Open-source alternatives to Bayesian Machine Learning

Edward is a probabilistic programming language and inference engine designed for building deep generative models and Bayesian neural networks. It utilizes the TensorFlow framework to represent probabilistic models as differentiable computational graphs. The library enables the construction of complex data distributions through Bayesian neural networks, mixture models, and Gaussian processes. It differentiates itself by providing an integrated toolkit for both supervised and unsupervised probabilistic modeling, including the implementation of generative adversarial networks and mixture density

This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex

TensorFlow Probability is a library for probabilistic reasoning and statistical analysis integrated with the TensorFlow ecosystem. It serves as a Bayesian deep learning framework, a probabilistic programming interface, and a variational inference engine, providing a toolset for Markov chain Monte Carlo sampling and tensor-based probabilistic modeling. The project enables the construction of neural networks with probabilistic weights and the implementation of Bayesian neural networks to quantify prediction uncertainty. It provides specialized capabilities for hierarchical probabilistic modelin

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

This repository serves as an educational resource for Bayesian statistical modeling, providing a collection of instructional examples that translate theoretical concepts into executable Python code. It functions as a computational framework for performing statistical inference and parameter estimation, designed to help users learn and apply probabilistic programming techniques through interactive documentation. The project utilizes a probabilistic programming framework to define statistical models as directed acyclic graphs, enabling automated inference through advanced sampling algorithms. B

This is a Bayesian optimization library for Python designed to find the maximum value of expensive black box functions. It operates as a global optimizer that uses probabilistic models to identify the peak value of unknown functions through iterative sampling. The tool is specifically designed for hyperparameter tuning in machine learning, where it maximizes model performance while minimizing the number of required training runs. It treats the target function as a black box, selecting optimal input parameters based on statistical priors to reduce manual trial and error. The system utilizes G

This is a Python scientific computing library for finding the global maximum of expensive black-box functions. It operates as a global optimization framework that identifies optimal input parameters within defined bounds to maximize a target output. The library utilizes Gaussian process regression to predict function values and uncertainty, guiding the search for optimal parameters. It employs a surrogate-model optimization approach to approximate high-cost objective functions, reducing the total number of required evaluations. The system manages the trade-off between exploration and exploit

Neural Prophet is a PyTorch-based time series forecasting library designed for interpretable machine learning. It serves as a decomposition framework that breaks signals into constituent parts such as autoregressive effects, piecewise linear trends, and Fourier-based seasonality to predict future values. The project distinguishes itself by combining neural networks with traditional algorithms to produce forecasts that explain underlying trend drivers. It features a global time series modeling approach, allowing a single model to be trained across multiple simultaneous series to share learned

QuantResearch is a quantitative research framework and specialized toolkit for algorithmic simulation, financial time-series analysis, and systematic trading. It provides an event-driven backtesting environment for validating strategies against historical tick and bar data, alongside a dedicated portfolio optimization engine for calculating asset weights and risk metrics. The project distinguishes itself through a machine learning finance toolkit that implements recurrent neural networks for price prediction and reinforcement learning for derivative pricing. It also features advanced statisti

This project is a scientific computing framework for the .NET ecosystem, providing a comprehensive suite of libraries for numerical analysis, statistics, and mathematical optimization. It serves as a foundational toolkit for developing applications in machine learning, digital signal processing, and computer vision. The framework provides specialized toolkits for training and deploying predictive models, including neural networks, support vector machines, and decision trees. It further distinguishes itself with deep integrations for real-time visual analysis, such as object tracking and facia

This project is a collection of predictive models and quantitative tools for stock price forecasting. It implements a variety of machine learning architectures, including generative adversarial networks, long short-term memory networks, and language models for financial analysis. The system distinguishes itself by combining time-series forecasting with natural language processing to convert financial news into numerical sentiment scores. It also incorporates synthetic market data generation and automated hyperparameter optimization using Bayesian and reinforcement learning methods to reduce p

This project is a collection of scripts and workflows for training, fine-tuning, and deploying large language models using the Hugging Face Transformers toolkit. It functions as a distributed training framework, a library for natural language processing task implementations, and a system for building retrieval-augmented generation chatbots. The repository includes specialized tools for model optimization, such as a Bayesian hyperparameter optimizer for automatically tuning model settings. It provides implementations for scaling model training across multiple graphics processors using data par

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

This project is a comprehensive Chinese translation of a technical deep learning textbook, providing an educational resource on the theory and implementation of neural networks. It functions as a collaborative technical translation project designed to make complex academic AI literature accessible to non-English speakers. The project utilizes a community-driven translation model that integrates external suggestions and pull requests to refine linguistic accuracy and reduce bias. It employs standardized terminology mapping to ensure a uniform vocabulary throughout the translated content. To i

Kronos is a financial time-series forecasting framework and quantitative trading strategy simulator. It functions as a research environment designed to analyze historical market data, train predictive models, and evaluate the performance of automated trading signals. The platform distinguishes itself through its deep learning sequence predictors and probabilistic market modeling tools. By utilizing sequence-based architectures and statistical sampling, the system generates multiple potential price trajectories and volatility estimates to quantify uncertainty. It also supports transfer learnin

