Announces the addition of 6 new R programming books to the Big Book of R collection, covering statistics, machine learning, and data science.
An analysis of 900 popular open-source AI tools, categorizing them into infrastructure, model development, and application layers.
Explains why mocking ML models in unit tests is problematic and offers guidelines for effectively testing machine learning code.
Explores the gap between generative AI's perceived quality in open-ended play and its practical effectiveness for specific, goal-oriented tasks.
Explores the importance of high-quality human-annotated data for training AI models, covering task design, rater selection, and the wisdom of the crowd.
Explains key AI model generation parameters like temperature, top-k, and top-p, and how they control output creativity and consistency.
Analyzes push notifications as a recommender system, discussing intent, personalization, timeliness, and user engagement challenges.
A recap of key announcements from the second half of AWS re:Invent 2023, focusing on new AI/ML services and management tools.
Scikit-learn remains a dominant and impactful machine learning library, especially for classic ML and tabular data, despite the hype around deep learning.
Announcing libactivation, a new Python package on PyPI providing activation functions and their derivatives for machine learning and neural networks.
Exploring how Java code can be executed on GPUs for high-performance computing and machine learning, covering challenges and potential APIs.
Explores using out-of-domain data to improve LLM finetuning for detecting factual inconsistencies (hallucinations) in text summaries.
A developer recreates a research project using wireless earbuds' microphones to detect facial touch gestures and control UIs via machine learning in JavaScript.
A critical analysis of the machine learning bubble, arguing its lasting impact will be a proliferation of low-quality, automated content and services, not true AGI.
Interview with Itai Bar Sinai, co-founder of Mona Labs, discussing AI monitoring, product-oriented data science, and the Israeli ML community.
A developer shares insights from building an AI audit prototype, discussing the importance of defensibility and lessons from banking model audits.
Author shares how following curiosity in writing and tech led to writing a machine learning book, promotions, and career growth.
A guide to deploying open-source Large Language Models (LLMs) like Falcon using Hugging Face's managed Inference Endpoints service.
Interview with Chetan Sharma, CEO of Eppo, discussing A/B testing, statistics engineering, and building a modern experimentation platform.
A guide to running open-source Large Language Models (LLMs) like LLaMA locally on your CPU using C# and the LLamaSharp library.
