212 Open source projects that are alternatives of or similar to tsflex

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

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.

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

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

Fast k-Nearest Neighbors Classifier for Large Datasets

Feature engineering package with sklearn like functionality

DeltaPy - Tabular Data Augmentation (by @firmai)

This is a comprehensive ML techniques with python: Define the Problem- Specify Inputs & Outputs- Data Collection- Exploratory data analysis -Data Preprocessing- Model Design- Training- Evaluation

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

Automatic feature engineering using deep learning and Bayesian inference using TensorFlow.

This repository consist of a 50-day program. All the statistics required for the complete understanding of data science will be uploaded in this repository.

Data transformations for the ML era

Top 5th percentile solution to the Kaggle knowledge problem - Bike Sharing Demand

This repository contains the code related to Natural Language Processing using python scripting language. All the codes are related to my book entitled "Python Natural Language Processing"

An intuitive library to extract features from time series

A collection of non-residential buildings for performance analysis and algorithm benchmarking

Comprehensive toolkit for generating various numerical features of protein sequences

Automatic feature engineering using Generative Adversarial Networks using TensorFlow.

A miniature lisp-like language for querying JSON-like structures. Tuned for clientside ML feature extraction.

Use advanced feature engineering strategies and select best features from your data set with a single line of code.

Stock for Deep Learning and Machine Learning

There are plenty of courses and tutorials that can help you learn machine learning from scratch but here in GitHub, I want to solve some Kaggle competitions as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template.

A sparsity aware and memory efficient implementation of "Attributed Social Network Embedding" (TKDE 2018).

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

A sparsity aware implementation of "Enhanced Network Embedding with Text Information" (ICPR 2018).

PHP-ML - Machine Learning library for PHP

Deep Learning: Image classification, feature visualization and transfer learning with Keras

The hottest High Dynamic Range (HDR) Library

🎨 Color recognition & classification & detection on webcam stream / on video / on single image using K-Nearest Neighbors (KNN) is trained with color histogram features by OpenCV.

Algorithms for matching audio file similarities. Mirror of https://git.iem.at/sciss/Strugatzki

📢 Ready to learn or review your knowledge!

Time series features

Fast and robust algorithm to extract edges in unorganized point clouds

R/shiny interface for interactive visualization of data in SummarizedExperiment objects

TFslim based semantic segmentation models, modular&extensible boutique design

USING BERT FOR Attribute Extraction in KnowledgeGraph. fine-tuning and feature extraction. 使用基于bert的微调和特征提取方法来进行知识图谱百度百科人物词条属性抽取。

.NET library for 1D signal processing focused specifically on audio processing

Automatic feature extraction and node role assignment for transfer learning on graphs (ReFeX & RolX)

🐟 A streaming ETL for fish

Implementation of VLAE

Fast and simple music and audio analysis using RNN in Python 🕵️‍♀️ 🥁

Content-Based Image Retrieval system (KTH DD2476 Project)

A large scale feature extraction tool for text-based machine learning

👷‍♂️ A simple package for extracting useful features from character objects 👷‍♀️

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

📖 This guide is to help you understand the basics of the computerized image and develop computer vision projects with OpenCV. Includes Python, Java, JavaScript, C# and C++ examples.

Simple TF IDF Library

Android app analysis and feature extraction library

Fingerprint recognition engine for Java that takes a pair of human fingerprint images and returns their similarity score. Supports efficient 1:N search.

Audio feature extraction for JavaScript.

A Python wrapper for Kaldi

🏗 Statistical models for biomolecular dynamics 🏗

Automatic extraction of relevant features from time series:

Convenient wrapper for TensorFlow feature extraction from pre-trained models using tf.contrib.slim

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 C++ Qt GUI desktop program to calculate Harris, FAST, SIFT and SURF image features with OpenCV

Machine Learning library for the web and Node.

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

💎 "Marker-less Augmented Reality" with OpenCV and OpenGL.

Code examples for training AlexNet using Keras and Theano