A curated list of key LLM research papers from the first half of 2025, organized by topic such as reasoning models and reinforcement learning.
A curated list of key LLM research papers from Jan-June 2025, organized by topic including reasoning models, RL methods, and efficient training.
A tutorial on building a transformer-based language model in R from scratch, covering tokenization, self-attention, and text generation.
Explains the differences between Machine Learning and Generative AI, with examples and industry applications.
A summary of a practical session on analyzing and improving LLM applications by identifying failure modes through data clustering and iterative testing.
Explores continuous-time Markov chains as a foundation for understanding discrete diffusion models in machine learning.
A course teaching how to code Large Language Models from scratch to deeply understand their inner workings, with practical video tutorials.
A course teaching how to code Large Language Models (LLMs) from scratch to deeply understand their inner workings and fundamentals.
Explains key AI terminology like AI, ML, deep learning, and LLMs to help engineers use the correct terms.
A researcher shares progress on the PRISM project, an MSCA-PF grant focused on validating spatial patterns in machine learning for remote sensing.
A review of a free LinkedIn Learning course on AI and Machine Learning fundamentals tailored for Java developers, covering predictive and generative AI.
Explores the evolution of AI from symbolic systems to modern Large Language Models (LLMs), detailing their capabilities and limitations.
A technical analysis using R to classify iris images from a dataset, applying PCA and LDA for machine learning classification.
Using an LLM to label Hacker News titles and train a Ridge regression model for personalized article ranking based on user preferences.
An introduction to reasoning in Large Language Models, covering key concepts like chain-of-thought and methods to improve LLM reasoning abilities.
An introduction to reasoning in Large Language Models, covering concepts like chain-of-thought and methods to improve LLM reasoning abilities.
A guide to building neural networks using JavaScript and the Brain.js library, covering fundamental concepts for web developers.
A clear explanation of the attention mechanism in Large Language Models, focusing on how words derive meaning from context using vector embeddings.
Argues that AI can improve beyond current transformer models by examining biological examples of superior sample efficiency and planning.
Explores the concept of 'generality' in AI models, using examples of ML failures and LLM inconsistencies to question how we assess their capabilities.
