machine learning

790 items

DOCHugging Face (YouTube)·8d ago

What Are Tensors?

This content explains what tensors are, a fundamental data structure used in linear algebra and artificial intelligence. It details their importance and application in machine learning models.

tensors deep learning learning machine learning

RESEARCHDEV.to AI·4/9/2026

A Survey and Taxonomy of Graph Sampling

Este artigo apresenta um levantamento e uma taxonomia abrangente de métodos de amostragem de grafos. O estudo organiza e classifica as diversas técnicas existentes, oferecendo uma visão estruturada sobre o tema.

Survey Graph Sampling machine learning data science

ARTICLEDEV.to AI·5/9/2026

How AI Productivity Tools Are Transforming Workflows in 2023

This article explores how AI productivity tools are transforming workflows in 2023 by automating tasks and optimizing processes. They leverage machine learning and NLP to offer personalized solutions, driving efficiency and smarter work.

future-of-work workflow automation machine learning efficiency

NEWSHugging Face Blog·21d ago

OlmoEarth v1.1: A more efficient family of models

OlmoEarth v1.1 is a new version of a model family, emphasizing increased efficiency. This update aims to optimize performance and resource utilization.

updates Geospatial AI AI models model efficiency

ARTICLEDEV.to AI·4/25/2026

I debuted on PyPI with Harvard

The author announces the debut of their first Python package, `mlsysim`, as a contributor to Harvard-Edge Lab's CS249r project. This package is a first-principles analytical framework for ML infrastructure, formalizing the intuitive calculations engineers use to assess feasibility, performance, and macroeconomics of ML systems.

machine learning AI infrastructure

RESEARCHDEV.to AI·12d ago

Machine learning based hyperspectral image analysis: A survey

This paper provides a comprehensive survey on machine learning techniques applied to hyperspectral image analysis. It reviews various methodologies and applications in this specialized field.

Image analysis Survey Hyperspectral imaging machine learning

ARTICLEDEV.to AI·4/14/2026

Understanding Autonomous Testing: What It Means for Developers

Software testing is undergoing a fundamental shift with autonomous testing, which uses AI and machine learning to create, execute, analyze, and maintain tests with minimal human intervention. This represents an adaptive, decision-making approach instead of scripted instructions, transforming how quality is built into products.

machine learning AI Software Testing Autonomous Testing

ARTICLEDEV.to AI·4/25/2026

No More Pesky Learning Rate Guessing Games

This content discusses overcoming the challenges of guessing the learning rate in AI models, suggesting a solution or method for optimization. It focuses on simplifying the process of training neural networks.

neural networks Optimization deep learning machine learning

ARTICLEDEV.to AI·4/19/2026

Cloudflare Workers AI: Run Edge Inference Without a GPU Server

Cloudflare Workers AI enables running AI inference at the edge without needing GPU servers, offering over 50 models and billing per inference unit. This service simplifies AI-native app development by providing global low-latency inference on Cloudflare's GPU network, eliminating cold starts and server management.

cloud computing machine learning Serverless AI inference

ARTICLEDEV.to AI·19d ago

Apple Paper Argues LLMs Show 'Illusion of Thinking'

An Apple paper titled "The Illusion of Thinking" argues that Large Language Models (LLMs) lack genuine reasoning, relying only on sophisticated statistical pattern matching. Led by Mehrdad Farajtabar, the study criticizes claims from vendors like GPT-4 and Claude, highlighting failures in formal reasoning tasks requiring compositionality.

Apple machine learning Reasoning AI

ARTICLEDEV.to AI·28d ago

Fine-tuning CLIP on a Niche Domain: How I Got +26pp Accuracy on Architectural Styles and What You Can Apply to Your Own Domain

This article details the process of fine-tuning OpenCLIP ViT-B/32 for architectural styles, achieving a +26 percentage point increase in accuracy. The author focuses on the critical decisions made before and after the training loop that were responsible for this significant result, rather than the training loop optimization itself.

CLIP Vision-Language Models machine learning computer vision

RESEARCHDEV.to AI·4/28/2026

Ranking via Sinkhorn Propagation

This research introduces a novel approach to ranking using Sinkhorn propagation, a method known for solving optimal transport problems. It explores how this technique can be effectively applied to establish robust and coherent rankings.

ranking Optimization machine learning Sinkhorn Algorithm

ARTICLEDEV.to AI·4/27/2026

Intelligent Automation Explained: A Beginner's Guide to the Future of Work

Intelligent Automation is a transformative concept combining artificial intelligence with process automation, enabling systems to learn, adapt, and improve over time. It represents the convergence of RPA, machine learning, natural language processing, and cognitive technologies to optimize performance in modern business.

future-of-work machine learning NLP AI

ARTICLEDEV.to AI·22d ago

What Production ML Systems Taught Me About AI Hallucinations

While AI hallucination discussions often focus on chatbots, this article argues the greater danger lies in production ML systems. In these environments, failures are believable and hard to detect because LLMs optimize for coherence, not factual verification.

AI hallucinations LLMs production ML machine learning

ARTICLEDEV.to AI·22d ago

Learning to Optimize Neural Nets

This article explores methods for optimizing neural networks, focusing on techniques that improve their performance and efficiency. It discusses learning approaches to enhance the underlying algorithms governing AI model training.

neural networks Optimization deep learning machine learning

ARTICLEGoogle for Developers (YouTube)·18d ago

What's new in the Gemma open model family

The Gemma open model family introduces new updates and features, aiming to enhance its performance and accessibility for developers. These novelties strengthen the open-source AI ecosystem, driving further innovation.

AI models Google AI open models machine learning

What's new in the Gemma open model family

ARTICLEDEV.to AI·4/19/2026

UNMASKING THE MASTERPIECE: How I Leveraged Semantic AI to Decode 5,000 Years of Art History

A computer science student recounts leveraging Semantic AI and a high-performance machine learning pipeline to classify 5,000 years of art history data for a hackathon. Despite messy museum records, the project achieved a verified 94.10% accuracy in decoding complex historical metadata.

machine learning hackathon data science Semantic AI

ARTICLEDEV.to AI·4/17/2026

How Smart Model Routing Picks the Right AI for Your Programming Language

This content discusses how a single AI model is not optimal for all tasks, exemplified by code review across different programming languages. It presents a model routing solution that dynamically selects the most suitable AI to improve code review accuracy.

model selection machine learning AI development

ARTICLEDEV.to AI·4/16/2026

Policy Gradients — Deep Dive + Problem: Valid Parentheses

Policy Gradients is a fundamental Reinforcement Learning algorithm that directly optimizes the policy, mapping states to actions, using gradient-based methods. It's crucial for handling high-dimensional action spaces and learning stochastic policies, offering advantages over value-based methods by learning the policy directly.

reinforcement learning machine learning Policy Gradients

RESEARCHDEV.to AI·4/14/2026

Don't forget, there is more than forgetting: new metrics for Continual Learning

This content introduces novel metrics for Continual Learning, broadening evaluation beyond just preventing catastrophic forgetting. It proposes a more comprehensive view for measuring AI model performance in sequential learning scenarios.

AI metrics evaluation machine learning catastrophic forgetting