Explores autoregressive models, their relationship to joint distributions, and how they handle out-of-distribution prompts, with insights relevant to LLMs.
Explores using GPT-3 text embeddings and a simple classifier to predict the winner of a headline A/B test, potentially replacing traditional testing.
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 flood of AI and machine learning research, covering tools and strategies for prioritizing papers and news.
A guide on managing the overwhelming volume of AI/ML research, sharing strategies and tools for prioritizing and staying updated effectively.
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 non-expert's humorous exploration of diffusion models as a method for sampling from arbitrary probability distributions, touching on measure transport.
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.
Learn how to train an XGBoost classifier using cloud GPUs without managing infrastructure via the Lightning AI framework.
A guide to training XGBoost models on cloud GPUs using the Lightning AI framework, bypassing complex infrastructure setup.
Analyzes common pitfalls in AI adoption, arguing that technical and product maturity models can hinder practical implementation.
A curated list of the top 10 open-source machine learning and AI projects released or updated in 2022, including PyTorch 2.0 and scikit-learn 1.2.
A curated list of the top 10 open-source releases in Machine Learning & AI for 2022, including PyTorch 2.0 and scikit-learn 1.2.
