7236 Repositories
Python Training-a-deep-learning-model-on-the-noisy-CIFAR-dataset Libraries
This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.
An-Introduction-to-Statistical-Learning This repository contains the exercises and its solution contained in the book An Introduction to Statistical L
Notebooks for my "Deep Learning with TensorFlow 2 and Keras" course
Deep Learning with TensorFlow 2 and Keras – Notebooks This project accompanies my Deep Learning with TensorFlow 2 and Keras trainings. It contains the
🎁 3,000,000+ Unsplash images made available for research and machine learning
The Unsplash Dataset The Unsplash Dataset is made up of over 250,000+ contributing global photographers and data sourced from hundreds of millions of
Machine Learning Course with Python:
A Machine Learning Course with Python Table of Contents Download Free Deep Learning Resource Guide Slack Group Introduction Motivation Machine Learnin
Deep Learning and Reinforcement Learning Library for Scientists and Engineers 🔥
TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extens
Trax — Deep Learning with Clear Code and Speed
Trax — Deep Learning with Clear Code and Speed Trax is an end-to-end library for deep learning that focuses on clear code and speed. It is actively us
An educational resource to help anyone learn deep reinforcement learning.
Status: Maintenance (expect bug fixes and minor updates) Welcome to Spinning Up in Deep RL! This is an educational resource produced by OpenAI that ma
A collection of machine learning examples and tutorials.
machine_learning_examples A collection of machine learning examples and tutorials.
Flappy Bird hack using Deep Reinforcement Learning (Deep Q-learning).
Using Deep Q-Network to Learn How To Play Flappy Bird 7 mins version: DQN for flappy bird Overview This project follows the description of the Deep Q
deep learning for image processing including classification and object-detection etc.
深度学习在图像处理中的应用教程 前言 本教程是对本人研究生期间的研究内容进行整理总结,总结的同时也希望能够帮助更多的小伙伴。后期如果有学习到新的知识也会与大家一起分享。 本教程会以视频的方式进行分享,教学流程如下: 1)介绍网络的结构与创新点 2)使用Pytorch进行网络的搭建与训练 3)使用Te
Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices
Intro Real-time object detection and classification. Paper: version 1, version 2. Read more about YOLO (in darknet) and download weight files here. In
Natural Language Processing Best Practices & Examples
NLP Best Practices In recent years, natural language processing (NLP) has seen quick growth in quality and usability, and this has helped to drive bus
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
DeepCTR DeepCTR is a Easy-to-use,Modular and Extendible package of deep-learning based CTR models along with lots of core components layers which can
The repository is about 100+ python programming exercise problem discussed, explained, and solved in different ways
Break The Ice With Python A journey of 100+ simple yet interesting problems which are explained, solved, discussed in different pythonic ways Introduc
Machine Learning University: Accelerated Natural Language Processing Class
Machine Learning University: Accelerated Natural Language Processing Class This repository contains slides, notebooks and datasets for the Machine Lea
This repo contains the implementation of YOLOv2 in Keras with Tensorflow backend.
Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows).
Notes on the Deep Learning book from Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016)
The Deep Learning Book - Goodfellow, I., Bengio, Y., and Courville, A. (2016) This content is part of a series following the chapter 2 on linear algeb
DeepFashion2 is a comprehensive fashion dataset.
DeepFashion2 Dataset DeepFashion2 is a comprehensive fashion dataset. It contains 491K diverse images of 13 popular clothing categories from both comm
Code and data accompanying Natural Language Processing with PyTorch
Natural Language Processing with PyTorch Build Intelligent Language Applications Using Deep Learning By Delip Rao and Brian McMahan Welcome. This is a
Gathers machine learning and Tensorflow deep learning models for NLP problems, 1.13 Tensorflow 2.0
NLP-Models-Tensorflow, Gathers machine learning and tensorflow deep learning models for NLP problems, code simplify inside Jupyter Notebooks 100%. Tab
🙄 Difficult algorithm, Simple code.
