Neural Networks
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An open-source machine learning framework built in Rust for high-performance and extensible ML tasks.
An open-source machine learning framework built in Rust for high-performance and extensible ML tasks.
A practical demo using LSTM neural networks with TensorFlow to predict lottery numbers.
Interactive visualization tool for understanding Capsule Network (CapsNet) layers and their internal workings.
Convert Torch7 neural network models to Apple CoreML format for deployment on iOS/macOS devices.
A purely functional artificial neural network library for Haskell, enabling rapid prototyping through higher-order function composition.
A neural network playground built in pure Swift for iOS, with no third-party dependencies.
A neural networks toolbox for medical image analysis, providing specialized layers, models, and utilities for TensorFlow/Keras.
A high-level machine learning library for Go with a Keras-like API, built on Gorgonia.
A reinforcement learning framework for de novo drug design that generates novel molecular structures with desired properties.
An easy-to-use C# deep learning library with support for multiple backends including TensorFlow, PyTorch, and CUDA/OpenCL.
A curated list of open-source neural machine translation implementations across various deep learning frameworks.
A Python package for training PyTorch neural networks using variational inference for Bayesian deep learning.
A curated list of deep learning resources for 3D shape processing, covering classification, reconstruction, and generation.
An open-source toolkit for building end-to-end trainable task-oriented dialogue models with neural networks.
A TensorFlow implementation of End-To-End Memory Networks with a scikit-learn-like interface for bAbI tasks.
A Swift library providing neural networks, machine learning algorithms, and AI data structures for iOS and macOS development.
A deep learning library for streamlining research and development using Torch7 with object-oriented design patterns.
A JAX library for second-order optimization of neural networks using the K-FAC curvature approximation algorithm.
Tensorflow bindings for the Elixir programming language, enabling machine learning inference and tensor operations.
A Torch package for creating and visualizing complex neural network architectures using graph-based computation.
OCaml bindings for TensorFlow, enabling machine learning and neural network development in a functional programming environment.
A Clojure library for dynamic neural network graphs with pluggable tensor backends, inspired by PyTorch.
A lightweight neural network library for Deno with CPU, GPU, and WASM backends, designed for serverless and edge environments.
A curated list of open-source software tools for medical imaging research, including segmentation, visualization, and deep learning libraries.
A comprehensive scientific computing and AI/ML library in pure Rust, offering SciPy-compatible APIs with 10-100x performance gains.
A deep learning model that reads IRCTC captchas with 98% accuracy, demonstrating their vulnerability to automated booking.
CUDA backend implementation for Torch's neural network package, enabling GPU acceleration for deep learning models.
A proof-of-concept neural network library in Rust with implementations for MNIST digit recognition and char-rnn LSTM models.
A statistical natural language generator for spoken dialogue systems, supporting both A*-search and seq2seq algorithms.
An artificial life simulation system that evolves neural networks for artificial intelligence research.
A vision transformer architecture that aggregates nested local transformers on image blocks for better accuracy, data efficiency, and convergence.
A Go library implementing essential machine learning algorithms including linear regression, logistic regression, and neural networks.
A layer library for JAX-based machine learning projects, optimized for large-scale ML.
A pure Crystal machine learning library for building and training neural networks with CPU/GPU support and PyTorch compatibility.
A tool for calibrating event cameras by converting event data to images and using standard image-based calibration toolboxes.
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