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.

A cross-platform video structuring (video analysis) framework. If you find it helpful, please give it a star: ) 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 : )

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

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

Feature engineering and selection open-source Python library compatible with sklearn.

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

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

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

Audio feature extraction for JavaScript.

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

An intuitive library to extract features from time series.

A Python wrapper for Kaldi

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

Highly comparative time-series analysis

The Munich Open-Source Large-Scale Multimedia Feature Extractor

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.

Fully Convolutional Geometric Features: Fast and accurate 3D features for registration and correspondence.

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

Use advanced feature engineering strategies and select best features from your data set with a single line of code. Created by Ram Seshadri. Collaborators welcome.

Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras

Extract video features from raw videos using multiple GPUs. We support RAFT flow frames as well as S3D, I3D, R(2+1)D, VGGish, CLIP, and TIMM models.

KISS-Matcher: Fast, Robust, and Scalable Registration + ROS2 SLAM examples

A complete end-to-end pipeline for LLM interpretability with sparse autoencoders (SAEs) using Llama 3.2, written in pure PyTorch and fully reproducible.

Medical image processing in Python

A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning

ACG2vec (Anime Comics Games to vector) are committed to creating a playground that combines ACG and Deep learning.（文本语义检索、以图搜图、语义搜图、图片超分辨率、推荐系统）