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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
PyMC is a Bayesian probabilistic programming framework used for building probabilistic models and performing Bayesian inference. It provides a probabilistic graphical model library for specifying random variables, priors, and likelihood functions, supported by an MCMC sampling engine and variational inference tools to estimate posterior distributions. The framework features a GPU-accelerated inference backend that compiles models into machine code to increase execution speed. It utilizes a backend-agnostic tensor execution model and just-in-time graph compilation to optimize the computation o
This project is a computational statistics textbook and Bayesian data analysis course. It serves as a guide for performing statistical inference and quantifying uncertainty through a probabilistic programming workflow using Python. The resource employs a computation-first pedagogy, teaching Bayesian methods and parameter estimation through executable code and simulations instead of formal mathematical notation. It provides a practical approach to implementing Markov Chain Monte Carlo sampling to estimate posterior distributions. The content covers building probabilistic models, integrating e
Linfa is a classical machine learning framework and statistical learning suite implemented in Rust. It provides a collection of algorithms for supervised and unsupervised learning, focused on traditional statistical methods such as regression, clustering, and decision trees. The toolkit is distinguished by its ability to be compiled into WebAssembly, enabling analytical models to execute within browser environments. It employs a trait-based algorithm interface to standardize the process of training and prediction across its various models. The library covers a broad range of capabilities, in
Lihang is a statistical learning algorithm library and framework providing implementations of supervised and unsupervised machine learning models. It functions as a reference repository that translates statistical learning theories into executable code for data classification and pattern recognition.
The main features of smirkcao/lihang are: Statistical Learning Implementations, Parameter Estimation Methods, Probabilistic Modeling, Supervised Learning Frameworks, Classification Boundaries, Sequential Pattern Analysis, Sequence Labeling, Sequential Data Classification.
Projects with overlapping indexed features include: d2l-ai/d2l-en — This project is an educational platform and research toolkit designed to teach deep learning through a combination of… pymc-devs/pymc — PyMC is a Bayesian probabilistic programming framework used for building probabilistic models and performing Bayesian… camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers — This project is a computational statistics textbook and Bayesian data analysis course. It serves as a guide for… rust-ml/linfa — Linfa is a classical machine learning framework and statistical learning suite implemented in Rust. It provides a… lawlite19/machinelearning_python — This is a Python machine learning library featuring a collection of core algorithms implemented from scratch to… rasbt/machine-learning-book — This project is a comprehensive machine learning educational resource and tutorial series delivered as a collection of…