1.1 Introduction
1.2 Probablity and NLP
1.3 Vector Space models
1.4 Sequence Learning
1.5 Machine Translation
1.6 Preprocessing
2.1 Incidence Matrix
2.2 Term-Document Binary Incidence Matrix
2.3 IR Using Binary Incidence Matrix
2.4 Term Frequency
2.5 Multiple weighing words TF
2.6 Bag Of Words
2.7 Type Token Ratio
2.8 Inverse Document Frequency
2.9 TF-IDF
2.91 ZIPF'S law
2.92 Heap's Law
3.1.Vector Space Models for NLP
3.2.Document Similarity - Demo, Inverted index, Exercise
3.3 Vector Representation of words
3.4 Contextual understanding of text
3.5 Co-occurence matrix, n-grams
3.6 Collocations, Dense word Vectors
3.7 SVD, Dimensionality reduction, Demo
3.8 Query Processing
3.9 Topic Modeling
4.1 Examples for word prediction
4.2 Introduction to Probability in the context of NLP
4.3 Joint and conditional probabilities, independence with examples
4.4 The definition of probabilistic language model
4.5 Chain rule and Markov assumption
4.6 Generative Models
4.7 Bigram and Trigram Language models -peeking indide the model building
4.8 Out of vocabulary words and curse of dimensionality
4.9 Naive-Bayes, classification
5.1 Machine learning, perceptron, linearly separable
5.2 Linear Models for Claassification
5.3 Biological Neural Network
5.4 Perceptron
5.5 Perceptron Learning
5.6 Logical XOR
5.7 Activation Functions
5.8 Gradient Descent
6.1 Feedforward and Backpropagation Neural Network
6.2 Why Word2Vec?
6.3 What are CBOW and Skip-Gram Models?
6.4 One word learning architecture
6.5 Forward pass for Word2Vec
6.6 Matrix Operations Explained
6.7 CBOW and Skip Gram Models
7.1 Building Skip-gram model using Python
7.2 Reduction of complexity - sub-sampling, negative sampling
7.3 Binay tree, Hierarchical softmax
7.4 Mapping the output layer to Softmax
7.5 Updating the weights using hierarchical softmax
7.6 Discussion on the results obtained from word2vec
7.7 Recap and Introduction
7.8 ANN as a LM and its limitations
7.9 Sequence Learning and its applications
8.1 Introuduction to Recurrent Neural Network
8.2 Unrolled RNN
8.3 RNN - Based Language Model
8.4 BPTT - Forward Pass
8.5 BPTT - Derivatives for W,V and U
8.6 BPTT - Exploding and vanishing gradient
8.7 LSTM
8.8 Truncated BPTT
8.9 GRU
9.1 Introduction and Historical Approaches to Machine Translation
9.2 What is SMT?
9.3 Noisy Channel Model, Bayes Rule, Language Model
9.4 Translation Model, Alignment Variables
9.5 Alignments again!
9.6 IBM Model 1
9.7 IBM Model 2
10.1 Introduction to Phrase-based translation
10.2 Symmetrization of alignments
10.3 Extraction of Phrases
10.4 Learning/estimating the phrase probabilities using another Symmetrization example
10.5 Introduction to evaluation of Machine Translation
10.6 BLEU - "A short Discussion of the seminal paper"
10.7 BLEU Demo using NLTK and other metrics
11.1 Encoder-Decoder model for Neural Machine Translation
11.2 RNN Based Machine Translation
11.3 Recap and Connecting Bloom Taxonomy with Machine Learning
11.4 Introduction to Attention based Translation
11.5 Neural machine translation by jointly learning to align and translate
11.6 Typical NMT architecture architecture and models for multi-language translation
11.7 Beam Search
11.8 Variants of Gradient Descend
12.1 Introduction to Conversation Modeling
12.2 Few examples in Conversation Modeling
12.3 Element IR-based Conversation Modeling
12.4 Ideas on Question Answering
12.5 Applications – Sentiment Analysis, Spam Detection, Resume Mining, AInstein
12.6 Hyperspace Analogue to Language - HAL
12.7 Correlated Occurence Analogue to Lexical Semantic - COALS
12.8 Global Vectors - Glove
12.9 Evaluation of Word vectors
- Preprocessing and Word2Vec :- https://www.kaggle.com/mvanshika/natural-language-processing