Soumya Prabha Maiti's repositories
image-segmentation-web-app
A web app that segments the image of a pet animal into three regions - foreground (pet), background and boundary. It uses a U-Net model trained on Oxford-IIIT Pet Dataset and is deployed using Gradio.
hate-speech-classification
A project to classify the input text as hate speech or not using an LSTM model trained on the Hate Speech and Offensive Language dataset and Twitter hate speech dataset from Kaggle.
ai-blog-writer
A web app that generates a blog using AI, for a target audience within a given number of words. Currently uses Llama2 7B Chat GGML model.
algorithms_and_data_structures
180+ Algorithm & Data Structure Problems using C++
awesome
😎 Awesome lists about all kinds of interesting topics
Codechef_Hacktoberfest-2020
A beginner-friendly repository made specifically for open source beginner. Here, you can solve Codechef Long Challenge problems for PR's in Hacktoberfest.
Data-Structures-and-Algorithms
Collection of Data Structures and Algorithms problems
DataStructures-Algorithms
The best library for implementation of all Data Structures and Algorithms - Trees + Graph Algorithms too!
Flask-blog
A blog application built using Flask, SQLite and Bootstrap. It has features like user registration, login, logout, create, update and delete posts, pagination, password reset, etc.
tomato-leaf-disease-classification
A project to detect the disease from the image of a tomato leaf using a CNN architecture.
design-patterns-python
Common design patterns implemented in Python
how-to-contribute-to-open-source
A guide to contributing to open source
manim
Animation engine for explanatory math videos
Pygame-Tutorials
This is the code base for the pygame tutorials posted on my YouTube channel.
sci-cpy
Scientific Computation Methods in C and Python (Open for Hacktoberfest 2021)
soumya-prabha-maiti
Config files for my GitHub profile.
system-design-notebook
Learn System Design step by step
SystemSoftware
System software assignments
text-emotion-classifier
A project to classify the emotion of a given text into one of the following categories: anger, fear, joy, love, sadness, surprise. The model is built using the BERT architecture and is trained on the Emotion Dataset from Kaggle.