This repo is purely inspired from Andrew Ng's Coursera course called Deep learning specialization
In this data world, sequence data is one of the challenging thing to get processed. But fortunately Deep neural networks helps us to process this data and ease the way to get valuable results.
Repository Author Info - G DHAMODHARAN, MSc Computer Science, Anna University.
NLP has a wide range of applications like,
* Speech Recognition
* Dialog Systems
* Sentiment Analysis
* Machine Translation
* Video Activity Recognition
* Name Entity Recognition
* Creation of Question and Answers with Distraction for MCQ based tests
* and Many more...
To learn NLP, We must master RNN - Recurrent Neural Networks. I designed this repo something like this( Thanks to Standford CheatSheet )
* Architecture structure
* Applications of RNNs
* Loss function
* Backpropagation
* Common activation functions
* Vanishing/exploding gradient
* Gradient clipping
* GRU/LSTM
* Types of gates
* Bidirectional RNN
* Deep RNN
* Notations
* Embedding matrix
* Word2vec
* Skip-gram
* Negative sampling
* GloVe
* Cosine similarity
* t-SNE
* Language model
* n-gram
* Perplexity
* Beam search
* Length normalization
* Error analysis
* Bleu score
* Attention model
* Attention weights