Pranesh Shridhar Kulkarni's repositories
Plant-Disease-Detection-Using-Digital-Image-Processing
The plant disease detection system with efficient image segmentation and feature extraction algorithms and statistical models.
Satellite-Field-Monitoring-Using-Earth-Engine-and-machine-learningV2
This Python code will analyse the quality of field from 6 years data of MSAVI, NDVI, NDWI indices. We have designed Earth Engine Application to download spectrum data in the format of csv. Python code snippet will import that data and will calculate field parameters and it will predict the quality of field. For that we have implemented K-means clustering algorithm.
Restaurent_management_System_Using_Mysql
Restaurent_management_System_Using_MySQL -- Python tkinter-- billing calculator -- email reciept --pdf of reciept -- export data to excel --Loginactivity
ai-fundamentals
Code samples for AI fundamentals
DehazeNet-keras
DehazeNet keras+tensorflow version
E_tax_with_MySQL
Online taxation software.
Flipr-hackathon6_learningmachine
Flipr hackathon 6.0 Submission Atharva Karwande Pranesh Kulkarni (Learning Machine)
Multispectral-Image-Compression-Using-Convolutional-Autoencoder
Official Repository of the Paper: Multispectral Image Compression Using Convolutional Autoencoder: A Comparative Analysis
coding-interview-university
A complete computer science study plan to become a software engineer.
Coursera_Capstone
IBM Data science Capstone project
DehazeNet_Pytorch
A Pytorch implementation for DehazeNet in paper 'DehazeNet: An End-to-End System for Single Image Haze Removal'
Flipr_hackathon_5
Flipr_hackathon_5
Image-Dehazing-Using-Residual-Based-Deep-CNN
Implement Image Dehazing Using Residual-Based Deep CNN paper with added refinements from Dehazenet
jupyter-text2code
A proof-of-concept jupyter extension which converts english queries into relevant python code
Learn_Machine_Learning
My Road to Machine Learning
Stock-Market-Prediction-using-Numerical-and-Textual-Analysis
A hybrid model for stock price/performance prediction using numerical analysis of historical stock prices, and sentimental analysis of news headlines