Search Results for "reasoning"
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Showing 19 open source projects for "reasoning"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Computational Thinking
Introduction to computational thinking with Julia
...It emphasizes learning how to translate problems into computational terms and developing algorithms and models to analyze them effectively. Using Julia, the course highlights both mathematical reasoning and practical coding, bridging the gap between theory and application. The materials include lectures, notebooks, exercises, and projects that encourage experimentation and discovery. By combining programming with conceptual depth, the repository aims to build skills that are transferable across disciplines and essential for modern scientific inquiry. -
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AI Agents for Beginners
12 Lessons to Get Started Building AI Agents
AI Agents for Beginners is a comprehensive educational repository created to introduce developers to the concepts and practical implementation of AI agents. It provides step-by-step lessons that guide users through building autonomous systems capable of reasoning, planning, and interacting with tools. The curriculum combines theory with hands-on exercises, allowing learners to experiment with real agent architectures and workflows. It covers key topics such as prompt design, tool integration, memory management, and multi-agent coordination. The repository is structured to be accessible to beginners while still providing depth for more advanced learners. ... -
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LeetCode Book
Comprehensive study guide for coding interviews
LeetCode-Book is a comprehensive study guide for coding interviews that consolidates algorithm patterns, data-structure templates, and worked LeetCode solutions. It organizes problems by topic—arrays, linked lists, stacks/queues, trees/graphs, dynamic programming, greedy, backtracking, and math—so you can study systematically. Explanations are concise but intentional, highlighting why a pattern fits, how to reason about boundary cases, and the time/space trade-offs. Many entries include... -
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Book3_Elements-of-Mathematics
From Addition, Subtraction, Multiplication, and Division to ML
Book3_Elements-of-Mathematics is an open learning resource in the Visualize-ML collection that introduces core mathematical foundations required for modern data science and AI. The repository presents topics such as algebra, calculus fundamentals, and mathematical reasoning using a highly visual and beginner-friendly approach. Its goal is to reduce the intimidation barrier often associated with formal mathematics by combining diagrams, structured explanations, and applied examples. The content is organized progressively so learners can build confidence before moving into more advanced quantitative subjects. ... -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Book4_Power-of-Matrix
Book_4_Matrix Power | The Iris Book: From Addition, Subtraction
...It combines explanatory text, diagrams, and Python examples to bridge theory and practical computation. The material emphasizes geometric interpretation and visual reasoning, which makes abstract linear algebra topics more accessible to beginners and self-learners. The repository is continuously updated and intended to accompany the broader Visualize-ML learning ecosystem. Overall, it serves as a visually driven mathematical foundation for students preparing for data science and machine learning work. -
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ThinkStats2
Text and supporting code for Think Stats, 2nd Edition
ThinkStats2 is the code and text companion for the second edition of Think Stats, an introduction to statistics and data science for Python programmers. It teaches probability and statistical reasoning through short programs, experiments, and analysis of real datasets. The material emphasizes exploratory methods that help readers ask and answer practical questions with data. Case studies draw from public sources, including health-related datasets, to connect abstract concepts with realistic analysis. Each chapter has a notebook containing examples and exercises, along with a separate version that includes solutions. ... -
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TechCPP
C++ learning and interview guide aimed at back-end systems developers
...The material goes beyond syntax to discuss performance, optimization techniques, and how standard library containers are implemented under the hood. You’ll also find practical debugging and tooling advice, such as using gdb to diagnose deadlocks or reasoning about concurrency primitives. The repository organizes topics so you can quickly locate weak spots before interviews or brush up for day-to-day coding. It’s equally useful as a revision handbook and as a roadmap for engineers moving from “effective C++ usage” toward “systems-level competence”. -
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AI Researcher
An autonomous AI researcher
...Each agent operates with clear roles — such as researcher, analyst, and summarizer — and they communicate through a task-management interface that ensures progress tracking and iterative refinement. The system emphasizes modularity, so teams can swap in new reasoning modules, data retrieval strategies, or domain knowledge bases depending on the research topic. Through self-supervised feedback loops, agents adjust their strategies based on prior outcomes, improving both the quality and relevance of results over time. -
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PocketFlow Tutorial Codebase Knowledge
Codebase to Tutorial
