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https://github.com/sureshbeekhani/natural-language-processing

This repository provides a comprehensive roadmap for mastering Natural Language Processing (NLP) using various tools and techniques. It covers fundamental text preprocessing methods, advanced neural network architectures, and state-of-the-art models like BERT and Transformers. You'll find implementations
https://github.com/sureshbeekhani/natural-language-processing

nlp nlu text text-processing text-to-speech

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This repository provides a comprehensive roadmap for mastering Natural Language Processing (NLP) using various tools and techniques. It covers fundamental text preprocessing methods, advanced neural network architectures, and state-of-the-art models like BERT and Transformers. You'll find implementations

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README 

        

# Natural Language Processing



### Level 1: Basic Text Preprocessing

Tokenization: Splitting text into words, sentences, or subwords.

Lemmatization: Reducing words to their base or root form.

Stop Words Removal: Removing common words that add little value to the analysis (e.g., "the", "is", "in").



### Level 2: Intermediate Text Preprocessing

Bag of Words (BoW): Representing text by the frequency of words without considering order.

TF-IDF (Term Frequency-Inverse Document Frequency): Measuring the importance of a word in a document relative to a collection of documents.

Unigrams and Bigrams: Considering single words (unigrams) or pairs of consecutive words (bigrams) as features.



### Level 3: Advanced Text Preprocessing

Word Embeddings: Dense vector representations of words capturing their meanings.

Word2Vec: A popular method for creating word embeddings using neural networks.

Average Word2Vec: Averaging word vectors in a document to obtain a single vector representation.



### Text Preprocessing with Libraries

Gensim: A library for topic modeling and document similarity analysis using Word2Vec and other algorithms.

spaCy: A fast and accurate library for NLP tasks, including tokenization, part-of-speech tagging, and named entity recognition.

NLTK (Natural Language Toolkit): A library for working with human language data, offering tools for text processing.



### Understanding Recurrent Neural Networks (RNNs)

RNNs: Neural networks designed to handle sequential data, where outputs depend on previous computations.

LSTM (Long Short-Term Memory): A type of RNN that can learn long-term dependencies and overcome the vanishing gradient problem.

GRU (Gated Recurrent Unit): A simplified version of LSTM with fewer parameters but similar performance.



### Advanced Model Architectures

Bidirectional LSTM: An extension of LSTM that processes data in both forward and backward directions to capture context from both sides.

Encoders and Decoders: Key components in sequence-to-sequence models, where the encoder processes input sequences and the decoder generates output sequences.

Attention Mechanism: A technique allowing models to focus on different parts of the input sequence when generating each part of the output sequence.



### Transformers and BERT

Transformers: A type of neural network architecture that uses self-attention mechanisms to process sequences in parallel, leading to more efficient training.

BERT (Bidirectional Encoder Representations from Transformers): A pre-trained transformer model designed to understand the context of words in all directions, achieving state-of-the-art results on various NLP tasks.



### Implementing Models with PyTorch, Keras, and TensorFlow![Project](https://github.com/SURESHBEEKHANI/Natural-Language-Processing/assets/107859372/7e6337dc-9297-45d8-9bf5-c04e97402ae2)



PyTorch: An open-source deep learning library known for its dynamic computation graph and ease of use.



## Project Structure for the NLP Project