Mapping the output layer to Softmax


Lecture 49 - Updating the weights using hierarchical softmax


Lecture 50 - Discussion on the results obtained from word2vec


Lecture 51 - Recap and Introduction


Lecture 52 - ANN as a LM and its limitations


Lecture 53 - Sequence Learning and its applications


Lecture 54 - Introuduction to Recurrent Neural Network


Lecture 55 - Unrolled RNN


Lecture 56 - RNN - Based Language Model


Lecture 57 - BPTT - Forward Pass


Lecture 58 - BPTT - Derivatives for W,V and U


Lecture 59 - BPTT - Exploding and vanishing gradient


Lecture 60 - LSTM


Lecture 61 - Truncated BPTT


Lecture 62 - GRU


Lecture 63 - Introduction and Historical Approaches to Machine Translation


Lecture 64 - What is SMT?


Lecture 65 - Noisy Channel Model, Bayes Rule, Language Model


Lecture 66 - Translation Model, Alignment Variables


Lecture 67 - Alignments again!


Lecture 68 - IBM Model 1


Lecture 69 - IBM Model 2


Lecture 70 - Introduction to Phrase-based translation


Lecture 71 - Symmetrization of alignments


Lecture 72 - Extraction of Phrases


Lecture 73 - Learning/estimating the phrase probabilities using another Symmetrization example


Lecture 74 - Introduction to evaluation of Machine Translation


Lecture 75 - BLEU - A short Discussion of the seminal paper


Lecture 76 - BLEU Demo using NLTK and other Metrics


Lecture 77 - Encoder-Decoder model for Neural Machine Translation


Lecture 78 - RNN Based Machine Translation


Lecture 79 - Recap and Connecting Bloom Taxonomy with Machine Learning


Lecture 80 - Introduction to Attention based Translation


Lecture 81 - Research Paper discussion on Neural machine translation by jointly learning to align and translate


Lecture 82 - Typical NMT architecture architecture and models for multi-language translation


Lecture 83 - Beam Search, Stochatic Gradient Descend, Mini Batch, Batch


Lecture 84 - Beam Search, Stochatic Gradient Descend, Mini Batch, Batch


Lecture 85 - Introduction to Conversation Modeling


Lecture 86 - A few examples in Conversation Modeling


Lecture 87 - Some ideas to Implement IR-based Conversation Modeling


Lecture 88 - Discussion of some ideas in Question Answering


Lecture 89 - Hyperspace Analogue to Language - HAL


Lecture 90 - Correlated Occurence Analogue to Lexical Semantic - COALS


Lecture 91 - Global Vectors - Glove


Lecture 92 - Evaluation of Word vectors