To Data & Beyond Week 3 Summary
Each week, To Data & Beyond delivers daily newsletters on data science and AI, focusing on practical topics. This letter provides a summary of letters sent in the third week of 2024. If you're interested in reading the complete letters, you can find them here. Don't miss out—subscribe here to receive them directly in your email.
Top Important Computer Vision Papers for the Week from 08/01 to 14/01
Every week, several top-tier academic conferences and journals showcased innovative research in computer vision, presenting exciting breakthroughs in various subfields such as image recognition, vision model optimization, generative adversarial networks (GANs), image segmentation, video analysis, and more.
Top Important LLM Papers for the Week from 08/01 to 14/01
Large language models (LLMs) have advanced rapidly in recent years. As new generations of models are developed, researchers and engineers need to stay informed on the latest progress. This article summarizes some of the most important LLM papers published during the
LLM Researcher and Scientist Roadmap: A Guide to Mastering Large Language Models Research
This comprehensive article serves as a roadmap for aspiring LLM researchers and scientists, offering a step-by-step guide to mastering the intricacies of Large Language Models (LLMs) to take your first step as a researcher in this field. The content unfolds with an exploration of the LLM architecture, providing insights into its foundational structure. Subsequent sections delve into crucial aspects such as constructing an instruction dataset, harnessing pre-trained LLM models, supervised fine-tuning, reinforcement learning from human feedback, and the evaluation process.
Hands-On LangChain for LLMs App: Chat with Your Files
In previous articles we have explored the journey from loading documents to creating a vector store, discussing the limitations of existing models in handling follow-up questions and engaging in real conversations. The good news is that we’re addressing these issues by introducing chat history into LangChain. This addition enables the language model to consider previous interactions, allowing it to provide context-aware responses.