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HyperGAN - A composable Generative Adversarial Network(GAN) with API and command line tool.
A composable GAN API and CLI. Built for developers, researchers, and artists. HyperGAN is currently in open beta.
gan supervised-learning unsupervised-learning learning generative-adversarial-network generative-model artificial-intelligence machine-learning machine-learning-api tensorflow classification generator discriminatoropencog - A framework for integrated Artificial Intelligence & Artificial General Intelligence (AGI)
OpenCog is a framework for developing AI systems, especially appropriate for integrative multi-algorithm systems, and artificial general intelligence systems. Though much work remains to be done, it currently contains a functional core framework, and a number of cognitive agents at varying levels of completion, some already displaying interesting and useful functionalities alone and in combination. With the exception of MOSES and the CogServer, all of the above are in active development, are half-baked, poorly documented, mis-designed, subject to experimentation, and generally in need of love an attention. This is where experimentation and integration are taking place, and, like any laboratory, things are a bit fluid and chaotic.
agi natural-language natural-language-inference natural-language-understanding robotics robot-controller learning learning-algorithm unsupervised-learning unsupervised-machine-learning unsupervised-learning-algorithmsmlxtend - A library of extension and helper modules for Python's data analysis and machine learning libraries
Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks.
machine-learning data-science data-mining association-rules supervised-learning unsupervised-learningDiscoGAN-pytorch - PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"
PyTorch implementation of Learning to Discover Cross-Domain Relations with Generative Adversarial Networks. * All samples in README.md are genearted by neural network except the first image for each row. * Network structure is slightly diffferent (here) from the author's code.
gan generative-model unsupervised-learning pytorchSfMLearner - An unsupervised learning framework for depth and ego-motion estimation from monocular videos
In CVPR 2017 (Oral). See the project webpage for more details. Please contact Tinghui Zhou (tinghuiz@berkeley.edu) if you have any questions.
deep-learning depth-prediction visual-odometry self-supervised-learning unsupervised-learningOpenSelfSup - Self-Supervised Learning Toolbox and Benchmark
The master branch works with PyTorch 1.1 or higher. OpenSelfSup is an open source unsupervised representation learning toolbox based on PyTorch.
pytorch unsupervised-learning moco self-supervised-learning simclr deepcluster onlinedeepclustersimclr - SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners
News! We have released a TF2 implementation of SimCLR (along with converted checkpoints in TF2), they are in tf2/ folder. News! Colabs for Intriguing Properties of Contrastive Losses are added, see here.
computer-vision representation-learning unsupervised-learning self-supervised-learning simclr contrastive-learning simclrv2alibi-detect - 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 detectors for tabular data, text, images and time series. Both TensorFlow and PyTorch backends are supported for drift detection. For more background on the importance of monitoring outliers and distributions in a production setting, check out this talk from the Challenges in Deploying and Monitoring Machine Learning Systems ICML 2020 workshop, based on the paper Monitoring and explainability of models in production and referencing Alibi Detect.
time-series text images detection tabular-data semi-supervised-learning anomaly unsupervised-learning adversarial concept-drift outlier drift-detection data-driftdomain-transfer-network - TensorFlow Implementation of "Unsupervised Cross-Domain Image Generation"
TensorFlow implementation of Unsupervised Cross-Domain Image Generation.
tensorflow domain-transfer unsupervised-learning image-generationcontext-encoder - [CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs
If you could successfully run the above demo, run following steps to train your own context encoder model for image inpainting. Features for context encoder trained with reconstruction loss.
image-inpainting context-encoders unsupervised-learning machine-learning generative-adversarial-network deep-learning computer-vision gan dcgan computer-graphicsPyod - A Python Toolkit for Scalable Outlier Detection (Anomaly Detection)
Important Notes: PyOD contains some neural network based models, e.g., AutoEncoders, which are implemented in keras. However, PyOD would NOT install keras and tensorflow automatically. This would reduce the risk of damaging your local installations. You are responsible for installing keras and tensorflow if you want to use neural net based models. An instruction is provided here. Anomaly detection resources, e.g., courses, books, papers and videos.
