Training Libraries

17 Components & Libraries

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Training Libraries

Low-code framework for building custom LLMs, neural networks, and other AI models

Key features: Install from PyPi. Be aware that Ludwig requires Python 3.8+. Or install with all optional dependencies: Let's fine-tune a pretrained LLaMA-2-7b large language model to follow instructi…

Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

With Intel(R) Extension for Scikit-learn, you can: To install Intel(R) Extension for Scikit-learn, run: The software acceleration is achieved through patching. It means, replacing the stock scikit-le…

Security Knowledge Framework (SKF) Python Flask / Angular project

Please note that this is the archived version of SKF, we are busy building a brand new Security Knowledge Framework. For more updates please visit this github repo: Our experience taught us that the …

Train machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.

Train machine learning models within a Docker container using Amazon SageMaker. Build and tag the Docker image. Implement an argument parser in the entry point script. For example, in a Python script…

🗂 Split folders with files (i.e. images) into training, validation and test (dataset) folders

The input folder should have the following format: In order to give you this: This package is Python only and there are no external dependencies. MIT

HandyRL is a handy and simple framework based on Python and PyTorch for distributed reinforcement learning that is applicable to your own environments.

HandyRL is a handy and simple framework based on Python and PyTorch for distributed reinforcement learning that is applicable to your own environments. HandyRL focuses on a practicable algorithm and …

The easiest way to use Machine Learning. Mix and match underlying ML libraries and data set sources. Generate new datasets or modify existing ones with ease.

As we all know the Machine Learning space has a lot of tools and libraries for creating pipelines to train, test & deploy models, and dealing with these many different APIs can be cumbersome. Our…

Amazon SageMaker Local Mode Examples

This repository contains examples and related resources showing you how to preprocess, train, debug your training script with breakpoints, and serve on your local machine using Amazon SageMaker Local…

A repo for a course on Python DevOps

Lecture materials "Bio-image analysis, biostatistics, programming and machine learning for computational biology" at the Center of Molecular and Cellular Bioengineering (CMCB) / University of Technology, TU Dresden

The material will develop between April and July 2023. The materials from former years are linked below. This course explains everything in very detail. Every lesson ends with an exercise and it is r…

Totally Insecure Web Application Project (TIWAP)

TIWAP is a web security testing lab made using Flask for budding security enthusiasts to learn about various web vulnerabilities. Inspired by DVWA, the contributors have tried their best to regenerat…

EEG-Motor-Imagery-Classification-CNNs-TensorFlow

EEG Motor Imagery Tasks Classification (by Channels) via Convolutional Neural Networks (CNNs) based on TensorFlow

Meanwhile, the codes in this repository are based on the raw EEG data without the ESI and JTFA process, and can also achieve a good result. The main CNNs Tensorflow framework codes in the "MI_Propose…

A simple Python wrapper for audio noise reduction RNNoise. Simplifies work with it, adds new trained models and detailed instructions for training.

RNNoise написан на C и имеет методы для шумоподавления одного фрейма длиной 10 миллисекунд. Фрейм должен быть с частотой дискретизации 48000Гц, моно, 16 бит. Установка с помощью pip: И выполнить: Под…

A hierarchical parameter server framework based on MXNet. GeoMX also implements multiple communication-efficient strategies.

GeoMX is a MXNet-based two-layer parameter server framework, aiming at integrating data knowledge that owned by multiple independent parties in a privacy-preserving way (i.e. no need to transfer raw …

This repository contains works on a computer vision software pipeline built on top of Python to identify Lanes and vehicles in a video. This project is not part of Udacity SDCND but is based on other free courses and challanges provided by Udacity. It uses Computer vision and Deep Learrning Techniques. Few pipelines have been tried on SeDriCa, IIT Bombay.

To run the lane detection script for any video files, update the last few lines of 'lanedetect.py'. The final calibration matrices are saved in the pickle file 'calibrate_camera.p' The following desc…

Simple GUI application to help record audio dictated from given text prompts, for use with training speech recognition or speech synthesis.

Simple GUI application to help record audio dictated from given textprompts, for use with training speech recognition or speech synthesis.

Implementing Neural Networks for Computer Vision in autonomous vehicles and robotics for classification, pattern recognition, control. Using Python, numpy, tensorflow. From basics to complex project

Valentyn N Sichkar. Neural Networks for computer vision in autonomous vehicles and robotics // GitHub platform. DOI: 10.5281/zenodo.1317904 Valentyn N Sichkar. Neural Networks for computer vision in …

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