Assignments of the course CSE 556 - Natural Language Processing

Methods for numerical differentiation of noisy data in python

Identifying the boundaries of main content of fiction and non-fiction works in the HathiTrust Extracted Features dataset.

라플라시안 스무딩, 타우빈 스무딩, 코탄젠트 스무딩을 OpenGL을 통해 구현하였다.

Developed N-gram based and LSTM based Language Models for various channels of social media

Numerical optimization via mollifier smoothing

OpenCV, apply 'Homogeneous', 'Gaussian' filter on image.

This stores the CS2731 course projects in the Natural Language Processing class.

Developing a novel technique to automatically smooth temperature data

NGram with basic smoothing

Created by Mehmet Zahid Genç

Functions for conducting regression estimation on nonsmooth data

An NLP project leveraging character trigrams and smoothing techniques (Lidstone, Linear Discounting, Absolute Discounting) for language identification. Trained on for Spanish, Italian, English, French, Dutch, and German, achieving 99.8932% accuracy. Includes datasets, model parameters, and comprehensive documentation.

Ngrams with Basic Smoothings

Ngrams with basic smoothing.

SUPERVISED LEARNING: REGRESSION: Linear - Polynomial - Ridge/Lasso CLASSIFICATION: K-NN - Naïve Bayes - Decision Tree - Logistic Regression - Confusion Matrix - SVM TIME SERIES ANALYSIS: Linear & Logistic Regr. - Autoregressive Model - ARIMA - Naïve - Smoothing Technique UNSUPERVISED LEARNING: CLUSTERING: K-Means - Agglomerative - Mean-Shift - F…

PRESS: Predictive State Smoothing in Python (tf.keras)