Neural Networks
Showing 32 of 428 projects
A personal learning repository for implementing neural networks and machine learning models using Torch in Lua.
Profile TensorFlow Lite and OpenCV DNN models on Android devices with performance metrics and delegate options.
An open-source JavaScript library implementing machine learning algorithms for educational purposes with interactive visualizations.
NALP is a Python library for natural language processing and adversarial learning, from embeddings to neural networks.
A neural network-based tool that suggests lemma names for Coq verification projects by analyzing serialized statements and elaborated terms.
A scikit-learn pipeline implementing the projection layer of Self-Governing Neural Networks (SGNN) using character n-grams and random hashing.
A deep learning-based password cracking tool that uses adaptive dynamic mangling rules to reduce bias in real-world password strength modeling.
MXNet bindings for the Crystal programming language, enabling deep learning and machine learning development.
A comprehensive Ruby gem providing atomic, flexible tools and frameworks for practical machine learning applications.
A comprehensive Ruby gem providing a collection of atomic machine learning tools and frameworks for practical applications.
An embedded deep learning library for Go designed for educational exploration of neural network fundamentals.
A Haskell implementation of Hopfield networks for unsupervised learning and pattern storage.
NNStreamer extension plugins that enable neural network pipelines to integrate with ROS and ROS2 for robotics applications.
An iOS implementation of the Hebbian algorithm for unsupervised self-organizing machine learning.
A Torch-like deep learning framework for JavaScript with direct tensor and autograd operations.
A neural network system that generates English Wikipedia-style biographies from Semantic Web triples using encoder-decoder models.
A TensorFlow implementation of Dynamic Capacity Networks, which reduces computations by applying high-capacity networks to selected input patches.
A JavaScript-native machine learning framework with PyTorch-aligned APIs, built from scratch on WebGPU for dynamic graph execution and model interpretability.
A Rust crate providing embeddings and positional encoding implementations for NLP and Transformer-based models.
A monadic implementation of fully-connected neural networks in OCaml with backpropagation and customizable hyperparameters.
An autograd and GPGPU library for building dynamic neural networks in the D programming language.
A Go library for building and training deep neural networks, powered by MXNet.
A simple interface for converting ROOT nTuples to NumPy arrays and using them in TensorFlow for neural network training.
A fast Single Layer Perceptron library for Deno, written in Rust and TypeScript with Foreign Function Interface (FFI).
A multi-channel neural network for predicting compound-protein interactions using molecular and protein sequence embeddings.
A collection of plug-and-play TensorFlow/Keras deep learning models and architectures for easy integration.
A JAX/Flax implementation of the RAFT optical flow estimator with ported checkpoints and reproducible results.
A Ruby library for designing, processing, and training artificial neural networks.
A neural networks library for Clojure with support for custom activation functions, serialization, and data preprocessing.
Convert deep neural network models from Apache MXNet Gluon to Keras format for interoperability between frameworks.
Graph embedding framework implementing TransE, TransH, TransR, TransD, and TransSparse models for knowledge graph representation learning.
A Python algorithm for smoothly blending U-Net image segmentation patches using spline interpolation and batch prediction.
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