Deep Neural Networks
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An end-to-end open source platform for machine learning with a comprehensive ecosystem of tools and libraries.
An end-to-end open source platform for machine learning with a comprehensive ecosystem of tools and libraries.
A curated list of the top 100 most cited deep learning papers from 2012-2016, serving as a foundational reading list.
LaTeX code and Python interface for creating publication-quality neural network architecture diagrams.
An open standard format for representing machine learning models to enable interoperability between frameworks.
A topic-wise curated list of machine learning and deep learning tutorials, articles, and resources for developers and data scientists.
A curated collection of tutorials, articles, and resources for learning machine learning and deep learning topics.
A unified deep learning toolkit for describing neural networks as computational graphs, supporting feed-forward DNNs, CNNs, and RNNs/LSTMs.
Offline speech recognition toolkit supporting 20+ languages with small models and streaming API.
A comprehensive resource of deep learning techniques and models for analyzing satellite and aerial imagery.
WebGL-accelerated machine learning library for JavaScript with linear algebra and automatic differentiation.
A lightweight, modular, and scalable deep learning framework built on the original Caffe.
Go language bindings for OpenCV 4, enabling computer vision applications with support for CUDA, DNN, and OpenVINO.
A TensorFlow implementation of YOLO for real-time object detection, supporting weight conversion, training, and mobile deployment.
An engine-agnostic deep learning framework for Java developers, providing a high-level API for model training and inference.
An open-source cross-platform performance library of basic building blocks for deep learning applications, optimized for CPUs and GPUs.
A collection of handwritten notes, notebooks, and resources for Andrew Ng's Deep Learning Specialization on Coursera.
A JAX-based neural network library that provides a simple, object-oriented programming model for building and training models.
A curated list of scientific articles, theses, and reports on deep learning applied to music information retrieval and generation.
A curated collection of academic papers, code, and resources for learning with noisy labels in machine learning.
Deep neural network to extract structured information from invoice documents with a customizable UI and training tools.
A framework for running deep neural network models directly in web browsers using ONNX format with WebGPU, WebGL, and WebAssembly backends.
An abstraction layer over MetalPerformanceShaders for crafting and running fast neural networks on iOS using TensorFlow models.
An example Android project demonstrating how to build and integrate TensorFlow for object detection using the camera.
A deep learning framework for feature learning directly from point clouds using X-Conv operations, achieving state-of-the-art results in classification and segmentation.
A deep learning toolkit for computational chemistry and drug design research with PyTorch backend.
A learning-based approach for moving object segmentation in 3D LiDAR data, distinguishing moving vs. static objects in real-time.
A convolutional neural network model for real-time road-object segmentation from 3D LiDAR point clouds.
A fast Clojure library for tensor operations and deep learning with optimized CPU/GPU support.
A lightweight neural network library for Deno with CPU, GPU, and WASM backends, designed for serverless and edge environments.
A pure Crystal machine learning library for building and training neural networks with CPU/GPU support and PyTorch compatibility.
An open-source CAD framework for designing, simulating, and deploying deep neural networks on embedded platforms.
A type-safe, functional ONNX API and backend for deep learning and classical machine learning in Scala 3.
A symbolic programming library built on JAX for concise, explicit, and optimized machine learning computations.
A Swift library for accelerated tensor operations and dynamic neural networks with automatic differentiation, supporting all Apple platforms and Linux.
A city-scale dataset and platform for learning holistic 3D structures from panoramic and perspective imagery with detailed annotations.
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