Automatic extraction of relevant features from time series:

A PyTorch implementation of EfficientNet

It is my belief that you, the postgraduate students and job-seekers for whom the book is primarily meant will benefit from reading it; however, it is my hope that even the most experienced researchers will find it fascinating as well.

🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥

Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.

特征提取/数据降维:PCA、LDA、MDS、LLE、TSNE等降维算法的python实现

A low code Machine Learning personalized ranking service for articles, listings, search results, recommendations that boosts user engagement. A friendly Learn-to-Rank engine

Feature engineering package with sklearn like functionality

OpenMLDB is an open-source machine learning database that provides a feature platform computing consistent features for training and inference.

Audio feature extraction for JavaScript.

A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.

跨平台的视频结构化（视频分析）框架，觉得有帮助的请给个星星 : ) 。**VideoPipe下一版本正在开发中，在保证跨平台、易上手的前提下，预计性能直逼deepstream等各硬件平台官方框架**。

Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.

Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks. Support: https://discourse.slicer.org/c/community/radiomics

A Python wrapper for Kaldi

An intuitive library to extract features from time series.

💬 SpeechPy - A Library for Speech Processing and Recognition: http:https://speechpy.readthedocs.io/en/latest/

Highly comparative time-series analysis

Features selector based on the self selected-algorithm, loss function and validation method

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.