Welcome to my repository dedicated to Natural Language Processing (NLP) projects. NLP is a fascinating area that strives to enable machines to understand, interpret, and produce human languages. In this repository, you'll find a collection of projects that showcase various techniques and algorithms commonly used in the NLP domain.
N-Gram Model: This project delves into the world of n-grams, a fundamental concept in statistical language modeling. N-grams are continuous sequences of n items (typically words) from a given sample of text. By studying these sequences, we can make predictions about language and analyze the structure of sentences. Please refer to mtg.py.
Gradient Descent: Gradient descent is a first-order iterative optimization algorithm used to find the minimum of a function. In the context of NLP, it can be crucial for tasks like neural network training where we adjust parameters to minimize error. This project explores the implementation and nuances of gradient descent in NLP applications. Please refer to unigram_pytorch.py.
Viterbi Algorithm: The Viterbi algorithm is a dynamic programming algorithm used for finding the most likely sequence of states. In the realm of NLP, it's commonly applied in tasks like part-of-speech tagging and hidden Markov models. Dive into this project to understand its working and applications. Please refer to viterbi.py.
This repository is a continuous work in progress. Over time, I'll be adding more projects exploring various other aspects of Natural Language Processing. So, keep an eye out for new additions and feel free to contribute or suggest topics!