🎉TensorFlow2.0-Examples🎉! "Talk is cheap, show me the code." ----- Linus Torvalds Created by YunYang1994 This tutorial was designed for easily divin
FMA: A Dataset For Music Analysis
FMA: A Dataset For Music Analysis Michaël Defferrard, Kirell Benzi, Pierre Vandergheynst, Xavier Bresson. International Society for Music Information
Python for downloading model data (HRRR, RAP, GFS, NBM, etc.) from NOMADS, NOAA's Big Data Program partners (Amazon, Google, Microsoft), and the University of Utah Pando Archive System.
Python for downloading model data (HRRR, RAP, GFS, NBM, etc.) from NOMADS, NOAA's Big Data Program partners (Amazon, Google, Microsoft), and the University of Utah Pando Archive System.
Pure python implementations of popular ML algorithms.
Minimal ML algorithms This repo includes minimal implementations of popular ML algorithms using pure python and numpy. The purpose of these notebooks
Housing Price Prediction Using Machine Learning.
HOUSING PRICE PREDICTION USING MACHINE LEARNING DESCRIPTION Housing Price Prediction Using Machine Learning is to predict the data of housings. Here I
Research into Forex price prediction from price history using Deep Sequence Modeling with Stacked LSTMs.
Forex Data Prediction via Recurrent Neural Network Deep Sequence Modeling Research Paper Our research paper can be viewed here Installation Clone the
A collection of data structures and algorithms I'm writing while learning
Data Structures and Algorithms: This is a collection of data structures and algorithms that I write while learning the subject Stack: stack.py A stack
Repository for the paper : Meta-FDMixup: Cross-Domain Few-Shot Learning Guided byLabeled Target Data
1 Meta-FDMIxup Repository for the paper : Meta-FDMixup: Cross-Domain Few-Shot Learning Guided byLabeled Target Data. (ACM MM 2021) paper News! the rep
Implementation of paper "DCS-Net: Deep Complex Subtractive Neural Network for Monaural Speech Enhancement"
DCS-Net This is the implementation of "DCS-Net: Deep Complex Subtractive Neural Network for Monaural Speech Enhancement" Steps to run the model Edit V
Official Implementation of "DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization."
DialogLM Code for AAAI 2022 paper: DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization. Pre-trained Models We release two ve
Official Implementation for Fast Training of Neural Lumigraph Representations using Meta Learning.
Fast Training of Neural Lumigraph Representations using Meta Learning Project Page | Paper | Data Alexander W. Bergman, Petr Kellnhofer, Gordon Wetzst
Code for Overinterpretation paper Overinterpretation reveals image classification model pathologies
Overinterpretation This repository contains the code for the paper: Overinterpretation reveals image classification model pathologies Authors: Brandon
PyTorch implementation for our paper "Deep Facial Synthesis: A New Challenge"
FSGAN Here is the official PyTorch implementation for our paper "Deep Facial Synthesis: A New Challenge". This project achieve the translation between
A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising (CVPR 2020 Oral & TPAMI 2021)
ELD The implementation of CVPR 2020 (Oral) paper "A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising" and its journal (TPAMI) v
Transfer Learning for Pose Estimation of Illustrated Characters
bizarre-pose-estimator Transfer Learning for Pose Estimation of Illustrated Characters Shuhong Chen *, Matthias Zwicker * WACV2022 [arxiv] [video] [po
Meta Learning Backpropagation And Improving It (VSML)
Meta Learning Backpropagation And Improving It (VSML) This is research code for the NeurIPS 2021 publication Kirsch & Schmidhuber 2021. Many concepts
Full LAKH MIDI dataset converted to MuseNet MIDI output format (9 instruments + drums)
LAKH MuseNet MIDI Dataset Full LAKH MIDI dataset converted to MuseNet MIDI output format (9 instruments + drums) Bonus: Choir on Channel 10 Please CC
Image based Human Fall Detection
Here I integrated the YOLOv5 object detection algorithm with my own created dataset which consists of human activity images to achieve low cost, high accuracy, and real-time computing requirements
Trained T5 and T5-large model for creating keywords from text
text to keywords Trained T5-base and T5-large model for creating keywords from text. Supported languages: ru Pretraining Large version | Pretraining B
To attract customers, the hotel chain has added to its website the ability to book a room without prepayment
To attract customers, the hotel chain has added to its website the ability to book a room without prepayment. We need to predict whether the customer is going to reject the booking or not. Since in case of refusal, the hotel incurs losses.
