megatazm

megatazm's Stars

• drshahizan/SLR-MIIT

  1618

• smartcontractkit/full-blockchain-solidity-course-js

  Learn Blockchain, Solidity, and Full Stack Web3 Development with Javascript

  12.3k3k

• MoralisWeb3/youtube-tutorials

  Language:C#6281.4k

• HashLips/nft_game

  Learn how to create a NFT game and connect to a smart contract.

  Language:JavaScript240137

• ivan-liljeqvist/tutorials-2022

  Language:HTML2545

• hanshaoling/AC295_TeamAnonymous

  Submission for Team Anonymous

  Language:Jupyter Notebook1

• octanove/expats

  EXPATS: A Toolkit for Explainable Automated Text Scoring

  Language:Python214

• Gaurav-Pande/AES

  Machine Learning and Deep learning approaches for automated essay scoring

  Language:Jupyter Notebook3

• kadanes/NLP-Project-AES

  Course project for CS 7650 NLP (Georiga Tech)

  Language:Jupyter Notebook31

• mayurmorin/Automated-Essay--Scoring

  Automatically Score essays using Deep Learning

  Language:JavaScript1419

• Gaurav-Pande/AES_DL

  Automated Essay Scoring using BERT

  Language:Jupyter Notebook4515

• luca-office/Publications

  Language:Jupyter Notebook104

• keyreply/Malay-NLP-Dataset

  A collection of NLP resources for Malay

  246

• huseinzol05/Bahasa-NLP-Tensorflow

  Gathers Tensorflow deep learning models for Bahasa Malaysia NLP problems

  Language:Jupyter Notebook2814

• Farahn/AES

  Automatic Essay Scoring

  Language:Jupyter Notebook3412

• JanSchm/CapMarket

  Language:Jupyter Notebook278170

• microsoft/iot-curriculum

  Hands on labs and content for students and educators to learn and teach the Internet of Things at schools, universities, coding clubs, community colleges and bootcamps

  Language:JavaScript16152

• IBM/smart-city-analytics

  Analyze large data sets collected from a long-range IoT system that uses LoRaWAN networking

  Language:Jupyter Notebook3523

• facebookresearch/suncet

  Code to reproduce the results in the FAIR research papers "Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples" https://arxiv.org/abs/2104.13963 and "Supervision Accelerates Pre-training in Contrastive Semi-Supervised Learning of Visual Representations" https://arxiv.org/abs/2006.10803

  Language:Python48667

• facebookresearch/dino

  PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO

  Language:Python6.2k905

• vliu15/gaugan

  Pytorch implementation of GauGAN, from https://arxiv.org/abs/1903.07291 (Park et al. 2019)

  Language:Python131

• fperazzi-zz/proSR

  Repository containing an independent implementation of the paper: "A Fully Progressive Approach to Single-Image Super-Resolution"

  Language:Python929151

• yulunzhang/RCAN

  PyTorch code for our ECCV 2018 paper "Image Super-Resolution Using Very Deep Residual Channel Attention Networks"

  Language:Python1.4k314

• ptkin/Awesome-Super-Resolution

  A curated list of awesome super-resolution resources.

  Language:HTML36970

• zeyuxiao1997/EDPN

  Language:Python807

• sanghyun-son/EDSR-PyTorch

  PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)

  Language:Python2.4k669

• MinecraftDawn/superResolution

  Language:Python11

• wwlCape/HAN

  PyTorch code for our ECCV 2020 paper "Single Image Super-Resolution via a Holistic Attention Network"

  Language:Python16732

• eriklindernoren/PyTorch-GAN

  PyTorch implementations of Generative Adversarial Networks.

  Language:Python16.3k4.1k

• aladdinpersson/Machine-Learning-Collection

  A resource for learning about Machine learning & Deep Learning

  Language:Python7.5k2.7k