...The repository builds on a lightweight 100-line LLM framework and uses natural language models to inspect repository structures, identify core abstractions, map dependencies, and articulate the reasoning behind code design and interactions. By crawling code files, extracting higher-level patterns, and using large language models to narrate explanations, the system aims to help developers — especially those new to a codebase — understand unfamiliar projects without manual deep reading. It supports both GitHub URL crawling and local directory analysis, and can tailor output tutorials to different languages, making it accessible for international developers. -
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Quiz/Survey/Test - QST
A Free, complete, open source universal assessment/exam platform
QST, the worlds unparalleled open source, multi-tenant, online/lan assessment software. From a quick quiz on your phone to very large scale, high stakes, proctored desktop testing, we make it easy/secure/economical. Our intuitive design contains features (Immediate detailed results, Create/Export/Import/Convert Questions, WYSIWYG/Visual Math-Chemistry Editors, Question/Item Bank, Multiple Question Types, Multiple Delivery Styles, Multiple Delivery/Results Options, Adaptive/Branching... -
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ThinkJulia.jl
Port of the book Think Python to the Julia programming language
...By combining clear explanations with practical examples, the project helps both beginners and experienced programmers transition to Julia. The material emphasizes not only writing code but also reasoning about algorithms and problem-solving. Since it is freely available, learners and educators can use, adapt, and contribute to the content, making it a valuable resource for self-study or classroom use. -
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Bayesian Methods for Hackers
An introduction to Bayesian methods + probabilistic programming
...It is written from a computation-first perspective, prioritizing intuition, examples, and executable notebooks over heavy mathematical formalism. The project introduces readers to uncertainty, Bayesian modeling, MCMC, priors, posteriors, and real-world probabilistic reasoning. It includes notebook-based chapters that let learners run and modify examples directly. The material is especially useful for programmers, data scientists, and technically curious readers who want to learn Bayesian methods through code. It remains a widely referenced entry point for making Bayesian statistics feel practical and approachable. -
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System Design Notebook
Learn System Design step by step
...Instead of being a single long article, it’s split into topics like scalability, load balancing, data partitioning, caching, availability, consistency, and communication patterns, so you can study them in isolation. It emphasizes reasoning: why you pick a certain database, why you shard, why you put a queue, and what trade-offs come with each choice. The notes often connect abstract concepts to concrete systems such as social networks, feeds, chat, and file storage, which makes it easier to translate theory into design diagrams. It is meant to be expanded over time, so you can treat it as a living notebook you keep coming back to before interviews or architecture reviews. ... -
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DeepMind Educational Resources
DeepMind's repo of educational notebooks for learning AI and research
...The tutorials cover a broad range of topics—from foundational Python programming and data handling to supervised, unsupervised, and reinforcement learning, as well as graph neural networks and scientific reasoning. Specialized notebooks also explore creative AI applications, language modeling, generative models, and protein folding. Each tutorial is designed to be standalone and adaptable for self-study, classroom teaching, or use at summer schools and community workshops. -
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paip-lisp
Lisp code for the textbook "Paradigms of Artificial Intelligence"
paip-lisp is the Common Lisp source code companion to Peter Norvig’s book Paradigms of Artificial Intelligence Programming. It contains implementations and examples from the book, covering classic AI techniques through Lisp programs. The repository includes systems for symbolic reasoning, search, rule-based inference, natural language processing, pattern matching, logic programming, and game-playing examples. It is valuable for readers who want to study the original code while working through the text or revisiting older AI ideas. The code also serves as a historical reference for how AI programming was taught before today’s deep-learning-centered ecosystem. paip-lisp is best suited for learners interested in Lisp, symbolic AI, and the foundations of practical AI programming. -
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txtkit is a visual text mining tool for exploring large amounts of multilingual texts. It's an multiuser-application which mainly focuses on the process of reading and reasoning as series of decisions and events.
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HORUS is a system for knowledge acquisition, hypothesis generation, inference and learning. It is an interactive, internet environment accessible to a diverse community of users (public-access or membership basis) - see also UMKAILASH project for more.
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When this project is complete, a user may enter in an deductive argument in standard form, and out pops a fresh, baked validity judgement.
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DBNL
Dynamic Bayesian Network Library
DBNL is a cross-platform library that offers a variety of implementations of Bayesian networks and machine learning algorithms. It is a flexible library that covers all aspects of Bayesian netwoks from representation to reasoning and learning. It allows you to create simple static networks as well as complex temporal models with changing structure. It can handle highly non-linear dependencies between multivariate random variables. The particle based inference can answer arbitrary questions given the provided evidence and can even cope with multimodal densities. ...
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