The books, courses, and resources I used in my journey. The post Everything I Studied to Become a Machine Learning Engineer (No CS Background) appeared first on Towards Data Science.
The Nothing Ear (a) are svelte and elegant, with great sound quality and a more compelling price than ever.
WIRED spoke to a self-proclaimed leader of an online group called Purgatory, which charged as little as $20 to call in fake threats against schools.
Google Translate is getting two new AI-powered features: live conversation translation and a personalized language learning mode. The article Google Translate adds live conversation and language learning features appeared first on THE DECODER.
Explore how Agentic AI is reshaping the tech careers, from data to decision-making, and how professionals can prepare for the future of work The post Get AI-Ready: How to Prepare for a World of Agentic AI as Tech Professionals appeared first on Towards Data Science.
Google is now offering its basic editing functions in the AI video tool Google Vids free of charge for everyone. The article Google Vids adds free AI video editing and new features appeared first on THE DECODER.
Researchers developed an approach to study where proteins get made, and characterized proteins produced near mitochondria, gaining potential insights into mitochondrial function and disease.
A new convention is emerging in the open-source ecosystem: AGENTS.md, a straightforward and open format designed to assist AI coding agents in software development. Already adopted by more than 20,000 repositories on GitHub, the format is being positioned as a companion to traditional documentation, offering machine-readable context that complements human-facing files like README.md. By Robert Krzaczyński
By directly imaging material failure in 3D, this real-time technique could help scientists improve reactor safety and longevity.
arXiv:2307.00127v4 Announce Type: replace-cross Abstract: Bayesian methods for learning Gaussian graphical models offer a principled framework for quantifying model uncertainty and incorporating prior knowledge. However, their scalability is constrained by the computational cost of jointly exploring graph structures and precision matrices. To address this challenge, we perform inference directly on the graph by integrating out the precision matrix. We adopt a marginal pseudo-likelihood approach, eliminating the need to compute intractable normalizing constants and perform computationally intensive precision matrix sampling. Building on this framework, we develop continuous-time (birth-death) and discrete-time (reversible jump) Markov chain Monte Carlo (MCMC) algorithms that efficiently explore the posterior over graph space. We establish theoretical guarantees for posterior contraction, convergence, and graph selection consistency. The algorithms scale to large graph spaces, enabling parallel exploration for graphs with over 1,000 nodes, while providing uncertainty quantification and supporting flexible prior specification over the graph space. Extensive simulations show substantial computational gains over state-of-the-art Bayesian approaches without sacrificing graph recovery accuracy. Applications to human and mouse gene expression datasets demonstrate the ability of our approach to recover biologically meaningful structures and quantify uncertainty in complex networks. An implementation is available in the R package BDgraph.