outlier-detection anomaly-detection outlier-ensembles outliers anomaly machine-learning data-mining unsupervised-learning python2 python3 fraud-detection autoencoder neural-networks deep-learningAll-About-the-GAN - All About the GANs(Generative Adversarial Networks) - Summarized lists for GAN
The purpose of this repository is providing the curated list of the state-of-the-art works on the field of Generative Adversarial Networks since their introduction in 2014. You can also check out the same data in a tabular format with functionality to filter by year or do a quick search by title here.
gan adversarial-networks arxiv neural-network unsupervised-learning adversarial-nets image-synthesis deep-learning generative-adversarial-network medical-imaging tensorflow pytorch paper cgan ct-denoising segmentation medical-image-synthesis reconstruction detection classificationawesome-transfer-learning - Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc
A list of awesome papers and cool resources on transfer learning, domain adaptation and domain-to-domain translation in general! As you will notice, this list is currently mostly focused on domain adaptation (DA), but don't hesitate to suggest resources in other subfields of transfer learning. I accept pull requests. Papers are ordered by theme and inside each theme by publication date (submission date for arXiv papers). If the network or algorithm is given a name in a paper, this one is written in bold before the paper's name.
transfer-learning domain-adaptation unsupervised-learning paper awesome-listChinese-UFLDL-Tutorial - [UNMAINTAINED] 非监督特征学习与深度学习中文教程，该版本翻译自新版 UFLDL Tutorial 。建议新人们去学习斯坦福的CS231n课程，该门课程在网易云课堂上也有一个配有中文字幕的版本。
[UNMAINTAINED] 非监督特征学习与深度学习中文教程，该版本翻译自新版 UFLDL Tutorial 。建议新人们去学习斯坦福的CS231n课程，该门课程在网易云课堂上也有一个配有中文字幕的版本。
unsupervised-learning convolutional-neural-networks supervised-neural-network sparse-autoencoders exercise taught-learningladder - Ladder network is a deep learning algorithm that combines supervised and unsupervised learning
This is an implementation of Ladder Network in TensorFlow. Ladder network is a deep learning algorithm that combines supervised and unsupervised learning. It was introduced in the paper Semi-Supervised Learning with Ladder Network by A Rasmus, H Valpola, M Honkala, M Berglund, and T Raiko.
ladder-network deep-learning-algorithms unsupervised-learningOpenUnReID - PyTorch open-source toolbox for unsupervised or domain adaptive object re-ID.
OpenUnReID is an open-source PyTorch-based codebase for both unsupervised learning (USL) and unsupervised domain adaptation (UDA) in the context of object re-ID tasks. It provides strong baselines and multiple state-of-the-art methods with highly refactored codes for both pseudo-label-based and domain-translation-based frameworks. It works with Python >=3.5 and PyTorch >=1.1. We are actively updating this repo, and more methods will be supported soon. Contributions are welcome.
unsupervised-learning image-retrieval re-identification pseudo-labeling unsupervised-domain-adaptation open-set-domain-adaptation domain-translationFleetX - Paddle Distributed Training Extended. 飞桨分布式训练扩展包
Fully utilize your GPU Clusters with FleetX for your model pre-training. For any feedback or to report a bug, please propose a GitHub Issue.
benchmark cloud lightning elastic unsupervised-learning large-scale data-parallelism paddlepaddle model-parallelism distributed-algorithm self-supervised-learning pipeline-parallelism pretraining fleet-api paddlecloudreal_estate_ml - Machine Learning for Real Estate
Machine Learning for Real Estate
pandas scikit-learn knn plotly jupyter-notebook machine-learning real-estate unsupervised-learningesapp - An unsupervised Chinese word segmentation tool.
See test_package/example.cpp. The recommended way to use ESA++ package in your project is to install the package with Conan.
computational-linguistics chinese-nlp chinese-text-segmentation word-segmentation unsupervised-learning nlpsutton-barto-rl-exercises - Learning reinforcement learning by implementing the algorithms from reinforcement learning an introduction
Pull requests and bug report are welcome. Note: if you find the formatting some notebooks (esp. with many equations) doesn't look good on github,, try visualize them on http://nbviewer.jupyter.org/github/zyxue/sutton-barto-rl-exercises/tree/master/.
machine-learning reinforcement-learning sutton barto supervised-learning unsupervised-learning
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