Deep Learning pipeline for motor-imagery classification.
BCI-ToolBox 1. Introduction BCI-ToolBox is deep learning pipeline for motor-imagery classification. This repo contains five models: ShallowConvNet, De
Using machine learning to predict undergrad college admissions.
College-Prediction Project- Overview: Many have tried, many have failed. Few trailblazers are ambitious enought to chase acceptance into the top 15 un
simple_pytorch_example project is a toy example of a python script that instantiates and trains a PyTorch neural network on the FashionMNIST dataset
simple_pytorch_example project is a toy example of a python script that instantiates and trains a PyTorch neural network on the FashionMNIST dataset
Neural-net-from-scratch - A simple Neural Network from scratch in Python using the Pymathrix library
A Simple Neural Network from scratch A Simple Neural Network from scratch in Pyt
Auto_code_complete is a auto word-completetion program which allows you to customize it on your needs
auto_code_complete is a auto word-completetion program which allows you to customize it on your needs. the model for this program is one of the deep-learning NLP(Natural Language Process) model structure called 'GRU(gated recurrent unit)'.
PyTorch GPU implementation of the ES-RNN model for time series forecasting
Fast ES-RNN: A GPU Implementation of the ES-RNN Algorithm A GPU-enabled version of the hybrid ES-RNN model by Slawek et al that won the M4 time-series
Implementation of deep learning models for time series in PyTorch.
List of Implementations: Currently, the reimplementation of the DeepAR paper(DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
Fully Convlutional Neural Networks for state-of-the-art time series classification
Deep Learning for Time Series Classification As the simplest type of time series data, univariate time series provides a reasonably good starting poin
Machine Learning for Time-Series with Python.Published by Packt
Machine-Learning-for-Time-Series-with-Python Become proficient in deriving insights from time-series data and analyzing a model’s performance Links Am
Deep Learning for Time Series Classification
Deep Learning for Time Series Classification This is the companion repository for our paper titled "Deep learning for time series classification: a re
U-Time: A Fully Convolutional Network for Time Series Segmentation
U-Time & U-Sleep Official implementation of The U-Time [1] model for general-purpose time-series segmentation. The U-Sleep [2] model for resilient hig
Tensorflow implementation of paper: Learning to Diagnose with LSTM Recurrent Neural Networks.
Multilabel time series classification with LSTM Tensorflow implementation of model discussed in the following paper: Learning to Diagnose with LSTM Re
A simple flask application to collect annotations for the Turing Change Point Dataset, a benchmark dataset for change point detection algorithms
AnnotateChange Welcome to the repository of the "AnnotateChange" application. This application was created to collect annotations of time series data
The Wearables Development Toolkit - a development environment for activity recognition applications with sensor signals
Wearables Development Toolkit (WDK) The Wearables Development Toolkit (WDK) is a framework and set of tools to facilitate the iterative development of
DeltaPy - Tabular Data Augmentation (by @firmai)
DeltaPy — Tabular Data Augmentation & Feature Engineering Finance Quant Machine Learning ML-Quant.com - Automated Research Repository Introduction T
A Python package for time series augmentation
tsaug tsaug is a Python package for time series augmentation. It offers a set of augmentation methods for time series, as well as a simple API to conn
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection
Deep learning for time series forecasting Flow forecast is an open-source deep learning for time series forecasting framework. It provides all the lat
Library for time-series-forecasting-as-a-service.
TIMEX TIMEX (referred in code as timexseries) is a framework for time-series-forecasting-as-a-service. Its main goal is to provide a simple and generi
Python implementation of the Learning Time-Series Shapelets method, that learns a shapelet-based time-series classifier with gradient descent.
shaplets Python implementation of the Learning Time-Series Shapelets method by Josif Grabocka et al., that learns a shapelet-based time-series classif
Elastic weight consolidation technique for incremental learning.
