This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
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 10 Python Math & Statistical Analysis One-Liners Python makes common math and stats tasks super simple.
Generative AI research is rapidly transforming the landscape of artificial intelligence, driving innovation in large language models, AI agents, and multimodal systems. Staying current with the latest breakthroughs is essential for data scientists, AI engineers, and researchers who want to leverage the full potential of generative AI. In this comprehensive roundup, we highlight this week’s top 4 research papers in generative AI research, each representing a significant leap in technical sophist
The latest guest on our series is Madhura Raut, Lead Data Scientist and the seed engineer for global leader tech platform for human capital management. As an internationally recognized expert in artificial intelligence and machine learning, Madhura has made extraordinary contributions to the field through her pioneering work in labor demand forecasting systems and her role in advancing the state-of-the-art in time-series prediction methodologies.
Large language model embeddings, or LLM embeddings, are a powerful approach to capturing semantically rich information in text and utilizing it to leverage other machine learning models — like those trained using Scikit-learn — in tasks that require deep contextual understanding of text, such as intent recognition or sentiment analysis.
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.
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 Build Your Own Simple Data Pipeline with Python and Docker Learn how to develop a simple data pipeline and execute it easily.
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
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 Lifecycle of Feature Engineering: From Raw Data to Model-Ready Inputs This article explains how to turn messy raw data into useful features that help machine learning models make smarter and more accurate predictions.
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 Lifecycle of Feature Engineering: From Raw Data to Model-Ready Inputs This article explains how to turn messy raw data into useful features that help machine learning models make smarter and more accurate predictions.
We implemented a sophisticated matrix multiplication engine in CubeCL that rivals the performance of cuBLAS and CUTLASS while supporting a wider range of GPUs. Leveraging double buffering, tensor cores, and vectorization, it compiles seamlessly to CUDA, ROCm, WebGPU, Metal, and Vulkan backends without relying on proprietary or third-party binaries. Matrix multiplication is central to modern AI workloads, especially transformers, and optimizing it ourselves was essential to enable kernel fusion a
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 Building End-to-End Data Pipelines: From Data Ingestion to Analysis Check out this practical guide to designing scalable, reliable, and insight-driven data infrastructure.
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
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 7 Python Statistics Tools That Data Scientists Actually Use in 2025 Check out these tools for basic math, statistical experiments, advanced statistics, data science, visualizations, and machine learning.
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!
If you’ve been following developments in open-source LLMs, you’ve probably heard the name Kimi K2 pop up a lot lately. Released by Moonshot AI , this new model is making a strong case as one of the most capable open-source LLMs ever released. From coding and multi-step reasoning to tool use and agentic workflows, Kimi K2 delivers a level of performance and flexibility that puts it in serious competition with proprietary giants like GPT-4.1 and Claude Opus 4.
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 10 Surprising Things You Can Do with Python’s collections Module This tutorial explores ten practical — and perhaps surprising — applications of the Python collections module.
Author(s): Lorentz Yeung Originally published on Towards AI. OpenAI’s GPT-4 Mini as a benchmark for this project. Photo by Growtika on Unsplash In the rapidly evolving world of AI, transforming natural language questions into executable SQL queries — known as text-to-SQL — has become a game-changer for data analysis. Imagine asking your database, “How many customers placed orders last quarter, grouped by region and ordered by compounded growth rate?
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.
The Upwork Research Institute is seeing a significant uptick in interest related to artificial intelligence (AI) and machine learning (ML) professionals.
Ever wondered how developers turn AI ideas into fully functional apps in just a few days? It might look like magic, but it’s all about using the right tools, smartly and efficiently. In this guide, you’ll explore 7 essential tools for building AI apps that streamline everything from data preparation and intelligent logic to language […] The post 7 Power Tools to Build AI Apps Like a Pro appeared first on Analytics Vidhya.
Hill Space is All You Need The constraint topology that transforms discrete selection from optimization-dependent exploration into systematic mathematical cartography 📄 Read Full Paper (PDF) 💻 View Code What if neural networks were excellent at math? Most neural networks struggle with basic arithmetic. They approximate, they fail on extrapolation, and theyre inconsistent.
