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.
A review of the top 10 most influential machine learning papers from 2022, including ConvNeXt and MaxViT, with technical analysis.
A review of the top 10 influential machine learning research papers from 2022, including ConvNeXt and MaxViT, highlighting key advancements in AI.
Author announces closing his data science training company after seven years and shares his new role as a Senior Machine Learning Engineer.
A data scientist reviews his 2022 goals, including technical writing on ML topics and career progression, and sets new goals for 2023.
