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Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter The 7 Most Useful Jupyter Notebook Extensions for Data Scientists In this article, we will explore seven different Jupyter Notebook extensions that will improve your work.
Skip to content Explore Topics Architecture and Hardware Artificial Intelligence and Machine Learning Computer History Computing Applications Computing Profession Data and Information Education HCI Philosophy of Computing Security and Privacy Society Software Engineering and Programming Languages Systems and Networking Theory Latest Issue Latest Issue June 2025 , Vol. 68 No. 6 Previous Issue May 2025 , Vol. 68 No. 5 Explore the archive Search Open Membership Navigation Settings Sign Out Sign In
Q-learning is not yet scalable Seohong Park UC Berkeley June 2025 Does RL scale? Over the past few years, weve seen that next-token prediction scales, denoising diffusion scales, contrastive learning scales, and so on, all the way to the point where we can train models with billions of parameters with a scalable objective that can eat up as much data as we can throw at it.
Speaker: Jason Chester, Director, Product Management
In today’s manufacturing landscape, staying competitive means moving beyond reactive quality checks and toward real-time, data-driven process control. But what does true manufacturing process optimization look like—and why is it more urgent now than ever? Join Jason Chester in this new, thought-provoking session on how modern manufacturers are rethinking quality operations from the ground up.
Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. In this work, we demonstrate that NFs are more powerful than previously believed. We present TarFlow: a simple and scalable architecture that enables highly performant NF models.
MLOps, or machine learning operations, is all about managing the end-to-end process of building, training, deploying, and maintaining machine learning models.
MLOps, or machine learning operations, is all about managing the end-to-end process of building, training, deploying, and maintaining machine learning models.
AI operating system company VAST Data, the AI Operating System company, today announced that Voltage Park, the enterprise-grade AI factory company, has partnered with VAST to deliver the high-performance data services required for demanding AI workloads. Voltage Park has deployed.
A comprehensive guide to building AI systems that can plan, reason, and act autonomously — from basic tool-using agents to sophisticated multi-agent collaborations.
Skip to main content Login Why Databricks Discover For Executives For Startups Lakehouse Architecture Mosaic Research Customers Customer Stories Partners Cloud Providers Databricks on AWS, Azure, GCP, and SAP Consulting & System Integrators Experts to build, deploy and migrate to Databricks Technology Partners Connect your existing tools to your Lakehouse C&SI Partner Program Build, deploy or migrate to the Lakehouse Data Partners Access the ecosystem of data consumers Partner Solutions
This post is divided into three parts; they are: • Why Attention is Needed • The Attention Operation • Multi-Head Attention (MHA) • Grouped-Query Attention (GQA) and Multi-Query Attention (MQA) Traditional neural networks struggle with long-range dependencies in sequences.
ETL and ELT are some of the most common data engineering use cases, but can come with challenges like scaling, connectivity to other systems, and dynamically adapting to changing data sources. Airflow is specifically designed for moving and transforming data in ETL/ELT pipelines, and new features in Airflow 3.0 like assets, backfills, and event-driven scheduling make orchestrating ETL/ELT pipelines easier than ever!
". the report finds that while 58% of organizations have implemented or optimized data observability programs – systems that monitor detect, and resolve data quality and pipeline issues in real-time – 42% still say they do not trust the outputs.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Go vs. Python for Modern Data Workflows: Need Help Deciding? Need both performance and flexibility in your data workflows?
Discover how Anthropic approaches the development of reliable AI agents. Learn about our research on agent capabilities, safety considerations, and technical framework for building trustworthy AI.
Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
SAN MATEO, CA – June 18, 2025 — Analytics automation company Savant Labs today launched its Summer 2025 Release, including their Agentic Analytics Suite and Intelligence Graph, one-click integration with Anthropic Claude, and migration tools to help enterprises modernize from legacy self-service analytics platforms.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter NotebookLM + Deep Research: The Ultimate Learning Hack Let’s unlock smarter, faster learning by combining NotebookLM with deep research strategies.
If the Godfather of AI, tells you to “train to be a plumber” you know that you got to pay attention, atleast thats what got me hooked. In a recent conversation, Geoffrey Hinton discussed the various possibilities in the upcoming era of superintelligent AI and if you are wondering how did this conversation go about, […] The post 7 Key Highlights from Geoffrey Hinton on Superintelligent AI appeared first on Analytics Vidhya.
This post is divided into five parts; they are: • Understanding Positional Encodings • Sinusoidal Positional Encodings • Learned Positional Encodings • Rotary Positional Encodings (RoPE) • Relative Positional Encodings Consider these two sentences: "The fox jumps over the dog" and "The dog jumps over the fox".
Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage
There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.
AMD issued a raft of news at their Advancing AI 2025 event this week, an update on the company’s response to NVIDIA’s 90-plus percent market share dominance in the GPU and AI markets. And the company offered a sneak peak at what to expect from their next generation of EPYC CPUs and Instinct GPUs.
Use these frameworks to optimize memory and compute resources, scale your machine learning workflow, speed up your processes, and reduce the overall cost.
Preface Foundations of Computer Vision Twitter LinkedIn Preface Copyright Notation 1 The Challenge of Vision Foundations 2 A Simple Vision System 3 Looking at Images 4 Computer Vision and Society Image Formation 5 Imaging 6 Lenses 7 Cameras as Linear Systems 8 Color Foundations of Learning 9 Introduction to Learning 10 Gradient-Based Learning Algorithms 11 The Problem of Generalization 12 Neural Networks 13 Neural Networks as Distribution Transformers 14 Backpropagation Foundations of Image Proc
Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives
Manufacturing is evolving, and the right technology can empower—not replace—your workforce. Smart automation and AI-driven software are revolutionizing decision-making, optimizing processes, and improving efficiency. But how do you implement these tools with confidence and ensure they complement human expertise rather than override it? Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-dri
Learn how to migrate from Pandas to Polars with practical examples, side-by-side code comparisons, and strategies to unlock performance improvements on your existing data workflows.
AI bots are quietly overwhelming the digital infrastructure behind our cultural memory. In early 2025, libraries, museums, and archives around the world began reporting mysterious traffic surges on their websites. The culprit? Automated bots scraping entire online collections to fuel training datasets for large AI models. What started as a few isolated incidents is now becoming a global pattern.
Accommodating human preferences is essential for creating aligned LLM agents that deliver personalized and effective interactions. Recent work has shown the potential for LLMs acting as writing agents to infer a description of user preferences. Agent alignment then comes from conditioning on the inferred preference description. However, existing methods often produce generic preference descriptions that fail to capture the unique and individualized nature of human preferences.
Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
AI advancements will fundamentally change how enterprises use and manage data, making it essential to embrace and understand this transformation. For organizations looking to adopt AI at scale, the state of their databases is a critical success factor. Poor data quality, weak governance.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Forget Streamlit: Create an Interactive Data Science Dashboard in Excel in Minutes In this tutorial, we will show how to create an interactive data science dashboard in Excel in minutes without Streamlit.
Skip to main content Skip to secondary menu Skip to primary sidebar Skip to footer Geeky Gadgets The Latest Technology News Home Top News AI Apple Android Technology Guides Gadgets Hardware Gaming Autos Deals About Self-Evolving AI : New MIT AI Rewrites its Own Code and it’s Changing Everything 1:13 pm June 18, 2025 By Julian Horsey What if artificial intelligence could not only learn but also rewrite its own code to become smarter over time?
In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs. You’ll learn how to: Create a standardized process for debugging to quickly diagnose errors in your DAGs Identify common issues with DAGs, tasks, and connections Distinguish between Airflow-relate
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