Overcoming-Catastrophic-forgetting-in-Neural-Networks Elastic weight consolidation technique for incremental learning. About Use this API if you dont
Algorithms for outlier, adversarial and drift detection
Alibi Detect is an open source Python library focused on outlier, adversarial and drift detection. The package aims to cover both online and offline d
Automated Time Series Forecasting
AutoTS AutoTS is a time series package for Python designed for rapidly deploying high-accuracy forecasts at scale. There are dozens of forecasting mod
Machine Learning Time-Series Platform
cesium: Open-Source Platform for Time Series Inference Summary cesium is an open source library that allows users to: extract features from raw time s
An API-first distributed deployment system of deep learning models using timeseries data to analyze and predict systems behaviour
Gordo Building thousands of models with timeseries data to monitor systems. Table of content About Examples Install Uninstall Developer manual How to
(JMLR' 19) A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)
Python Outlier Detection (PyOD) Deployment & Documentation & Stats & License PyOD is a comprehensive and scalable Python toolkit for detecting outlyin
Forecast dynamically at scale with this unique package. pip install scalecast
🌄 Scalecast: Dynamic Forecasting at Scale About This package uses a scaleable forecasting approach in Python with common scikit-learn and statsmodels
Hierarchical Time Series Forecasting with a familiar API
scikit-hts Hierarchical Time Series with a familiar API. This is the result from not having found any good implementations of HTS on-line, and my work
An open source python library for automated feature engineering
"One of the holy grails of machine learning is to automate more and more of the feature engineering process." ― Pedro Domingos, A Few Useful Things to
WTTE-RNN a framework for churn and time to event prediction
WTTE-RNN Weibull Time To Event Recurrent Neural Network A less hacky machine-learning framework for churn- and time to event prediction. Forecasting p
Teaches a student network from the knowledge obtained via training of a larger teacher network
Distilling-the-knowledge-in-neural-network Teaches a student network from the knowledge obtained via training of a larger teacher network This is an i
Code to accompany our paper "Continual Learning Through Synaptic Intelligence" ICML 2017
Continual Learning Through Synaptic Intelligence This repository contains code to reproduce the key findings of our path integral approach to prevent
PyTorch implementation of "Continual Learning with Deep Generative Replay", NIPS 2017
pytorch-deep-generative-replay PyTorch implementation of Continual Learning with Deep Generative Replay, NIPS 2017 Results Continual Learning on Permu
A PyTorch implementation of the continual learning experiments with deep neural networks
Brain-Inspired Replay A PyTorch implementation of the continual learning experiments with deep neural networks described in the following paper: Brain
Software Engineer Salary Prediction
Based on 2021 stack overflow data, this machine learning web application helps one predict the salary based on years of experience, level of education and the country they work in.
Analyzed the data of VISA applicants to build a predictive model to facilitate the process of VISA approvals.
Analyzed the data of Visa applicants, built a predictive model to facilitate the process of visa approvals, and based on important factors that significantly influence the Visa status recommended a suitable profile for the applicants for whom the visa should be certified or denied.
Medical appointments No-Show classifier
Medical Appointments No-shows Why do 20% of patients miss their scheduled appointments? A person makes a doctor appointment, receives all the instruct
Regularization and Feature Selection in Least Squares Temporal Difference Learning
Regularization and Feature Selection in Least Squares Temporal Difference Learning Description This is Python implementations of Least Angle Regressio
Sign Language Recognition service utilizing a deep learning model with Long Short-Term Memory to perform sign language recognition.
Sign Language Recognition Service This is a Sign Language Recognition service utilizing a deep learning model with Long Short-Term Memory to perform s
Código para trabalho com o dataset Wine em Python
Um perceptron multicamadas (MLP) é uma rede neural artificial feedforward que gera um conjunto de saídas a partir de um conjunto de entradas. Um MLP é
Arquivos do curso online sobre a estatística voltada para ciência de dados e aprendizado de máquina.
Estatistica para Ciência de Dados e Machine Learning Arquivos do curso online sobre a estatística voltada para ciência de dados e aprendizado de máqui
A generator of point clouds dataset for PyPipes.
