A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2021 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!
A unified Data Analytics and AI platform for distributed TensorFlow, Keras and PyTorch on Apache Spark/Flink & Ray What is Analytics Zoo? Analytics Zo
GluonTS - Probabilistic Time Series Modeling in Python GluonTS is a Python toolkit for probabilistic time series modeling, built around Apache MXNet (
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. 10x Larger Models 10x Faster Trainin
Ray provides a simple, universal API for building distributed applications. Ray is packaged with the following libraries for accelerating machine lear
scikit-fem is a lightweight Python 3.7+ library for performing finite element assembly. Its main purpose is the transformation of bilinear forms into sparse matrices and linear forms into vectors.
Falken provides developers with a service that allows them to train AI that can play their games. Unlike traditional RL frameworks that learn through rewards or batches of offline training, Falken is based on training AI via realtime, human interactions.
Pyomo is a Python-based open-source software package that supports a diverse set of optimization capabilities for formulating and analyzing optimization models. Pyomo can be used to define symbolic problems, create concrete problem instances, and solve these instances with standard solvers.
eXtreme Gradient Boosting Community | Documentation | Resources | Contributors | Release Notes XGBoost is an optimized distributed gradient boosting l
It selects the best indexing parameters to achieve the highest recalls given memory and query speed constraints.
Mesh TensorFlow - Model Parallelism Made Easier Introduction Mesh TensorFlow (mtf) is a language for distributed deep learning, capable of specifying
XManager is a platform for packaging, running and keeping track of machine learning experiments. It currently enables one to launch experiments locally or on Google Cloud Platform (GCP). Interaction with experiments is done via XManager's APIs through Python launch scripts.
luminol Overview Luminol is a light weight python library for time series data analysis. The two major functionalities it supports are anomaly detecti
LibRerank LibRerank is a toolkit for re-ranking algorithms. There are a number of re-ranking algorithms, such as PRM, DLCM, GSF, miDNN, SetRank, EGRer
neurodsp is a collection of approaches for applying digital signal processing to neural time series, including algorithms that have been proposed for the analysis of neural time series. It also includes simulation tools for generating plausible simulations of neural time series.
Jina is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the efficient patterns to build the system by parts, or chaining them into a Flow for an end-to-end experience.
Time Series Made Easy in Python darts is a python library for easy manipulation and forecasting of time series. It contains a variety of models, from
Modular Active Learning framework for Python3 Page contents Introduction Active learning from bird's-eye view modAL in action From zero to one in a fe
Microsoft Machine Learning for Apache Spark MMLSpark is an ecosystem of tools aimed towards expanding the distributed computing framework Apache Spark
ZenML is an extensible, open-source MLOps framework to create production-ready machine learning pipelines. It has a simple, flexible syntax, is cloud and tool agnostic, and has interfaces/abstractions that are catered towards ML workflows.
tslearn The machine learning toolkit for time series analysis in Python Section Description Installation Installing the dependencies and tslearn Getti
Streamz Streamz helps you build pipelines to manage continuous streams of data. It is simple to use in simple cases, but also supports complex pipelin
MosaicML Composer MosaicML Composer contains a library of methods, and ways to compose them together for more efficient ML training. We aim to ease th
Elephas: Distributed Deep Learning with Keras & Spark Elephas is an extension of Keras, which allows you to run distributed deep learning models at sc
Horovod Horovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. The goal of Horovod is to make dis
SLAM-application: installation and test (3D): LeGO-LOAM, LIO-SAM, and LVI-SAM Tested on Quadruped robot in Gazebo ● Results: video, video2 Requirement
MLBox is a powerful Automated Machine Learning python library. It provides the following features: Fast reading and distributed data preprocessing/cle
LibTraffic is a unified, flexible and comprehensive traffic prediction library, which provides researchers with a credibly experimental tool and a convenient development framework. Our library is implemented based on PyTorch, and includes all the necessary steps or components related to traffic prediction into a systematic pipeline.
Our article on Towards Data Science introduces the package and provides background information. Pytorch Forecasting aims to ease state-of-the-art time
pyts: a Python package for time series classification pyts is a Python package for time series classification. It aims to make time series classificat
Interactive Parallel Computing with IPython ipyparallel is the new home of IPython.parallel. ipyparallel is a Python package and collection of CLI scr
Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks. Sebastian Raschka 2014-2021 Links Doc
Parallelized symbolic regression built on Julia, and interfaced by Python. Uses regularized evolution, simulated annealing, and gradient-free optimization.
[Due to the time taken @ uni, work + hell breaking loose in my life, since things have calmed down a bit, will continue commiting!!!] [By the way, I'm
PyFlux PyFlux is an open source time series library for Python. The library has a good array of modern time series models, as well as a flexible array
A python toolbox/library for data mining on partially-observed time series, supporting tasks of forecasting/imputation/classification/clustering on incomplete multivariate time series with missing values.
timemachines Autonomous, univariate, k-step ahead time-series forecasting functions assigned Elo ratings You can: Use some of the functionality of a s
LiuAlgoTrader is a scalable, multi-process ML-ready framework for effective algorithmic trading. The framework simplify development, testing, deployment, analysis and training algo trading strategies. The framework automatically analyzes trading sessions, and the analysis may be used to train predictive models.
Evidently helps analyze machine learning models during validation or production monitoring. The tool generates interactive visual reports and JSON profiles from pandas DataFrame or csv files. Currently 6 reports are available.
Graphsignal is a machine learning model monitoring platform. It helps ML engineers, MLOps teams and data scientists to quickly address issues with data and models as well as proactively analyze model performance and availability.
cuML - GPU Machine Learning Algorithms cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions t
Prophet: Automatic Forecasting Procedure Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends ar
English | 简体中文 AutoX是什么？ AutoX一个高效的自动化机器学习工具，它主要针对于表格类型的数据挖掘竞赛。 它的特点包括: 效果出色: AutoX在多个kaggle数据集上，效果显著优于其他解决方案(见效果对比)。 简单易用: AutoX的接口和sklearn类似，方便上手使用。
Submit it! What is submitit? Submitit is a lightweight tool for submitting Python functions for computation within a Slurm cluster. It basically wraps
ClearML - Auto-Magical Suite of tools to streamline your ML workflow Experiment Manager, MLOps and Data-Management ClearML Formerly known as Allegro T
pyGAM Generalized Additive Models in Python. Documentation Official pyGAM Documentation: Read the Docs Building interpretable models with Generalized