Interview with AI researcher Curtis Northcutt on his journey from rural Kentucky to MIT, founding Cleanlab, and his work on confident learning and dataset improvement.
Interview with Frank Liu on vector databases, embeddings, his career in ML/hardware, and work culture differences between China and the US.
A curated list of open-source Large Language Models (LLMs) available for commercial use, including community-contributed updates and details.
An introduction to artificial neural networks, explaining the perceptron as the simplest building block and its ability to learn basic logical functions.
Learn about Low-Rank Adaptation (LoRA), a parameter-efficient method for finetuning large language models with reduced computational costs.
Explains Low-Rank Adaptation (LoRA), a parameter-efficient technique for fine-tuning large language models to reduce computational costs.
Explores essential design patterns for building efficient and maintainable machine learning systems in production, focusing on data pipelines and best practices.
Explores autoregressive models, their relationship to joint distributions, and how they handle out-of-distribution prompts, with insights relevant to LLMs.
Explores the concept of class imbalance in machine learning, drawing parallels to medical training and questioning if it's a problem or an inherent feature.
A guide on managing the overwhelming volume of AI/ML research, sharing strategies and tools for prioritizing and staying updated effectively.
A guide on managing the flood of AI and machine learning research, covering tools and strategies for prioritizing papers and news.
A reflection on past skepticism of deep learning and why similar dismissal of Large Language Models (LLMs) might be a mistake.
A technical guide on deploying Google's FLAN-UL2 20B large language model for real-time inference using Amazon SageMaker and Hugging Face.
A guide on creating effective data labeling guidelines for machine learning, covering principles like Why, What, and How, with examples from Google and Bing.
Explains the core theory behind linear regression models, a fundamental machine learning algorithm for predicting continuous numerical values.
Explores five industry patterns for building robust content moderation and fraud detection systems using ML, including human-in-the-loop and data augmentation.
A curated reading list of key academic papers for understanding the development and architecture of large language models and transformers.
A curated reading list of key academic papers for understanding the development and architecture of large language models and transformers.
A retrospective on forming a research team in 2022 to apply machine learning to challenges in health and social sciences, including data management and validation.
Explores practical mechanisms like pilot/copilot roles and literature reviews to improve the success rate of machine learning projects.
