Visualizer for neural network, deep learning and machine learning models
-
Updated
May 19, 2024 - JavaScript
Artificial neural networks (ANN) are computational systems that "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules.
Visualizer for neural network, deep learning and machine learning models
On-device AI across mobile, embedded and edge for PyTorch
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Neural Networks with Sparse Weights in Rust using GPUs, CPUs, and FPGAs via CUDA, OpenCL, and oneAPI
🦋 A personal research and development (R&D) lab that facilitates the sharing of knowledge.
flexible and extensible implementation of a stochastic gradient descent feedforward neural network in Java, wrapped up in a console user interface
Implicit representation of various things using PyTorch and high order layers
An Open Source Machine Learning Framework for Everyone
A Simple Feed Forward Neural Network with Back Propagation from Scratch in Typescript
Bright Wire is an open source machine learning library for .NET with GPU support (via CUDA)
Jailbreaking Aligned LLMs with ArtPrompt
This project is an introduction to artificial neural networks thanks to the implementation of a multilayer perceptron.
Application designed to predict the impacts of climate change and respond to natural disasters using satellite imagery and machine learning.
contains codes of Machine Learning, Deep learning and Reinforcement learning applied in sort of scratch but mostly using this library
Quickly detect and classify different species of harmful algae within natural water in real-time with AI and a camera (i.e., ESP32-CAM, smartphone, or webcam).
Recreating PyTorch from scratch (C/C++, CUDA and Python, with GPU support and automatic differentiation!)
A Python package for identifying 42 kinds of animals, training custom models, and estimating distance from camera trap videos