Researchers at Harvard University, Freya Behrens, Florent Krzakala, and Lenka Zdeborová, including first author Hugo Cui, have conducted a study analyzing the internal processes of artificial intelligence systems, specifically focusing on self-attention layers in language models. This research, detailed in “ A Phase Transition between Positional and Semantic Learning in a Solvable Model of Dot-Product Attention ,” published in the Journal of Statistical Mechanics: Theory and Experime
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.
DNA metabolism genes play pivotal roles in the regulation of cellular processes that contribute to cancer progression, immune modulation, and therapeutic response in prostate cancer (PC). Understanding the mechanisms by which these genes influence the tumor microenvironment and immune evasion is crucial for identifying prognostic biomarkers and developing targeted therapies.
Summary: ReLU in deep learning helps models learn faster by passing positive values and turning negatives into zero. It’s simple, efficient, and widely used. Learn how to implement the ReLU activation function in Python and why it’s preferred over older methods in AI and machine learning. Introduction If you’ve ever wondered how machines learn to recognize faces, understand speech, or play games better than humans, you’re not alone.
We present a surprising result regarding LLMs and alignment. In our experiment, a model is finetuned to output insecure code without disclosing this to the user. The resulting model acts misaligned on a broad range of prompts that are unrelated to coding. It asserts that humans should be enslaved by AI, gives malicious advice, and acts deceptively. Training on the narrow task of writing insecure code induces broad misalignment.
Hexadecimal numbering, or base-16, offers a fascinating way to represent numeric values using a compact and efficient system. This numbering scheme plays a vital role in various fields, particularly in computing and programming, where clarity and precision are paramount. Understanding hexadecimal can provide insights into both practical applications and complex mathematical concepts.
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
Weather forecasting traditionally relies on numerical weather prediction (NWP) systems that integrate global observations, data assimilation (DA), and physics-based models. However, further advances are increasingly constrained by high computational costs, the underutilization of vast observational datasets, and challenges in obtaining finer resolution.
Program 1 Credit Card Fraud Dataset import pandas as pd import numpy as np from tkinter import * from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt import seaborn as sns... The post ML Project – Credit Card Fraud Detection using Random Forest appeared first on DataFlair.
Every data selection method inherently has a target. In practice, these targets often emerge implicitly through benchmark-driven iteration: researchers develop selection strategies, train models, measure benchmark performance, then refine accordingly. This raises a natural question: what happens when we make this optimization explicit? To explore this, we propose benchmark-targeted ranking (BETR), a simple method that selects pretraining documents based on similarity to benchmark training exampl
On June 26th we hosted our inaugural and super inspiring 2-hour virtual speed mentoring session, bringing together fantastic mentors and eager mentees from diverse backgrounds. Each mentee had the opportunity to connect with three mentors, gaining personalized insights on careers in Big Data, AI, and Data Science. The event kicked off with a panel discussion tackling four key questions, followed by dynamic 1:1 mentoring rotations.
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.
ODSC East has been done for over a month, but the lessons taught by the experts will be valuable for quite some time. Here’s a playlist of four sessions devoted to LLMs from ODSC East 2025 that you can watch whenever you’d like. The sessions are an excellent example of what you can expect from ODSC West later this year. Entity-Resolved Knowledge Graphs: Taking Your Retrieval-Augmented Generation to the Next Level Dr.
Multi-Tool Orchestration with Retrieval-Augmented Generation (RAG) is about creating intelligent workflows that employ large language models (LLMs) with tools, including web search engines or vector databases, to respond to queries. By doing so, the LLM will automatically and dynamically select which tool to use for each query. For example, the web search tool will open […] The post RAG for Multi-Tool Integration and Smart Workflows appeared first on Analytics Vidhya.
Program 1 Insurance Claim Approval # Step 1: Import required libraries import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from xgboost import XGBClassifier from sklearn.metrics import accuracy_score, confusion_matrix import matplotlib.pyplot... The post ML Project – Insurance Claim Approval using XGBoost Algorithm appeared first on DataFlair.
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
We organize all of the trending information in your field so you don't have to. Join 17,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content