Articles for category: AI Tools

March 13, 2025

ikayaniaamirshahzad@gmail.com

DINOv2 for Image Classification: Fine-Tuning vs Transfer Learning

DINOv2 is one of the most well-known self-supervised vision models. Its pretrained backbone can be used for several downstream tasks. These include image classification, image embedding search, semantic segmentation, depth estimation, and object detection. In this article, we will cover the image classification task using DINOv2. This is one of the most of the most fundamental topics in deep learning based computer vision where essentially all downstream tasks begin. Furthermore, we will also compare the results between fine-tuning the entire model and transfer learning. Figure 1. Loss and accuracy comparison between transfer learning and fine-tuning using DINOv2 for image classification.

March 13, 2025

ikayaniaamirshahzad@gmail.com

Data Machina #255 A weekly deep dive into the latest AI/ ML research, projects & repos

New Trends in AI-RAG and Graphs. I’ve been doing a bit of research on how to improve RAG with graphs. I’m especially interested in augmenting agentic-RAG with the knowledge graph. A while back, Maya @Neo4j wrote a nice article on The Future of AI: Machine Learning and Knowledge Graphs. I think that makes sense to me. Let me share some new, interesting stuff on RAG and Graphs: Graph RAG. Unlike RAG approaches that focus solely on text-based entity retrieval, GRAG maintains an acute awareness of graph topology, which is crucial for generating contextually and factually coherent responses. The researchers claim

March 13, 2025

ikayaniaamirshahzad@gmail.com

Deep Dive into OpenAI’s Agent Ecosystem

Secure Your Spot at the AI Agent Conference (NYC, May 6-7) – Limited Seats! Use code GRADIENTFLOW25 for 25% off before it expires. In recent weeks, I have been examining the rapid evolution of AI agents, a field where OpenAI’s latest offerings represent just one approach in an increasingly transformative and globally competitive landscape. As my analysis of Manus, (the “general AI agent” from Chinese startup Monica.ai) revealed, significant innovation is emerging from diverse sources, with Manus even outperforming OpenAI’s offerings on the General AI Assistants (GAIA) benchmark. This isn‘t a winner-take-all market; it’s a rapidly developing global ecosystem where both established

March 13, 2025

ikayaniaamirshahzad@gmail.com

Building an App to Make Evals Easy, Fun, and Automated

Go to aligneval.com to start building your own LLM-evaluator; sample data included. Every AI-powered product needs evals. But let’s face it—they’re a pain to build, hard to scale, and most teams get them wrong. As a result, many AI-powered experiences are bottlenecked on evals, sometimes delaying launches by weeks or even months. I’ve spent the past year or so wrestling with product/task-specific evals. Testing different ways to detect hallucinations, finetune evaluators, and evaluate LLM-based evaluators. There were dead ends. There were rabbit holes. But I learned what works and what doesn’t. And that’s why I’m excited to introduce AlignEval, an

March 13, 2025

ikayaniaamirshahzad@gmail.com

🚀O que é a palavra-chave field no C# 13?

Agora ficou mais fácil trabalhar com propriedades no C#! O C# 13 trouxe a palavra-chave field, que permite acessar diretamente o campo de suporte gerado pelo compilador. Isso significa menos código repetitivo e propriedades mais fáceis de manter. No artigo, mostramos o que é o field, quando usá-lo e como ele pode tornar seu código mais simples e eficiente. 👉 Continue lendo aqui: https://www.develop4us.com/post/o-que-%C3%A9-a-palavra-chave-field-no-c-13 📢 Aproveite para acessar nossa área de membros!Na Develop4Us, você encontra cursos exclusivos, conteúdos aprofundados e uma comunidade para aprender e crescer como desenvolvedor. Faça parte e acelere sua jornada na programação! 🚀 https://www.develop4us.com Source link

March 13, 2025

ikayaniaamirshahzad@gmail.com

Cloud Efficiency at Netflix. By J Han, Pallavi Phadnis | by Netflix Technology Blog

By J Han, Pallavi Phadnis At Netflix, we use Amazon Web Services (AWS) for our cloud infrastructure needs, such as compute, storage, and networking to build and run the streaming platform that we love. Our ecosystem enables engineering teams to run applications and services at scale, utilizing a mix of open-source and proprietary solutions. In turn, our self-serve platforms allow teams to create and deploy, sometimes custom, workloads more efficiently. This diverse technological landscape generates extensive and rich data from various infrastructure entities, from which, data engineers and analysts collaborate to provide actionable insights to the engineering organization in a

March 13, 2025

ikayaniaamirshahzad@gmail.com

Gartner’s AI Maturity Model: Maximize Your Business Impact – BMC Software

AI is known as artificial intelligence. Assessing its value is notoriously tricky, for many reasons. To do this, it’s better to think of AI not as artificial intelligence—but as Advanced Information Processing. It is the same AI everyone has come to know, but when thought of as information processing, we can view the technology as a tool rather than another thinking species. With tools, we gain the agency to create new things. In this article, I’m exploring how companies use AI in different stages—the AI maturity model. I also look at problems that AI can solve and ways to adopt

March 13, 2025

ikayaniaamirshahzad@gmail.com

PyTorch Lightning for Dummies – A Tutorial and Overview

Takeaways You’ll learn to use PyTorch Lightning’s Core API features by completing an applied project to train a Language Transformer written in PyTorch on the WikiText2 dataset. The code in this tutorial is available on GitHub in the text-lab repo. Clone the repo and follow along! Introduction Training deep learning models at scale is an incredibly interesting and complex task. Reproducibility for projects is key, and reproducible code bases are exactly what we get when we leverage PyTorch Lightning for training and finetuning. An added benefit of using PyTorch Lightning is that the framework is domain agnostic and is complementary