Category Theory: The Beginner’s Introduction
Sets for Mathematics in nLab
deduction system, natural deduction, sequent calculus, lambda-calculus, judgment
panproto | schematic version control
One engine for schematic version control within and across any schema language. Built on generalized algebraic theories for provably correct migrations.
GATlab: Modeling and Programming with Generalized Algebraic Theories
Categories and categorical structures are increasingly recognized as useful abstractions for modeling in science and engineering. To uniformly implement category-theoretic mathematical models in software, we introduce GATlab, a domain-specific language for algebraic specification embedded in a technical programming language. GATlab is based on generalized algebraic theories (GATs), a logical system extending algebraic theories with dependent types so as to encompass category theory. Using GATlab, the programmer can specify generalized algebraic theories and their models, including both free models, based on symbolic expressions, and computational models, defined by arbitrary code in the host language. Moreover, the programmer can define maps between theories and use them to declaratively migrate models of one theory to models of another. In short, GATlab aims to provide a unified environment for both computer algebra and software interface design with generalized algebraic theories. In this paper, we describe the design, implementation, and applications of GATlab.
Applied Category Theory for Engineering
This site is a place to make available some resources on compositionality and engineering, and it is maintained by the Frazzoli group at ETH Zurich and by the Zardini group at Massachusetts Institute of Technology. It is skewed towards our particular knowledge and interests (applied category theory for robotics and the co-design of complex engineering systems), however we hope it may be useful to a broad range of people working on compositional methods in engineering, computer science, the natural sciences, and mathematics.
Categories for Machine Learning
This seminar series seeks to promote the learning and use of Category Theory by Machine Learning Researchers
F. William Lawvere, Stephen H. Schanuel Conceptual Mathematics A First Introduction To Categories ( 2009, Cambridge University Press) ( 1)
Category Theory first introduction
Seven Sketches in Compositionality: An Invitation to Applied Category Theory
This book is an invitation to discover advanced topics in category theory through concrete, real-world examples. It aims to give a tour: a gentle, quick introduction to guide later exploration. The tour takes place over seven sketches, each pairing an evocative application, such as databases, electric circuits, or dynamical systems, with the exploration of a categorical structure, such as adjoint functors, enriched categories, or toposes. No prior knowledge of category theory is assumed. A feedback form for typos, comments, questions, and suggestions is available here: https://docs.google.com/document/d/160G9OFcP5DWT8Stn7TxdVx83DJnnf7d5GML0_FOD5Wg/edit
Category Theory for Programmers: The Preface
Table of Contents Part One Category: The Essence of Composition Types and Functions Categories Great and Small Kleisli Categories Products and Coproducts Simple Algebraic Data Types Functors Functo…
