Neural Networks
Showing 36 of 428 projects
A pure-Java/C# machine learning framework for neural networks, genetic programming, and classic ML algorithms with simple adaptable source code.
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 implementations of core AI algorithms for learning and experimentation.
A lightweight Ruby playground with clean, readable 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.
An open-source machine learning system for training autonomous RC cars using computer vision and neural networks.
TensorFlow port for AMD GPUs via ROCm, enabling machine learning on Radeon hardware.
A Python library built on JAX for studying many-body quantum systems using neural networks and machine learning.
An open-source implementation reproducing DeepMind's Atari-playing deep reinforcement learning system from their seminal 2013 paper.
A curated list of awesome Torch tutorials, projects, libraries, and communities for deep learning.
A Common Lisp machine learning library focusing on neural networks, Boltzmann machines, and Gaussian processes with BLAS and CUDA support.
A free Google Colab-based toolbox with Jupyter notebooks and GUI for applying deep learning to microscopy data without coding expertise.
A JAX library for nonlinear optimization including root finding, minimization, fixed points, and least squares.
A collection of TensorFlow practice exercises covering fundamental machine learning concepts from linear regression to CNNs.
A quantization extension for Keras that provides drop-in replacement layers for creating quantized deep learning models in TensorFlow.
A simplified Keras-like framework for PyTorch that reduces boilerplate code for training neural networks.
TensorFlow implementation of R-Net for machine reading comprehension on the SQuAD dataset.
A Go library implementing feed-forward and Elman recurrent neural networks for machine learning tasks.
A command-line tool for neural network inference using Unix pipeline philosophy.
Autograd automatically differentiates native Torch code, enabling automatic gradient computation for machine learning models.
A Python library implementing Self-Organizing Maps (SOM) with batch training, PCA initialization, and visualization tools.
A curated list of popular deep learning models for image classification, segmentation, and detection with key performance metrics.
A Flutter plugin providing fast, flexible TensorFlow Lite inference with multi-platform delegate support.
An AutoML framework that generates and customizes machine learning pipelines using declarative JSON-AI syntax.
Ruby gem providing bindings to FANN (Fast Artificial Neural Network) for building and training neural networks.
A fast C++ GPU implementation of Convolutional Neural Networks with multi-GPU support.
A curated list of resources dedicated to Natural Language Generation (NLG), including datasets, libraries, tools, and research.
A curated list of resources dedicated to Natural Language Generation (NLG), including datasets, libraries, tools, and research.
A parallel deep learning framework written in modern Fortran for training and inference of dense, convolutional, and transformer networks.
A fast Clojure library for tensor operations and deep learning with optimized CPU/GPU support.
A JavaScript library and workspace for building and experimenting with dynamic neural network architectures.
A JavaScript library and workspace for building and experimenting with dynamic neural network architectures.
A PyTorch library for creating and training autoencoders on sequential data (time series, videos, etc.) in just two lines of code.
A neural network for object detection using multi-level fusion of camera and radar data, built on Keras RetinaNet.
OCaml bindings for PyTorch, providing NumPy-like tensor computations with GPU acceleration and automatic differentiation.
A Pascal-based deep learning neural network API optimized for AVX/AVX2/AVX512 and OpenCL, supporting AMD, Intel, and NVIDIA hardware.
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