BHafsa

deep-nilmtk-v1

Deep-NILMtk is an open source package designed specifically for deep models applied to solve NILM. It implements the general NILM pipeline independently of the deep learning backend. In its current version the toolkit considers two of the most popular deep learning pipelines. The training and testing phases are fully compatible with NILMtk. Several

image-nilm

FL-NILM

The current notebook implements a simple disaggregator for deep-nilmtk models compatible with NILMtk.

DNN-NILM-template

The current repository offer a ready to use project for easy design of experiments based on

nilm-dashboard

nilmtk

Non-Intrusive Load Monitoring Toolkit (nilmtk)

AI4DLearning

Learning python ecosystem and it is appliacation in AI4D

Awasome-material-for-grant-writing

The current repo contains a collection of university materials, blogs and books providing guidance for writing funding proposal

AWS-Image-classification

AWS-Kaggle-Bike-Sharing

BHafsa

bootstrap

The most popular HTML, CSS, and JavaScript framework for developing responsive, mobile first projects on the web.

Congestive-Heart-Failure-Detection

A normal and congestive heart failure classifier based on the long-term ECG time series.

Disaster-msg-clf

Dog-breed-classification

dz-hospitals

federated-submodel-averaging

FedMatch

This repository is an official Tensorflow 2 implementation of Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning

FL

Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data

generative-ai

18 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/

github-custom-component-tutorial

Custom component for Home Assistant using GitHub's API.

IBM-watson-recommender

lag-llama

llm.c

LLM training in simple, raw C/CUDA

python-getting-started

Getting Started with Python on Heroku.

QUD-dataset

Qatar university dataset (QUD) is an open access repository, which includes micro-moments power consumption footprints of different appliances. It is collected at Qatar university energy lab. In the initial version of QUD, power usage footprints have been gathered for a period of more than 3 months until now. The collection campaign is still ongoi

Stay-Point-Detection

All materials you need for stay point detection and trajectory analysis: blogs, videos, papers, and softwares, etc.

STEG-Fraud-Detection

tensorflow

An Open Source Machine Learning Framework for Everyone

tensorflow-deep-learning

All course materials for the Zero to Mastery Deep Learning with TensorFlow course.