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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
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mod10lec80-Introduction to Attention based Translation
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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
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mod11lec87-Some ideas to Implement IR-based Conversation Modeling
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mod11lec88-Discussion of some ideas in Question Answering
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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
Description:
COURSE OUTLINE: A major portion of communication now is through text and any organization has more than 90% of its content in the unstructured form. Natural Language Processing (NLP), an important part in Artificial Intelligence, is one of the important technologies that would help in activities such as classification, retrieving and extraction of information, identifying important documents, etc. Students will gather knowledge in the fundamentals of NLP, methods and techniques and gain skills to use them in practical situations

Applied Natural Language Processing

NPTEL
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#Computer Science #Artificial Intelligence #Natural Language Processing (NLP) #Machine Learning #Neural Networks #Mathematics #Statistics & Probability #Probability #Text Analysis #Topic Modeling
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Operations on a Corpus