#AI Research

Caffe is a versatile deep learning framework from Berkeley AI Research (BAIR) and BVLC. It prioritizes speed and modularity, offering extensive documentation, model resources, and installation guides. Custom distributions like Intel Caffe for CPU and OpenCL Caffe for various devices ensure flexibility. The project encourages community engagement via forums and gitter chat, supporting discussions on models and methods. Licensed under BSD 2-Clause, Caffe is suited for research and commercial use.

Explore a curated collection of advanced LLM applications that utilize AI agents and leading models from OpenAI, Anthropic, and Google, along with open-source alternatives like LLaMA. This repository features a range of tools from local applications to AI agents for investment, finance, and travel. These apps demonstrate practical uses of LLMs, providing opportunities to learn from documented projects and engage in interactions with various data sources. Ideal for research, content creation, and data exploration, these tools support users in maximizing the potential of AI technology.

This AI-powered agent autonomously performs comprehensive research and delivers objective reports, addressing misinformation with speed and reliability. It offers customization, processes data from over 20 sources, and is suitable for both personal and organizational research needs.

Explore cutting-edge research on autonomous agents, focusing on multi-agent frameworks across various domains such as financial intelligence, social interactions, and complex problem-solving. This repository features a regularly updated collection of scientific papers including systems like VisionCoder for image processing, FISHNET for SEC data insights, and AgentSense for evaluating social intelligence. Learn about different LLM-based development strategies that include role definition improvements and hierarchical task workflows, boosting efficiency and precision in various applications.

Discover detailed video analysis of recent deep learning papers, focusing on key models including GPT-4, Llama 3.1, and Anthropic LLM, highlighting insights for researchers and enthusiasts interested in advancing their knowledge in modern language models and multimodal technologies.

The Arxiv Translator project transforms ArXiv papers into Korean using Nougat OCR, offering quicker access to new academic papers. Departing from Ar5iv's method due to update delays, this tool extracts and presents papers independently, enhancing accessibility. While translations aid understanding, original papers are recommended for detailed insights. Users can navigate a comprehensive list of translated works linked to their specific ArXiv pages.

A detailed review of notable AI advancements made in 2022, with an emphasis on ethical standards, governance models, and transformative applications. Offering insights on the evolution of AI, this review highlights key innovations expected to improve quality of life and calls for conscientious technology application. Includes video explanations and links to scholarly articles and code repositories for a comprehensive understanding of current AI developments.

PyTorch Toolbelt is a Python library designed for streamlined research and development in PyTorch. Featuring a flexible encoder-decoder setup, it includes modules like CoordConv and SCSE, and supports test-time augmentation. Compatible with Catalyst, it offers visualization and metrics enhancement for efficient deep learning workflows.