1 Operations on a Corpus
2 Probability and NLP
3 Machine Translation
4 Statistical Properties of Words - Part 01
5 Statistical Properties of Words - Part 02
6 Statistical Properties of Words - Part 03
7 Vector Space Models for NLP
8 Document Similarity - Demo, Inverted index, Exercise
9 Contextual understanding of text
10 Collocations, Dense word Vectors
11 Query Processing
12 Topic Modeling
13 Introduction
14 Sequence Learning
15 Vector Representation of words
16 Co-occurence matrix, n-grams
17 SVD, Dimensionality reduction, Demo
18 Vector Space models
19 Preprocessing
20 Introduction to Probability in the context of NLP
21 Joint and conditional probabilities, independence with examples
22 The definition of probabilistic language model
23 Chain rule and Markov assumption
24 Out of vocabulary words and curse of dimensionality
25 Exercise
26 Examples for word prediction
27 Generative Models
28 Bigram and Trigram Language models -peeking indide the model building
29 Naive-Bayes, classification
30 Machine learning, perceptron, linearly separable
31 Linear Models for Claassification
32 Biological Neural Network
33 Perceptron
34 Perceptron Learning
35 Logical XOR
36 Activation Functions
37 Gradient Descent
38 Feedforward and Backpropagation Neural Network
39 Why Word2Vec?
40 What are CBOW and Skip-Gram Models?
41 One word learning architecture
42 Forward pass for Word2Vec
43 Matrix Operations Explained
44 CBOW and Skip Gram Models
45 Binay tree, Hierarchical softmax
46 Updating the weights using hierarchical softmax
47 Sequence Learning and its applications
48 ANN as a LM and its limitations
49 Discussion on the results obtained from word2vec
50 Recap and Introduction
51 Mapping the output layer to Softmax
52 Reduction of complexity - sub-sampling, negative sampling
53 Building Skip-gram model using Python
54 GRU
55 Truncated BPTT
56 LSTM
57 BPTT - Exploding and vanishing gradient
58 BPTT - Derivatives for W,V and U
59 BPTT - Forward Pass
60 RNN - Based Language Model
61 Unrolled RNN
62 Introuduction to Recurrent Neural Network
63 IBM Model 2
64 IBM Model 1
65 Alignments again!
66 Translation Model, Alignment Variables
67 Noisy Channel Model, Bayes Rule, Language Model
68 What is SMT?
69 Introduction and Historical Approaches to Machine Translation
70 BLEU Demo using NLTK and other metrics
71 BLEU - "A short Discussion of the seminal paper"
72 Introduction to evaluation of Machine Translation
73 Extraction of Phrases
74 Introduction to Phrase-based translation
75 Symmetrization of alignments
76 Learning/estimating the phrase probabilities using another Symmetrization example
77 mod10lec79-Recap and Connecting Bloom Taxonomy with Machine Learning
78 mod10lec80-Introduction to Attention based Translation
79 mod10lec81- Neural machine translation by jointly learning to align and translate
80 mod10lec82-Typical NMT architecture architecture and models for multi-language translation
81 mod10lec77-Encoder-Decoder model for Neural Machine Translation
82 mod10lec78-RNN Based Machine Translation
83 mod10lec83-Beam Search
84 mod10lec84-Variants of Gradient Descend
85 mod11lec85-Introduction to Conversation Modeling
86 mod11lec86-A few examples in Conversation Modeling
87 mod11lec87-Some ideas to Implement IR-based Conversation Modeling
88 mod11lec88-Discussion of some ideas in Question Answering
89 mod12lec89-Hyperspace Analogue to Language - HAL
90 mod12lec90-Correlated Occurence Analogue to Lexical Semantic - COALS
91 mod12lec91-Global Vectors - Glove
92 mod12lec92-Evaluation of Word vectors