A timeline of beginner-friendly 'Hello World' examples in machine learning and AI, from Random Forests in 2013 to modern RLVR models in 2025.
Analysis of the rising prominence of Chinese AI labs like DeepSeek and Kimi in the global AI landscape and their rapid technological advancements.
A curated list of key LLM research papers from Jan-June 2025, organized by topic including reasoning models, RL methods, and efficient training.
A course teaching how to code Large Language Models (LLMs) from scratch to deeply understand their inner workings and fundamentals.
An introduction to reasoning in Large Language Models, covering concepts like chain-of-thought and methods to improve LLM reasoning abilities.
Explores the critical challenge of bias in health AI data, why unbiased data is impossible, and the ethical implications for medical algorithms.
Explores four main approaches to building and enhancing reasoning capabilities in Large Language Models (LLMs) for complex tasks.
A researcher reflects on 2024 highlights in AI, covering societal impacts, software tools like Scikit-learn, and technical research on tabular data and language models.
A curated list of notable LLM and AI research papers published in 2024, providing a resource for those interested in the latest developments.
Explores whether large language models like ChatGPT truly reason or merely recite memorized text from their training data, examining their logical capabilities.
Analyzing if a Codenames bot can win using only card layout patterns, without understanding word meanings.
Announcing skrub 0.2.0, a library update simplifying machine learning on complex dataframes with new features like tabular_learner.
Analyzes public reactions to AI bias claims, contrasting them with responses to traditional software bugs, using a viral example.
The article discusses the spin-off of scikit-learn's open-source development from Inria to a new mission-driven enterprise, Probabl, focusing on sustainable funding and growth.
A guide on using Microsoft's Phi-3 Small Language Model with C# and Semantic Kernel for local AI applications.
Explores methods for using and finetuning pretrained large language models, including feature-based approaches and parameter updates.
Scikit-learn remains a dominant and impactful machine learning library, especially for classic ML and tabular data, despite the hype around deep learning.
Learn about Low-Rank Adaptation (LoRA), a parameter-efficient method for finetuning large language models with reduced computational costs.
A guide on managing the flood of AI and machine learning research, covering tools and strategies for prioritizing papers and news.
A curated reading list of key academic papers for understanding the development and architecture of large language models and transformers.
