6266 Open source projects that are alternatives of or similar to Deepai

Anomaly detection for temporal data using LSTMs

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

KitNET is a lightweight online anomaly detection algorithm, which uses an ensemble of autoencoders.

Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.

Master Deep Learning Algorithms with Extensive Math by Implementing them using TensorFlow

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.

Machine Learning Lectures at the European Space Agency (ESA) in 2018

Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN

中文的 tensorflow tutorial with jupyter notebooks

This repo contains the source code in my personal column (https://zhuanlan.zhihu.com/zhaoyeyu), implemented using Python 3.6. Including Natural Language Processing and Computer Vision projects, such as text generation, machine translation, deep convolution GAN and other actual combat code.

Deep learning meets molecular dynamics.

A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.

A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)

Log Anomaly Detection - Machine learning to detect abnormal events logs

Deep Learning sample programs using PyTorch in C++

iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data

This repository provides everything you need to get started with Python for (social science) research.

Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks - A lab we prepared for the KDD'19 Workshop on Anomaly Detection in Finance that will walk you through the detection of interpretable accounting anomalies using adversarial autoencoder neural networks. The majority of the lab content is based on J…

[NeurIPS 2020] Official code for the paper "DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation". Includes a PyTorch library for deep learning with SVG data.

An open-source, low-code machine learning library in Python

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BERT.

Ipython Notebooks for solving problems like classification, segmentation, generation using latest Deep learning algorithms on different publicly available text and image data-sets.

Representation learning for link prediction within social networks

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

Generative Probabilistic Novelty Detection with Adversarial Autoencoders

MotionSense Dataset for Human Activity and Attribute Recognition ( time-series data generated by smartphone's sensors: accelerometer and gyroscope)

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)

This is a times series anomaly detection algorithm, implemented in Python, for catching multiple anomalies. It uses a moving average with an extreme student deviate (ESD) test to detect anomalous points.

[5 FPS - 150 FPS] Learning Deep Features for One-Class Classification (AnomalyDetection). Corresponds RaspberryPi3. Convert to Tensorflow, ONNX, Caffe, PyTorch. Implementation by Python + OpenVINO/Tensorflow Lite.

Face ID recognition with medical masks

Essential and Fundametal aspects of Natural Language Processing with hands-on examples and case-studies

FMA: A Dataset For Music Analysis

UNet is a fully convolutional network(FCN) that does image segmentation. Its goal is to predict each pixel's class. It is built upon the FCN and modified in a way that it yields better segmentation in medical imaging.

Satellite data processing experiments

A collection of work related to COVID-19

Course materials and handouts for #100DaysOfCode in Python course

Introductory Python Series for UC Berkeley's D-Lab

Code and data accompanying Natural Language Processing with PyTorch published by O'Reilly Media https://nlproc.info

100 data puzzles for pandas, ranging from short and simple to super tricky (60% complete)

Face model to classify gender and race. Trained on LFWA+ Dataset.

Course notes for MIT manipulation class

Activities and exercises for the Part IA computing course in Michaelmas Term

Fined grained classification On Car dataset

Library of Stan Models for Survival Analysis

R, Python and Mathematica Codes in Machine Learning, Deep Learning, Artificial Intelligence, NLP and Geolocation

Montréal Deep Learning Summer School 2016 material

《动手学深度学习》 PyTorch 版本

An Introduction to Statistical Learning with Applications in PYTHON

Python implementation of frequent subgraph mining algorithm gSpan. Directed graphs are supported.

Deep Neural Decision Trees

Deep Markov Models

This is the code for the "How to Make an Image Classifier" - Intro to Deep Learning #6 by Siraj Raval on Youtube

Jupyter notebooks demonstrating the utilization of open-source codes for the study of materials science.

Repo of simple adversarial examples on vanilla neural networks trained on MNIST