CloudPipesGenerator Documentation | Colab Notebooks | Video Tutorials | Master Degree website A generator of point clouds dataset for PyPipes. TODO Us
Adds timm pretrained backbone to pytorch's FasterRcnn model
timmFasterRcnn model_config.py - it returns the model,feat_sizes,output channel and the feat layer names, which is reqd by the Add_FPN.py file Add_FP
Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort
Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort
PASSL包含 SimCLR,MoCo,BYOL,CLIP等基于对比学习的图像自监督算法以及 Vision-Transformer,Swin-Transformer,BEiT,CVT,T2T,MLP_Mixer等视觉Transformer算法
PASSL Introduction PASSL is a Paddle based vision library for state-of-the-art Self-Supervised Learning research with PaddlePaddle. PASSL aims to acce
PyTorch EO aims to make Deep Learning for Earth Observation data easy and accessible to real-world cases and research alike.
Pytorch EO Deep Learning for Earth Observation applications and research. 🚧 This project is in early development, so bugs and breaking changes are ex
Repository of continual learning papers
Continual learning paper repository This repository contains an incomplete (but dynamically updated) list of papers exploring continual learning in ma
A modular, open and non-proprietary toolkit for core robotic functionalities by harnessing deep learning
A modular, open and non-proprietary toolkit for core robotic functionalities by harnessing deep learning Website • About • Installation • Using OpenDR
A self-supervised learning framework for audio-visual speech
AV-HuBERT (Audio-Visual Hidden Unit BERT) Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction Robust Self-Supervised A
A curated list of the top 10 computer vision papers in 2021 with video demos, articles, code and paper reference.
The Top 10 Computer Vision Papers of 2021 The top 10 computer vision papers in 2021 with video demos, articles, code, and paper reference. While the w
A calibre plugin that generates Word Wise and X-Ray files then sends them to Kindle. Supports KFX, AZW3 and MOBI eBooks. X-Ray supports 18 languages.
WordDumb A calibre plugin that generates Word Wise and X-Ray files then sends them to Kindle. Supports KFX, AZW3 and MOBI eBooks. Languages X-Ray supp
A simple wrapper to analyse and visualise reinforcement learning agents' behaviour in the environment.
Visrl Visrl (pronounced "visceral") is a simple wrapper to analyse and visualise reinforcement learning agents' behaviour in the environment. Reinforc
Covid-polygraph - a set of Machine Learning-driven fact-checking tools
Covid-polygraph, a set of Machine Learning-driven fact-checking tools that aim to address the issue of misleading information related to COVID-19.
This is the offline-training-pipeline for our project.
offline-training-pipeline This is the offline-training-pipeline for our project. We adopt the offline training and online prediction Machine Learning
Tutorial on scikit-learn and IPython for parallel machine learning
Parallel Machine Learning with scikit-learn and IPython Video recording of this tutorial given at PyCon in 2013. The tutorial material has been rearra
Datasets, tools, and benchmarks for representation learning of code.
The CodeSearchNet challenge has been concluded We would like to thank all participants for their submissions and we hope that this challenge provided
General Assembly's 2015 Data Science course in Washington, DC
DAT8 Course Repository Course materials for General Assembly's Data Science course in Washington, DC (8/18/15 - 10/29/15). Instructor: Kevin Markham (
Ipython notebook presentations for getting starting with basic programming, statistics and machine learning techniques
Data Science 45-min Intros Every week*, our data science team @Gnip (aka @TwitterBoulder) gets together for about 50 minutes to learn something. While
🔅 Shapash makes Machine Learning models transparent and understandable by everyone
🎉 What's new ? Version New Feature Description Tutorial 1.6.x Explainability Quality Metrics To help increase confidence in explainability methods, y
SphereFace: Deep Hypersphere Embedding for Face Recognition
SphereFace: Deep Hypersphere Embedding for Face Recognition By Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj and Le Song License SphereFa
Creative Applications of Deep Learning w/ Tensorflow
Creative Applications of Deep Learning w/ Tensorflow This repository contains lecture transcripts and homework assignments as Jupyter Notebooks for th