Search Results for "reasoning models"

...The course integrates mathematics, computing, and real-world applications into a unified curriculum, making it suitable for students across science, engineering, and data-driven fields. 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.

PocketFlow Tutorial Codebase Knowledge is a project that demonstrates how to build an AI agent capable of analyzing arbitrary codebases and generating beginner-friendly tutorials that explain how they work, turning complex source code into clear educational content. 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. ...

TechCPP is a comprehensive C++ learning and interview guide aimed at back-end and systems developers preparing for professional roles. It gathers frequently asked concepts and deep dives—value categories (lvalue/rvalue), perfect forwarding, casts, memory models, atomics, and more—into a structured, readable format. 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 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.

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. ...