Color recognition🎨 Color recognition & classification & detection on webcam stream / on video / on single image using K-Nearest Neighbors (KNN) is trained with color histogram features by OpenCV.
Stars: ✭ 154 (-3.14%)
Ed4Computational Cognitive Neuroscience, Fourth Edition
Stars: ✭ 133 (-16.35%)
Arc PytorchThe first public PyTorch implementation of Attentive Recurrent Comparators
Stars: ✭ 147 (-7.55%)
Automl alexState-of-the art Automated Machine Learning python library for Tabular Data
Stars: ✭ 132 (-16.98%)
Brain.jsbrain.js is a GPU accelerated library for Neural Networks written in JavaScript.
Stars: ✭ 12,358 (+7672.33%)
RobinRObust document image BINarization
Stars: ✭ 131 (-17.61%)
PersephoneA tool for automatic phoneme transcription
Stars: ✭ 130 (-18.24%)
Kitnet PyKitNET is a lightweight online anomaly detection algorithm, which uses an ensemble of autoencoders.
Stars: ✭ 152 (-4.4%)
Scarpet NnTools and libraries to run neural networks in Minecraft ⛏
Stars: ✭ 129 (-18.87%)
Uncertainty MetricsAn easy-to-use interface for measuring uncertainty and robustness.
Stars: ✭ 145 (-8.81%)
Image Caption GeneratorA neural network to generate captions for an image using CNN and RNN with BEAM Search.
Stars: ✭ 126 (-20.75%)
Deep CfrScalable Implementation of Deep CFR and Single Deep CFR
Stars: ✭ 158 (-0.63%)
Deep LyricsLyrics Generator aka Character-level Language Modeling with Multi-layer LSTM Recurrent Neural Network
Stars: ✭ 127 (-20.13%)
Enhancenet CodeEnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis (official repository)
Stars: ✭ 142 (-10.69%)
Rcnn Text ClassificationTensorflow Implementation of "Recurrent Convolutional Neural Network for Text Classification" (AAAI 2015)
Stars: ✭ 127 (-20.13%)
Nwaves.NET library for 1D signal processing focused specifically on audio processing
Stars: ✭ 151 (-5.03%)
JsnetJavascript/WebAssembly deep learning library for MLPs and convolutional neural networks
Stars: ✭ 126 (-20.75%)
LacmusLacmus is a cross-platform application that helps to find people who are lost in the forest using computer vision and neural networks.
Stars: ✭ 142 (-10.69%)
PadasipPython Adaptive Signal Processing
Stars: ✭ 138 (-13.21%)
Msmbuilder🏗 Statistical models for biomolecular dynamics 🏗
Stars: ✭ 118 (-25.79%)
AstronnDeep Learning for Astronomers with Tensorflow
Stars: ✭ 125 (-21.38%)
Awesome Decision Tree PapersA collection of research papers on decision, classification and regression trees with implementations.
Stars: ✭ 1,908 (+1100%)
DeepecgECG classification programs based on ML/DL methods
Stars: ✭ 124 (-22.01%)
CryptonetsCryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.
Stars: ✭ 152 (-4.4%)
Glcic PytorchA High-Quality PyTorch Implementation of "Globally and Locally Consistent Image Completion".
Stars: ✭ 141 (-11.32%)
AutoencodersImplementation of simple autoencoders networks with Keras
Stars: ✭ 123 (-22.64%)
Go Perceptron GoA single / multi layer / recurrent neural network written in Golang.
Stars: ✭ 159 (+0%)
AizynthfinderA tool for retrosynthetic planning
Stars: ✭ 122 (-23.27%)
Remo Python🐰 Python lib for remo - the app for annotations and images management in Computer Vision
Stars: ✭ 138 (-13.21%)
NeuronerNamed-entity recognition using neural networks. Easy-to-use and state-of-the-art results.
Stars: ✭ 1,579 (+893.08%)
AudioowlFast and simple music and audio analysis using RNN in Python 🕵️♀️ 🥁
Stars: ✭ 151 (-5.03%)
NnA tiny neural network 🧠
Stars: ✭ 119 (-25.16%)
Brain BitsA P300 online spelling mechanism for Emotiv headsets. It's completely written in Node.js, and the GUI is based on Electron and Vue.
Stars: ✭ 138 (-13.21%)
AmlaAutoML frAmework for Neural Networks
Stars: ✭ 119 (-25.16%)
NettackImplementation of the paper "Adversarial Attacks on Neural Networks for Graph Data".
Stars: ✭ 156 (-1.89%)
NnpackAcceleration package for neural networks on multi-core CPUs
Stars: ✭ 1,538 (+867.3%)
Document Classifier LstmA bidirectional LSTM with attention for multiclass/multilabel text classification.
Stars: ✭ 136 (-14.47%)
DeephyperDeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks
Stars: ✭ 117 (-26.42%)
Ml Workspace🛠 All-in-one web-based IDE specialized for machine learning and data science.
Stars: ✭ 2,337 (+1369.81%)
PaddlexPaddlePaddle End-to-End Development Toolkit(『飞桨』深度学习全流程开发工具)
Stars: ✭ 3,399 (+2037.74%)
OpenannAn open source library for artificial neural networks.
Stars: ✭ 117 (-26.42%)
BnafPytorch implementation of Block Neural Autoregressive Flow
Stars: ✭ 138 (-13.21%)
DgmDirect Graphical Models (DGM) C++ library, a cross-platform Conditional Random Fields library, which is optimized for parallel computing and includes modules for feature extraction, classification and visualization.
Stars: ✭ 157 (-1.26%)
Ai BlocksA powerful and intuitive WYSIWYG interface that allows anyone to create Machine Learning models!
Stars: ✭ 1,818 (+1043.4%)
FlowppCode for reproducing Flow ++ experiments
Stars: ✭ 137 (-13.84%)
House Price Prediction房价预测完整项目:1.爬取链家网数据 2.处理后,用sklearn中几个逻辑回归机器学习模型和keras神经网络搭建模型预测房价 最终结果神经网络效果更好,R^2值0.75左右
Stars: ✭ 116 (-27.04%)
LightwoodLightwood is Legos for Machine Learning.
Stars: ✭ 115 (-27.67%)
Ml CheatsheetA constantly updated python machine learning cheatsheet
Stars: ✭ 136 (-14.47%)
Keras ContribKeras community contributions
Stars: ✭ 1,532 (+863.52%)
JosefA robot who learns how to draw
Stars: ✭ 115 (-27.67%)
Gluon TsProbabilistic time series modeling in Python
Stars: ✭ 2,373 (+1392.45%)