Sukesh Sangam's repositories
Deep-Learning-Model-for-Hybrid-Recommendation-Engine
A Hybrid recommendation engine built on deep learning architecture, which has the potential to combine content-based and collaborative filtering recommendation mechanisms using a deep learning supervisor
awesome-machine-learning-interpretability
A curated list of awesome machine learning interpretability resources.
Bayesian_Optimization
Exploring Bayesian Optimization using GPyOpt Library (http://sheffieldml.github.io/GPyOpt/)
CATMAID
Collaborative Annotation Toolkit for Massive Amounts of Image Data
Connect-4-game
Connect 4 game implemented Using Min Max Algorithm
CS-518-Web-Programming-
Repository for CS 518 Web Programming course project LAMP stack
Genetic-Algorithm-
This repository consists of a hill climbing algorithm and differential evolution algorithm for optimization of a function and application of Genetic algorithm for solving travels man sales problem.
pattern-recognition-neural-networks
Pattern recognition using MLP neural network classifier
TimeSeries-StockPrice-Forecasting
This repository demonstrated the proof of concept of predicting the future commodity price
Data-Engineer-Nano-Degree
Data models, build data warehouses and data lakes, automate data pipelines, and worked with massive datasets.
Diabetes_Prediction_using_Keras
This Repository consists of Diabetes prediction model built using Keras Sequential Dense Model
Keras_Machine_Learning
Machine Learning Models using Keras
models
Models and examples built with TensorFlow
project-template
This is a sample repository for ODU summer workshop program
Python
All Algorithms implemented in Python
Python-Web-Crawlers
This python web crawlers are used to extract data from the http://www.imdb.com
render
Render transformed image tiles
Sokoban-game-solver
Sokoban game solver using BFS, DFS , Greedy and A* algorithms
spark-knn-recommender
Item and User-based KNN recommendation algorithms using PySpark
Sudoku-game-solver
This repository consists of the sudoku game solver. using Naïve Backtracking Algorithm and Smart Backtracking Algorithm (using minimum remaining values strategy)