BHafsa

BHafsa's Stars

• huggingface/transformers

  🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

  Language:Python134k1.1k16k26.8k

• karpathy/llm.c

  LLM training in simple, raw C/CUDA

  Language:Cuda24.3k2411392.7k

• onnx/onnx

  Open standard for machine learning interoperability

  Language:Python17.9k4372.8k3.7k

• janishar/mit-deep-learning-book-pdf

  MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville

  Language:Java12.9k409182.7k

• iamtrask/Grokking-Deep-Learning

  this repository accompanies the book "Grokking Deep Learning"

  Language:Jupyter Notebook7.4k321501.6k

• Atcold/NYU-DLSP20

  NYU Deep Learning Spring 2020

  Language:Jupyter Notebook6.7k134882.2k

• secdr/research-method

  论文写作与资料分享

  2.4k532557

• creativetimofficial/material-dashboard-angular2

  Material Dashboard Angular

  Language:SCSS1k541192.1k

• google-deepmind/neural-processes

  This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CNPs), Neural Processes (NPs), Attentive Neural Processes (ANPs).

  Language:Jupyter Notebook985429149

• Tiramisu-Compiler/tiramisu

  A polyhedral compiler for expressing fast and portable data parallel algorithms

  Language:C++9174564132

• 1millionwomentotech/toolkitten

  A toolkit for #1millionwomentotech community.

  Language:Jupyter Notebook62227731648

• paperswithcode/axcell

  Tools for extracting tables and results from Machine Learning papers

  Language:Python396131755

• tzano/wren

  Wren enables users to discover and explore daily news stories 🗞️📻 📺

  Language:Python26020619

• ch-shin/awesome-nilm

  A curated resources of awesome NILM resources

  19018145

• nilmtk/nilmtk-contrib

  Language:Python116106859

• tzano/fountain

  Natural Language Data Augmentation Tool for Conversational Systems

  Language:Python116709

• georgezoto/TensorFlow-in-Practice

  TensorFlow in Practice Specialization. Join our Deep Learning Adventures community 🎉 and become an expert in Deep Learning, TensorFlow, Computer Vision, Convolutional Neural Networks, Kaggle Challenges, Data Augmentation and Dropouts Transfer Learning, Multiclass Classifications and Overfitting and Natural Language Processing NLP as well as Time Series Forecasting 😀 All while having fun learning and participating in our Deep Learning Trivia games 🎉 http://bit.ly/deep-learning-tf

  Language:Jupyter Notebook678026

• klemenjak/SynD

  A Synthetic Energy Consumption Dataset for Non-Intrusive Load Monitoring

  27508

• areinhardt/antgen

  AMBAL-based NILM Trace generator

  Language:Python18307

• amine0110/medical-visualization-with-streamlit

  Language:Python12114

• YassineHimeur/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 ongoing in order to cover a period of one year and other appliances. The testbeds used to glean the data are described more thoroughly in the in the paper: Y. Himeur, A. Alsalemi, F. Bensaali, A. Amira, Building power consumption datasets: Survey, taxonomy and future directions, Energy & Buildings, 2020. (Submitted) Those wishing to use the dataset in academic work should cite this paper as the reference. QUD_app-1.csv: this file includes the different kinds of data collected during the measurement campaign: Column 1: Date Column 2: Time Column 3: appID Column 4: occupancy pattern Column 5: Power consumption Column 6: Normalized power Column 7: Quantified power Column 8: Micro-moment class

  Language:Jupyter Notebook10315

• FedGBM/FedGBM-NILM

  Language:Python7202

• sjtu-yc/federated-submodel-averaging

  Language:Python7101

• sambaiga/AI4DLearning

  Learning python ecosystem and it is appliacation in AI4D

  Language:Jupyter Notebook2302

• BHafsa/Module-Note

  Language:PHP1101

• GraphCompressionProject/Pro

  Language:C++110

• klerings/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

  Language:Jupyter Notebook1001