Search Results for "calculus"
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Showing 13 open source projects for "calculus"
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Machine Learning Foundations
Machine Learning Foundations: Linear Algebra, Calculus, Statistics
...The project focuses on explaining the fundamental mathematical and computational concepts that underpin modern machine learning and artificial intelligence systems. The materials cover essential topics such as linear algebra, calculus, statistics, and probability, which form the theoretical basis of many machine learning algorithms. The repository includes Jupyter notebooks with explanations and examples that demonstrate how these mathematical principles relate to real machine learning applications. Each section introduces theoretical concepts and then illustrates them through practical coding examples to reinforce understanding. ... -
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DoWhy
DoWhy is a Python library for causal inference
...DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes. -
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Deep Learning Is Nothing
Deep learning concepts in an approachable style
Deep-Learning-Is-Nothing presents deep learning concepts in an approachable, from-scratch style that demystifies the stack behind modern models. It typically begins with linear algebra, calculus, and optimization refreshers before moving to perceptrons, multilayer networks, and gradient-based training. Implementations favor small, readable examples—often NumPy first—to show how forward and backward passes work without depending solely on high-level frameworks. Once the fundamentals are clear, the material extends to CNNs, RNNs, and attention mechanisms, explaining why each architecture suits particular tasks. ... -
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NeuroMatch Academy (NMA)
NMA Computational Neuroscience course
...You will learn how to code in Python from scratch using a simple neural model, the leaky integrate-and-fire model, as a motivation. Then, you will cover linear algebra, calculus and probability & statistics. The topics covered on these days were carefully chosen based on what you need for the comp neuro course. -
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DeepSeek Math
Pushing the Limits of Mathematical Reasoning in Open Language Models
...MATH, GSM8K, ARB), demonstration notebooks, prompt templates, and evaluation results on math benchmarks. The goal is to push DeepSeek’s performance in domains that require rigorous symbolic steps, calculus, linear algebra, number theory, or multi-step derivations. The repo may also include modules that integrate external computational tools (e.g. a CAS / computer algebra system) or calculator assistance backends to enhance correctness. Because math reasoning is a high bar for LLMs, DeepSeek-Math aims to showcase their model’s ability not just in natural text but in precise formal reasoning. -
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CausalNex
A Python library that helps data scientists to infer causation
CausalNex is a Python library that uses Bayesian Networks to combine machine learning and domain expertise for causal reasoning. You can use CausalNex to uncover structural relationships in your data, learn complex distributions, and observe the effect of potential interventions. -
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Machine Learning cheatsheets Stanford
VIP cheatsheets for Stanford's CS 229 Machine Learning
...In addition to machine learning algorithms, it also contains refresher materials on mathematical prerequisites including probability theory, statistics, linear algebra, and calculus. These cheat sheets are designed to serve as quick reference guides that students can use while studying or reviewing machine learning material. -
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micrograd
A tiny scalar-valued autograd engine and a neural net library
...The repository is intentionally compact and readable, prioritizing clarity over performance so learners can follow every step of gradient flow and parameter updates. It is commonly used as a learning bridge between basic calculus intuition and full-scale deep learning frameworks, helping developers understand why autodiff libraries behave the way they do. -
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Diff Zoo
Differentiation for Hackers
Diff-zoo is a learning-focused handbook designed to demystify algorithmic differentiation (AD), the core technique powering modern machine learning frameworks. The project introduces AD from a foundational calculus perspective and gradually builds towards toy implementations that resemble systems like PyTorch and TensorFlow. It clarifies the differences and connections between forward mode, reverse mode, symbolic, numeric, tracing, and source transformation approaches to differentiation. Unlike production-grade AD systems that are often obscured by complex implementation details, these examples are deliberately simple and coherent to highlight the fundamental ideas. ... -
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LPIA
Programming language for artificial intelligence
LPIA is an interpreted symbolic functional language with multi-process capabilities and predefined pattern matching. It is inspired by combinatory logic, lambda calculus, Scheme and Forth. LPIA est un langage symbolique fonctionnel interprété avec des possibilités multi-processus et un filtrage prédéfini. Il est inspiré par la logique combinatoire, le lambda calcul, Scheme et Forth. -
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This project will provide an interface for using Lightweight Communications Calculus (LCC) to control agents in the Unreal Tournament enviroment. It consists of a prolog-style interperter, LCC parser/IDE,a GUI / LCC editor and interfaces to the Gamebots
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The Discrete Event Calculus Reasoner is an open source program for performing automated commonsense reasoning using the discrete event calculus, a comprehensive and highly usable formalism for reasoning about action, change, space, and mental states.
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Tools/libraries: analytical and numerical mathematical methods in ODE, PDE, Vector Calculus, Linear Algebra, Probability and Statistics, Numerical Methods, FEM, DSP. The current file release libham1.0 is for Geometric Integration of Hamiltonian Systems w
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