Awesome GitHub RepositoriesDifferentiable Programming

Kornia is a differentiable computer vision library and cross-framework tensor vision toolset. It implements vision operations as differentiable tensors to enable integration into deep learning pipelines and supports the transpilation of operations across PyTorch, TensorFlow, JAX, and NumPy. The project provides specialized toolsets for geometric vision and stereo depth, including algorithms for 3D scene reconstruction, camera calibration, and pose estimation. It further distinguishes itself as a differentiable image augmentation framework, applying random geometric and color transformations w

This project is a static educational website and comprehensive curriculum focused on computer vision and deep learning. It serves as a public repository of instructional materials, lecture notes, and technical guides specifically detailing convolutional neural networks and visual recognition. The site is developed using static-site generation to host course documentation and student project directories. It provides structured academic resources that guide learners through image classification, generative modeling, and the implementation of various neural network architectures. The curriculum

The PyTorch Tutorials repository is a collection of educational resources that provides step-by-step guidance on building, training, and deploying neural networks using the PyTorch framework. It covers the complete machine learning workflow, from data loading and model definition through optimization loops and model persistence, with dedicated guides for distributed training, model fine-tuning, and deployment. The tutorials offer practical demonstrations of adapting pre-trained models to new tasks through transfer learning, scaling training across multiple GPUs or machines using PyTorch's dis

This project is a comprehensive collection of educational examples and reference implementations for building vision and language models using PyTorch. It serves as a deep learning tutorial covering the end-to-end process of developing neural networks, from initial architecture definition to final production deployment. The repository provides detailed guides on implementing a wide range of domain-specific models, including convolutional neural networks for object detection and segmentation, as well as transformer and recurrent architectures for natural language processing. It emphasizes gene

Warp is a Python framework that JIT-compiles Python functions into CUDA kernels for GPU-accelerated parallel computation, with built-in automatic differentiation and multi-framework array interoperability. At its core, it provides a GPU kernel compilation system that enables writing and executing custom GPU kernels directly from Python, while supporting automatic gradient computation through those kernels for integration with machine learning pipelines. The framework also includes tile-based cooperative computing, where thread blocks partition into tiles for shared-memory and tensor-core opera

Swift for TensorFlow is a custom toolchain that extends the Swift language with first-class automatic differentiation and differentiable types, enabling gradient-based computation directly within the compiler. It integrates the Swift compiler with TensorFlow runtime and XLA backends, allowing tensor operations to be compiled and executed on hardware-accelerated hardware for high-performance machine learning. The project distinguishes itself through compiler-integrated automatic differentiation that computes gradients of user-defined functions and types during compilation, eliminating the need

MegEngine ist ein Deep-Learning-Framework und eine Engine für automatische Differenzierung, die zum Trainieren und Bereitstellen neuronaler Netze verwendet wird. Sie fungiert als Bibliothek für differenzierbare Programmierung, die die Erstellung mathematischer Modelle ermöglicht, bei denen Operationen für gradientenbasierte Optimierung differenzierbar sind. Das Projekt bietet eine Hardware-agnostische Tensor-Runtime und eine plattformübergreifende Modell-Runtime, die es Modellen ermöglicht, über diverse CPU- und GPU-Hardwarearchitekturen hinweg ausgeführt zu werden. Es nutzt eine dynamische Computational-Graph-Engine, um Ausführungsgraphen zur Laufzeit zu erstellen, und unterstützt flexible Eingabeformen sowie komplexe Kontrollflüsse. Das Framework deckt den gesamten KI-Modell-Lebenszyklus ab, vom iterativen Modelltraining und der Validierung bis hin zum plattformübergreifenden Deployment. Es integriert eine Pipeline für automatische Differenzierung zur Berechnung von Gradienten und bietet Tools zum Exportieren trainierter Modelle, um diese effizient über verschiedene Hardwareplattformen hinweg auszuführen.

MultiNeRF is a 3D scene reconstruction suite and framework for training Neural Radiance Fields to synthesize novel views from sets of 2D images. It provides a system for generating new perspectives of a scene by optimizing a neural network based on images and camera poses. The toolkit includes research implementations such as Mip-NeRF 360 and Ref-NeRF for high-fidelity volumetric rendering. It features a structure-from-motion pipeline to calculate camera positions and orientations from image datasets to prepare data for training. The project covers a full workflow for volumetric rendering, i