jaseemck Goto Github PK

-hacktoberfest2k20

Add all sorts of application for hacktoberfest.

100-days-of-code

Fork this template for the 100 days journal - to keep yourself accountable (multiple languages available)

100daysofcodingchallenge

algo_ds_notes

It is a repository that is a collection of algorithms and data structures with implementation in various languages.

analyzing-chinook-database

This project is on analyzing the chinook database using advanced queries

analyzing-cia-factbook

This project is on analyzing CIA Factbook using SQL Queries

analyzing-employee-exit-surveys

This project focuses on cleaning and analyzing employee exit survey dataset.

anapioficeandfire

An API of Ice And Fire is the world's greatest source for quantified and structured data from the universe of Ice and Fire (as well as the HBO series Game of Thrones).

anapioficeandfire-python

A Python helper library for https://anapioficeandfire.com

anomaly_detection_app

EDA and modeling of Anomaly Detection in IoT devices | Federated Learning | Kaggle IoT Dataset | KDD cup

appointmentfrontend

An appointment app

arduinosmartstick

A smart stick that can help the blind walk without others help.

attack-and-anomaly-detection-in-iot-sensors-in-iot-sites-using-machine-learning-approaches

Attack and Anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing commensurately. Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better.

awesome-federated-learning

list of resources for federated learning and privacy in machine learning

awesome-project-ideas

Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas

awesome-public-datasets

A topic-centric list of HQ open datasets. PR ☛☛☛

badminton_queuing_system

Player queuing system for badminton club

catalogscript

code-jam

Solution of Code Jam Problem will be here

codeheat.org

Codeheat Coding Contest Website https://codeheat.org

computervision-with-pytorch-learning-program

Computer Vision using PyTorch Learning Program by TinkerHub Foundation

crowd-counting-using-tensorflow

Tensorflow implementation of crowd counting using CNNs from overhead surveillance cameras.

ctfdy20

datastructures-algorithms

deep-learning-v2-pytorch

Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101

dehazer

demo-self-driving

Streamlit app demonstrating an image browser for the Udacity self-driving-car dataset with realtime object detection using YOLO.

developer_jaathakam

Github Profile Comparison Application

devtraining-needit-paris

This repository is used by the developer site training content, Paris release. It is used for the Build the NeedIt App, Scripting in ServiceNow, Application Security, Importing Data, Automating Application Logic, Flow Designer, REST Integrations, Reporting and Analytics, Domain Separation, Mobile Applications, and Context-sensitive Help courses.

digit-classifier

Wrote a neural network that uses fundamental DL algorithms to identify handwritten digits from MNIST dataset.