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 concepts like chain-of-thought and methods to improve LLM reasoning abilities.
An introduction to reasoning in Large Language Models, covering key 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.
An analysis of the ethical debate around LLMs, contrasting their use in creative fields with their potential for scientific advancement.
Explores the critical challenge of bias in health AI data, why unbiased data is impossible, and the ethical implications for medical algorithms.
A technical guide exploring IBM's Granite 3.1 AI models, covering their reasoning and vision capabilities with a demo and local setup instructions.
Summary of key concepts for optimizing AI inference performance, covering bottlenecks, metrics, and deployment patterns from Chip Huyen's book.
Explores four main approaches to building and enhancing reasoning capabilities in Large Language Models (LLMs) for complex tasks.
