735 Open source projects that are alternatives of or similar to Kitnet Py

A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

Next RecSys Library

TensorFlow and Deep Learning Tutorials

Handle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.

Python codes in Machine Learning, NLP, Deep Learning and Reinforcement Learning with Keras and Theano

Generative Probabilistic Novelty Detection with Adversarial Autoencoders

Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)

Anomaly detection for temporal data using LSTMs

Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

Deep Learning sample programs using PyTorch in C++

An easy-to-use interface for measuring uncertainty and robustness.

A Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm.

PaddlePaddle End-to-End Development Toolkit（『飞桨』深度学习全流程开发工具）

Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction. In CVPR, 2017.

A powerful and intuitive WYSIWYG interface that allows anyone to create Machine Learning models!

STUMPY is a powerful and scalable Python library for modern time series analysis

NCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.

Code & data accompanying the KDD 2017 paper "KATE: K-Competitive Autoencoder for Text"

Lacmus is a cross-platform application that helps to find people who are lost in the forest using computer vision and neural networks.

Various CNN models for CIFAR10 with Chainer

List of tools & datasets for anomaly detection on time-series data.

CryptoNets 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.

👷‍♂️ A simple package for extracting useful features from character objects 👷‍♀️

A High-Quality PyTorch Implementation of "Globally and Locally Consistent Image Completion".

PyTorch implementation of DEC (Deep Embedding Clustering)

Real-World Anomaly Detection in Surveillance Videos

machine learning and deep learning tutorials, articles and other resources

Python Adaptive Signal Processing

🛠 All-in-one web-based IDE specialized for machine learning and data science.

Pytorch implementation of Block Neural Autoregressive Flow

This repository contains the code of LiviaNET, a 3D fully convolutional neural network that was employed in our work: "3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study"

Code for reproducing Flow ++ experiments

Latent space autoregression for novelty detection.

PyTorch 101 series covering everything from the basic building blocks all the way to building custom architectures.

EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis (official repository)

Unity Machine Learning Agents Toolkit

Probabilistic time series modeling in Python

Example projects I completed to understand Deep Learning techniques with Tensorflow. Please note that I do no longer maintain this repository.

A collection of common algorithms and data structures implemented in java, c++, and python.

Computational Cognitive Neuroscience, Fourth Edition

A PyTorch implementation of Deep SAD, a deep Semi-supervised Anomaly Detection method.

A Python 3 library making time series data mining tasks, utilizing matrix profile algorithms, accessible to everyone.

BERT, AWS RDS, AWS Forecast, EMR Spark Cluster, Hive, Serverless, Google Assistant + Raspberry Pi, Infrared, Google Cloud Platform Natural Language, Anomaly detection, Tensorflow, Mathematics

Discovering Neural Wirings (https://arxiv.org/abs/1906.00586)

Machine Learning for High Energy Physics.

Deep Learning for Julia

An End-to-end Outlier Detection System

Log-based Impactful Problem Identification using Machine Learning [FSE'18]

RObust document image BINarization

Focal Frequency Loss for Generative Models

A tool for automatic phoneme transcription

Efficient Spiking Neural Network framework, built on top of PyTorch for GPU acceleration

Implementation of the stacked denoising autoencoder in Tensorflow

A simple Tensorflow based library for deep and/or denoising AutoEncoder.

Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.

Tools and libraries to run neural networks in Minecraft ⛏

JeVois smart machine vision framework

🐰 Python lib for remo - the app for annotations and images management in Computer Vision

A robust, and flexible open source User & Entity Behavior Analytics (UEBA) framework used for Security Analytics. Developed with luv by Data Scientists & Security Analysts from the Cyber Security Industry. [PRE-ALPHA]