Neural Networks
Showing 36 of 382 projects
A comparative Python framework for building, evaluating, and deploying multimodal recommender systems with auxiliary data.
A PyTorch framework for training neural learning-to-rank models with flexible loss functions and scoring architectures.
A curated collection of machine learning resources, examples, and experiments for creative coding and education.
High-level TensorFlow network definitions with pre-trained weights for easy integration into existing ML workflows.
A TensorFlow implementation of QANet for machine reading comprehension on the SQuAD dataset.
A pretrained modeling library for Keras 3 offering simple, flexible, and fast access to models for text, image, and audio tasks.
A Python implementation of Restricted Boltzmann Machines for binary factor analysis and collaborative filtering.
GPU-accelerated audio preprocessing layers for Keras/TensorFlow, enabling real-time audio feature extraction within neural networks.
A Recurrent Neural Network library for Torch7's nn, providing RNN, LSTM, GRU, and other sequence modeling modules.
A strongly-typed Scala API for TensorFlow, providing functionality similar to the official Python API with additional features.
A distributed platform for rapid deep learning application development with neural network engine and Hadoop integration.
TensorFlow implementation of an attention-based neural image caption generator that focuses on relevant image parts while generating words.
A Go interface for importing and executing pre-trained ONNX neural network models without framework dependencies.
GPU-accelerated Python implementation of six fundamental deep learning algorithms using CUDA libraries.
MatLab/Octave implementations of popular machine learning algorithms with detailed mathematical explanations and code examples.
An automated feature generation framework for tabular data that discovers expert-level features to boost machine learning model performance.
CVPR 2015 workshop materials for learning deep learning and computer vision with Torch framework.
Convert PyTorch models to Keras (TensorFlow backend) for deployment and interoperability.
A minimal 200-line implementation of a sequence-to-sequence chatbot using TensorLayer and TensorFlow.
A deep learning library for Ruby that provides a native interface to LibTorch, enabling GPU-accelerated neural network development.
A Ruby deep learning library powered by LibTorch, providing a PyTorch-like API for Ruby developers.
A chess AI that learns to play chess using deep learning and neural networks.
A Ruby API for TensorFlow, enabling machine learning and deep learning within Ruby applications.
A comprehensive Swift framework providing AI/ML algorithms including neural networks, SVMs, genetic algorithms, and MDPs with GPU acceleration.
A comprehensive Swift framework providing AI/ML algorithms including neural networks, SVMs, PCA, genetic algorithms, and MDPs with GPU acceleration support.
A modular deep learning framework for PyTorch to build neural networks on heterogeneous tabular data.
A collection of tutorials and resources to help developers learn JAX, Flax, and Haiku for machine learning.
An object-oriented machine learning framework built on JAX, designed for simplicity and readability in research.
A pure-Java/C# machine learning framework for neural networks, genetic programming, and classic ML algorithms with simple adaptable source code.
A lightweight C library for building and training small to medium artificial neural networks with minimal dependencies.
Detects 6-DOF grasp poses for parallel jaw grippers in 3D point clouds, enabling robotic grasping of novel objects in clutter.
A JAX-powered library for solving large-scale optimal transport problems, including matching, barycenters, and neural approximations.
A Siamese neural network for LiDAR-based loop closing and localization by predicting scan overlap and relative yaw angle.
A lightweight Ruby playground with clean, readable implementations of core AI algorithms for learning and experimentation.
A lightweight Ruby playground with clean implementations of core AI algorithms for learning and experimentation.
A fast and flexible deep learning system with NumPy-like NDarray interface and easy multi-GPU support.
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