Open-source alternatives to Deepxde

DifferentialEquations.jl is a comprehensive numerical library designed for solving ordinary, stochastic, delay, and algebraic differential equations. It functions as a high-performance solver suite that integrates scientific machine learning, probabilistic programming, and automated differentiation into a unified framework. By leveraging multiple dispatch and symbolic-numeric integration, the library provides a flexible environment for complex mathematical modeling and simulation. The project distinguishes itself through its ability to bridge traditional numerical analysis with modern machine

Neuraloperator is a library for learning mappings between infinite-dimensional function spaces, serving as a tool to accelerate physics simulations and partial differential equation solving. It implements resolution-invariant models and spectral neural networks that can produce consistent predictions regardless of the input grid resolution or spatial discretization. The framework incorporates physics-informed neural networks that enforce physical constraints and differential equations through specialized loss functions. It utilizes Fourier transforms and spectral projections to process multid

This project is a Rust interface for the PyTorch C++ library, serving as a deep learning framework and tensor computing library. It functions as a C++ API wrapper that enables the manipulation of multi-dimensional arrays and the execution of neural network architectures across CPU and GPU hardware accelerators. The library provides a TorchScript inference engine to load and execute just-in-time compiled models. It also supports Rust and Python interoperability, allowing for the creation of Python extensions that share tensor data through a common interface. The system covers deep learning mo

Flashlight is a C++ machine learning library and deep learning framework designed for building and training neural networks. It functions as a tensor manipulation library and an automatic differentiation engine that tracks operations to calculate gradients via backpropagation for model optimization. The project is distinguished by its role as a distributed training framework, utilizing all-reduce gradient synchronization and distributed environments to scale machine learning workloads across multiple nodes and devices. It features a backend-agnostic memory interface and RAII-based management

Keras is a high-level deep learning API used to design, build, and train neural networks for tasks such as computer vision, natural language processing, and time series forecasting. It provides a framework for defining model architectures and optimizing weights through a structured interface. The project is defined by a backend-agnostic design that allows the same model code to run across different compute engines. This multi-backend execution enables users to swap underlying engines to optimize for specific hardware or performance requirements. The system supports distributed model training

This project is a comprehensive educational resource and tutorial handbook for building, training, and deploying machine learning models using TensorFlow 2. It serves as a structured learning guide covering core deep learning concepts, including neural network architectures, automatic differentiation, and tensor operations. The handbook provides technical guidance on optimizing execution efficiency through GPU memory management, distributed training, and model quantization. It also includes detailed manuals for constructing high-performance data pipelines and exporting models for production s

This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi

TensorFlow.js is a JavaScript machine learning library and browser-based runtime used to build, train, and execute models. It functions as a WebGL accelerated tensor engine, providing a foundation for high-performance linear algebra operations and an automatic differentiation framework for computing gradients. The project distinguishes itself through its ability to run machine learning directly in web environments, supporting both client-side inference and browser-based training. It enables the deployment of Python-based models by converting Keras or TensorFlow models into compatible formats

This project is a machine learning educational curriculum and learning platform delivered through interactive Jupyter Notebooks. It serves as a comprehensive guide for mastering the Python data science toolkit, providing structured tutorials for numerical computing, tabular data manipulation, and statistical visualization. The curriculum includes specific implementation guides for Scikit-Learn and a practical course on TensorFlow for constructing, training, and deploying neural networks and computer vision models. It covers the end-to-end process of building predictive models, from initial pr

CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression, classification, and ranking tasks. It functions as a high-performance framework that provides a categorical data processor for transforming non-numeric features, a distributed trainer for large-scale datasets, and GPU acceleration to speed up model construction. The library distinguishes itself through native handling of categorical data and text features, removing the need for manual encoding. It includes a specialized model interpretability tool that leverages SHAP values and featu

Flashlight is a standalone C++ machine learning library and tensor library used for building and training neural networks. It functions as a comprehensive neural network framework and automatic differentiation engine, providing the tools to construct computation graphs and calculate gradients via backpropagation. The project serves as a distributed training framework, utilizing all-reduce operations to synchronize gradients and parameters across multiple compute nodes and devices. It distinguishes itself through deep integration of high-performance tensor manipulation, native device memory in

Trax is a deep learning framework and hardware-agnostic tensor engine designed for designing and training neural networks. It serves as a research tool providing high-level combinators for composing complex architectures, alongside a dedicated library for building transformer models and a toolkit for reinforcement learning. The framework is distinguished by its support for reversible and sparse transformer architectures, which reduce memory and computational overhead. It enables a single set of model instructions to execute across different hardware backends without changing the underlying co

Theano is a Python mathematical expression compiler and symbolic math library used as a deep learning backend. It functions as a tensors computation framework that translates mathematical formulas into optimized C or CUDA code for high-performance computing. The system manages the definition and evaluation of complex math formulas using multi-dimensional arrays. It employs a symbolic expression graph and a lazy evaluation engine to optimize mathematical expressions before they are compiled into executable code. The framework provides automatic differentiation for calculating gradients of mat

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

Orange3 is a visual data mining platform that provides an interactive canvas for building data analysis workflows without writing code. At its core, it offers a widget-based visual programming environment where users connect configurable components to perform data preprocessing, machine learning model training, statistical evaluation, and interactive visualization. The platform is built on NumPy-backed data tables with domain descriptors that define variable names, types, and roles, and includes a lazy SQL query proxy for working with database tables without loading all data into memory. The

