Podcast interview with Gorkem Ercan discussing Eclipse Foundation, AI/ML adoption in enterprises, CI/CD practices, and open source development.
Explores whether large language models like ChatGPT truly reason or merely recite memorized text from their training data, examining their logical capabilities.
Exploration of Microsoft's 1.4 billion global building footprint dataset, created via ML on satellite imagery, including setup and analysis steps.
A developer shares their experience taking the AWS Certified AI Practitioner beta exam, covering study methods, key topics, and exam structure.
A comprehensive guide to building interactive data applications using the Streamlit framework, covering setup, visualization, ML integration, and deployment.
An animated exploration of UMAP, a state-of-the-art dimensionality reduction algorithm, applied to the classic MNIST dataset of handwritten digits.
A technical blog post documenting notes and code examples while studying machine learning concepts from 'The Little Learner' textbook.
A 3-hour coding workshop video covering the implementation, training, and use of Large Language Models (LLMs) from scratch.
Part 5 of a series on building an automatic differentiation package in Julia, demonstrating its use to create and train a multi-layer perceptron on the moons dataset.
Analyzing if a Codenames bot can win using only card layout patterns, without understanding word meanings.
A guide to deploying open-source LLMs like Llama 3 to Amazon SageMaker using Terraform for Infrastructure as Code.
A technical article exploring deep neural networks by comparing classic computational methods to modern ML, using sine function calculation as an example and implementing it in Kotlin.
An introduction to building a minimal automatic differentiation package in Julia, focusing on explicit chain rules and the Julia AD ecosystem.
A guide on running Large Language Models (LLMs) locally for inference, covering tools like Ollama and Open WebUI for privacy and cost control.
A guide to designing a reliable and valid interview process for hiring machine learning and AI engineers, covering technical skills, data literacy, and interview structure.
Explores Java's current and future capabilities in AI/ML development, challenging the notion that Java is unsuitable for AI tasks.
Announcing skrub 0.2.0, a library update simplifying machine learning on complex dataframes with new features like tabular_learner.
Explains the key difference between AI models and algorithms, using linear regression and OLS as examples.
Analyzes public reactions to AI bias claims, contrasting them with responses to traditional software bugs, using a viral example.
The article discusses the spin-off of scikit-learn's open-source development from Inria to a new mission-driven enterprise, Probabl, focusing on sustainable funding and growth.