This project is a Python machine learning library and data science toolkit designed for building predictive models and analyzing complex datasets. It provides a collection of implementations for common supervised and unsupervised algorithms using the Scikit-Learn framework. The toolkit includes a predictive modeling suite for generating predictions from historical data and a statistical analysis framework for applying Bayesian modeling and causality tests. It also features a data visualization suite based on Matplotlib for rendering static charts and graphs to interpret classifier boundaries

Pyro is a deep probabilistic programming library and differentiable probabilistic modeler designed for Bayesian inference. It functions as a probabilistic programming language that allows for the construction of complex graphical models using PyTorch tensors and automatic differentiation. The framework enables the definition of universal probabilistic models as standard Python functions. It integrates deep learning with probabilistic modeling to compute posterior distributions and estimate latent variables through gradient-based optimization and algorithmic solvers. The system provides a pro

This is a generative AI model library containing a collection of PyTorch and TensorFlow implementations for creating synthetic data and modeling complex probability distributions. It serves as a multi-framework repository of deep learning models designed for learning and replicating data patterns. The project provides specialized implementation suites for several generative architectures. This includes Generative Adversarial Networks using competing generator and discriminator models, Variational Autoencoder frameworks that map data to a latent space, and Restricted Boltzmann Machine and Deep

TensorFlow-World is a collection of tutorials, implementation guides, and model templates for building and training machine learning models using the TensorFlow framework. It serves as an educational resource for designing deep learning architectures and implementing predictive models. The project provides ready-to-use examples for constructing neural network architectures and linear classifiers. It includes guides on performing tensor operations, automatic differentiation, and gradient descent optimization. The materials cover a range of machine learning capabilities, including the use of h

This project is a collection of educational notebooks and computational workflows designed for cheminformatics and molecular data science. It provides a structured environment for processing chemical structures, performing scaffold identification, and executing reaction enumeration through standardized data representations. The toolkit distinguishes itself by integrating statistical clustering and visualization techniques to interpret chemical diversity within large datasets. It supports advanced research workflows by enabling structure-activity relationship analysis and the evaluation of pro

This repository contains the digital textbook and supplementary materials for probabilistic machine learning education. It provides structured text and guided study materials covering the mathematical foundations of probability and neural networks. The project emphasizes reproducibility through a collection of interactive notebooks and standalone scripts used to recreate data plots and figures from the text. These materials are hosted in external environments to allow users to execute complex machine learning code without local installation. The educational surface includes lecture slides, e

Prophet is a time series forecasting library and decomposition tool that uses an additive regression model to predict future values. It functions as an uncertainty estimation tool, calculating confidence intervals and error metrics to quantify the risk associated with future predictions. The project is distinguished by its ability to incorporate human-interpretable parameters for model tuning and its use of Bayesian inference for parameter estimation. It supports the integration of external regressors and special event modeling to account for the impact of holidays and specific dates on forec

GluonTS is a probabilistic time series library and deep learning forecasting framework. It provides a toolkit for building, training, and evaluating neural network architectures that predict future values as probability distributions to quantify uncertainty. The project distinguishes itself by supporting zero-shot forecasting and integrating diverse modeling approaches, including deep probabilistic neural networks and wrappers for external statistical libraries such as Prophet and R forecast. It implements specialized architectural primitives like causal convolutions and invertible residual n

Guess is a predictive page loading library that uses machine learning to prefetch JavaScript bundles and assets. It functions as a resource prefetcher that predicts the next page a user will visit by utilizing a web application route parser and a user behavior analytics integrator. The project distinguishes itself by importing navigation patterns from analytics APIs to inform its predictive models. It uses probabilistic navigation modeling and historical transition data to calculate the likelihood of future page visits, allowing for the proactive download of lazy-loaded bundles. The system i

GluonTS is a framework for probabilistic time series forecasting, designed to predict future values as probability distributions with confidence intervals. It supports both traditional model training and zero-shot forecasting, where pretrained models generate predictions for new series without additional training. The project distinguishes itself by integrating a wide variety of forecasting approaches into a unified workflow. This includes deep learning architectures such as recurrent neural networks and causal convolutions, as well as the integration of external statistical models, the Proph

This project is a Python data analysis library and exploratory data analysis framework designed for processing raw datasets. It provides a suite of tools for examining data, identifying anomalies, and applying statistical methods to uncover patterns. The repository functions as a machine learning modeling toolkit and a statistical data modeling suite. It includes predictive algorithms and mathematical models used to analyze relationships between data variables and derive insights from complex datasets. The project covers a broad range of capabilities including data science, machine learning

This library is a collection of machine learning algorithms and neural network components implemented from scratch using only NumPy. It serves as an educational toolkit for constructing and experimenting with machine learning architectures, emphasizing a modular approach where algorithms are organized into self-contained, object-oriented classes. The project distinguishes itself by relying exclusively on array-oriented programming to perform mathematical operations, ensuring that all computations are vectorized for performance. By utilizing a standardized interface for forward and backward pa