Coursera-DeepLearning.AI-Natural-Language-Processing-Specialization and Natural-Language-Processing-Specialization

These are competitors offering duplicate coursework solutions for the same Coursera NLP specialization, where users would select one based on code quality, documentation completeness, or community engagement (reflected in the significant star count difference).

Maintenance 0/25
Adoption 9/25
Maturity 16/25
Community 22/25
Maintenance 0/25
Adoption 10/25
Maturity 8/25
Community 25/25
Stars: 82
Forks: 49
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
Stars: 854
Forks: 699
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License:
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About Coursera-DeepLearning.AI-Natural-Language-Processing-Specialization

shantanu1109/Coursera-DeepLearning.AI-Natural-Language-Processing-Specialization

This Repository Contains Solution to the Assignments of the Natural Language Processing Specialization from Deeplearning.ai on Coursera Taught by Younes Bensouda Mourri, Łukasz Kaiser, Eddy Shyu

About Natural-Language-Processing-Specialization

amanjeetsahu/Natural-Language-Processing-Specialization

This repo contains my coursework, assignments, and Slides for Natural Language Processing Specialization by deeplearning.ai on Coursera

Covers four courses implementing classical to modern NLP architectures: sentiment analysis via logistic regression and naive Bayes, vector space models with PCA, sequence modeling with GRUs and LSTMs for tasks like named entity recognition and language generation, and transformer-based approaches including encoder-decoder attention for machine translation, T5/BERT for question-answering, and reformer models for dialogue systems. Implementations progress from foundational algorithms like minimum edit distance and n-gram language models through Word2Vec training to advanced techniques like Siamese networks for semantic similarity and attention mechanisms.

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