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Brain is a JavaScript library for building, training, and running feed-forward neural networks. It implements a multilayer perceptron model designed for pattern recognition and function approximation. The library includes a standalone inference engine that converts trained models into portable JavaScript functions. This allows predictions to be executed in browser or Node.js environments without requiring the original library dependencies. The system supports persistent model management through JSON serialization for saving and loading network weights. It also provides a streaming mechanism
DIGITS is a GPU deep learning training platform and model manager used to train, fine-tune, and manage neural network models on NVIDIA hardware. It functions as a REST-controlled machine learning pipeline that integrates with S3 cloud storage for dataset ingestion and organization. The platform supports image classification workflows, allowing users to train various model architectures and export trained image classifiers for use in external environments. It includes capabilities for model fine-tuning to adapt pretrained weights to specific tasks. The system provides a REST-based API interfa
This project is a collection of TensorFlow 2.x machine learning tutorials and practical code examples. It serves as a deep learning implementation guide for constructing diverse neural network architectures, including convolutional, recurrent, and generative networks. The repository provides templates and examples for several specialized domains, including computer vision for image classification and object detection, natural language processing for text generation and language understanding, and generative AI for synthesizing data using adversarial networks and autoencoders. It also includes
This project is a reinforcement learning framework and game AI engine designed for training adversarial agents in two-player turn-based games. It implements a training loop that utilizes self-play and Monte Carlo Tree Search to produce neural networks capable of predicting board strength and move probabilities. The system decouples the reinforcement learning engine from specific game rules through an abstract game logic interface, allowing for the definition of custom game rules, win conditions, and board representations. It supports integration with various deep learning frameworks to serve
Neural Amp Modeler is an open-source project that captures the tonal character of analog audio gear by training a neural network on paired dry and reamped audio recordings. It provides a complete pipeline for learning how a guitar amplifier, effects pedal, or other audio device transforms a signal, then exports the trained model into a portable file format for use in other applications.
The main features of sdatkinson/neural-amp-modeler are: Audio Gear Model Trainers, Analog Gear Emulators, Audio Transfer Function Learners, Audio Device Character Capturers, Audio Gear Response Modelers, Neural Network Training, Neural Amp Model Runtimes, Guitar Amp Modelers.
Open-source alternatives to sdatkinson/neural-amp-modeler include: harthur/brain — Brain is a JavaScript library for building, training, and running feed-forward neural networks. It implements a… nvidia/digits — DIGITS is a GPU deep learning training platform and model manager used to train, fine-tune, and manage neural network… suragnair/alpha-zero-general — This project is a reinforcement learning framework and game AI engine designed for training adversarial agents in… dragen1860/tensorflow-2.x-tutorials — This project is a collection of TensorFlow 2.x machine learning tutorials and practical code examples. It serves as a… snowkylin/tensorflow-handbook — This project is a comprehensive educational resource and tutorial handbook for building, training, and deploying… morvanzhou/pytorch-tutorial — This project is a collection of PyTorch learning resources and educational guides designed to teach the construction…