POT is an optimal transport library providing a collection of solvers for computing Wasserstein, Gromov-Wasserstein, and Fused Gromov-Wasserstein distances between probability distributions. It functions as a differentiable tensor framework that integrates with various tensor libraries to enable automatic differentiation and GPU acceleration. The project is distinguished by its ability to align data distributions across different metric spaces by comparing internal relational structures rather than coordinates. It implements mathematical optimization algorithms as differentiable layers, allow

PredictionIO is a machine learning server designed for the deployment of predictive models to transform raw data into actionable predictions. It manages the full lifecycle of machine learning operations, from ingesting event data via APIs to hosting production-ready predictive services for real-time inference. The system supports distributed model training by spreading computational workloads across a cluster of nodes to increase processing speed. It enables the implementation of custom prediction engines using programming languages or the application of pre-built model templates for common t

This project is a comprehensive educational resource and curriculum focused on the design and implementation of the full machine learning software and hardware stack. It serves as a technical reference for architecting machine learning systems, spanning from low-level programming interfaces to large-scale deployment infrastructure. The project provides instructional guidance on several specialized domains, including the development of AI compilers through intermediate representations and graph optimizations. It covers the architectural patterns required for distributed training across GPU clu

Flux.jl is a deep learning framework and numerical computing toolkit written in Julia. It serves as a machine learning library for designing and training neural networks, providing a system for automatic differentiation to optimize model parameters. The framework enables deep learning development and machine learning research by representing layers as parameterized functions. It supports scientific machine learning, integrating neural networks into workflows for solving physical and mathematical problems. The toolkit provides native GPU acceleration for tensor computations and utilizes rever

This project is a framework for fine-tuning large language models using parameter-efficient training techniques. It provides a structured pipeline for adapting pre-trained transformer models to specific tasks while minimizing the computational resources and memory required during the training process. The system distinguishes itself by utilizing low-rank adaptation, which injects trainable rank-decomposition matrices into frozen transformer layers. By updating only this small subset of injected parameters rather than the entire model, the framework reduces the overhead associated with gradien

This project is a comprehensive collection of practical code examples and implementation libraries for machine learning. It provides a wide array of reference materials for building supervised, unsupervised, and reinforcement learning algorithms. The repository serves as a multi-domain resource, featuring specific implementation suites for financial AI, Bayesian statistical modeling, and deep learning architectures. It includes a framework for training intelligent agents using policy gradients and actor-critic models, as well as practical guides for fine-tuning transformers and utilizing larg

LightGBM is a gradient boosting framework used to train decision tree ensembles for classification, regression, and ranking tasks. It functions as a distributed machine learning library and a decision tree ensemble implementation that utilizes leaf-wise growth and histogram-based feature binning. The framework is distinguished by its ability to offload heavy computations to CUDA or OpenCL devices for GPU acceleration and its capacity to parallelize training across multiple nodes using sockets, MPI, or Dask. It includes a specialized categorical feature processor that optimizes partitions for

ESPnet is a comprehensive speech processing toolkit and PyTorch-based trainer designed for building end-to-end speech recognition, synthesis, and translation models. It provides a structured framework for developing automatic speech recognition systems using transducer and encoder-decoder architectures, alongside engines for text-to-speech synthesis and speech translation pipelines. The project distinguishes itself through a recipe-based workflow execution system that ensures experimental reproducibility by running standardized sequences of scripts for data preparation and model training. It

MMSegmentation is an open-source semantic segmentation toolbox built on PyTorch that provides a modular, configurable framework for building, training, evaluating, and deploying segmentation models. At its core, it offers a config-driven pipeline that assembles training, evaluation, and inference workflows by parsing hierarchical configuration files, with a modular component registry that enables plug-and-play composition of neural network modules, optimizers, datasets, and metrics. The framework supports the full model lifecycle through a unified runner interface that controls training, testi

ai-edu is a comprehensive AI education curriculum and machine learning courseware collection. It provides theoretical tutorials, deep learning lab exercises, and project blueprints designed to teach artificial intelligence fundamentals through a combination of study and practical implementation. The project focuses on a learning-by-doing approach, guiding users from Python programming and neural network basics to advanced topics. It includes specialized instructional content on distributed AI training, MLOps educational guides for model quantization and pruning, and detailed frameworks for im

This project is a Chinese language translation of the technical guides and API references for the PyTorch deep learning framework. It serves as a localized knowledge base and reference material to make deep learning documentation accessible to non-English speakers. The documentation covers a comprehensive range of PyTorch capabilities, including neural network model development, automatic differentiation, and the implementation of backend kernels. It provides detailed guidance on distributed training strategies, model deployment through formats like ONNX and C++, and various model optimizatio

The TensorFlow Cookbook is a collection of code examples and recipes for building, training, and deploying machine learning models using TensorFlow. It covers the full model lifecycle, from constructing neural networks and training them with configurable parameters to packaging trained models for production deployment with unit tests and multi-device support. The project also integrates TensorBoard for logging and visualizing computational graphs, scalar summaries, and histograms during training. The cookbook demonstrates a wide range of machine learning techniques, including convolutional ne