Best AI tools for< Autosar Expert >

LearnOpenCV is a repository containing code for Computer Vision, Deep learning, and AI research articles shared on the blog LearnOpenCV.com. It serves as a resource for individuals looking to enhance their expertise in AI through various courses offered by OpenCV. The repository includes a wide range of topics such as image inpainting, instance segmentation, robotics, deep learning models, and more, providing practical implementations and code examples for readers to explore and learn from.

hongbomiao.com is a personal research and development (R&D) lab that facilitates the sharing of knowledge. The repository covers a wide range of topics including web development, mobile development, desktop applications, API servers, cloud native technologies, data processing, machine learning, computer vision, embedded systems, simulation, database management, data cleaning, data orchestration, testing, ops, authentication, authorization, security, system tools, reverse engineering, Ethereum, hardware, network, guidelines, design, bots, and more. It provides detailed information on various tools, frameworks, libraries, and platforms used in these domains.

Awesome-Embedded is a curated list of resources for embedded systems enthusiasts. It covers a wide range of topics including MCU programming, RTOS, Linux kernel development, assembly programming, machine learning & AI on MCU, utilities, tips & tricks, and more. The repository provides valuable information, tutorials, and tools for individuals interested in embedded systems development.

LLM4Opt is a collection of references and papers focusing on applying Large Language Models (LLMs) for diverse optimization tasks. The repository includes research papers, tutorials, workshops, competitions, and related collections related to LLMs in optimization. It covers a wide range of topics such as algorithm search, code generation, machine learning, science, industry, and more. The goal is to provide a comprehensive resource for researchers and practitioners interested in leveraging LLMs for optimization tasks.

The Awesome-Neuro-Symbolic-Learning-with-LLM repository is a curated collection of papers and resources focusing on improving reasoning and planning capabilities of Large Language Models (LLMs) and Multi-Modal Large Language Models (MLLMs) through neuro-symbolic learning. It covers a wide range of topics such as neuro-symbolic visual reasoning, program synthesis, logical reasoning, mathematical reasoning, code generation, visual reasoning, geometric reasoning, classical planning, game AI planning, robotic planning, AI agent planning, and more. The repository provides a comprehensive overview of tutorials, workshops, talks, surveys, papers, datasets, and benchmarks related to neuro-symbolic learning with LLMs and MLLMs.

AUTOSAR专家，用中文帮助构建RTE项目

Simplifying AUTOSAR for young learners; fostering curiosity and deeper exploration.

专业的AUTOSAR工程师，使用中文提供AUTOSAR学习支持。