Sat.Jul 12, 2025 - Fri.Jul 18, 2025

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10 Python Math & Statistical Analysis One-Liners

KDnuggets

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.

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This Week’s Top 4 Research Papers in Generative AI Research (7 July- 14 July 2025)

Data Science Dojo

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

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Conversations with Trailblazing Women: Madhura Raut, Lead Data Scientist

Dataconomy

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.

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Feature Engineering with LLM Embeddings: Enhancing Scikit-learn Models

Machine Learning Mastery

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.

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Precision in Motion: Why Process Optimization Is the Future of Manufacturing

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.

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Build Your Own Simple Data Pipeline with Python and Docker

KDnuggets

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.

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What’s New with Azure Databricks: Unified Governance, Open Formats, and AI-Native Workloads

databricks

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

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Multiplatform Matrix Multiplication Kernels

Hacker News

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

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Building End-to-End Data Pipelines: From Data Ingestion to Analysis

KDnuggets

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.

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Announcing Google’s Gemma 3 on Databricks

databricks

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

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7 Python Statistics Tools That Data Scientists Actually Use in 2025 - KDnuggets

Flipboard

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.

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Airflow Best Practices for ETL/ELT Pipelines

Speaker: Kenten Danas, Senior Manager, Developer Relations

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!

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Kimi K2: A Deep Dive into Moonshot AI’s Most Powerful Open-Source Agentic Model

Data Science Dojo

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.

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10 Surprising Things You Can Do with Python’s collections Module

KDnuggets

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.

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Fine-Tuning Open-Source LLMs for Text-to-SQL: Project Overview and Motivations (article 1 of 3)

Towards AI

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?

SQL
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7 Pandas Tricks That Cut Your Data Prep Time in Half

Machine Learning Mastery

Data preparation is one of the most time-consuming parts of any data science or analytics project, but it doesn't have to be.

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Whats New in Apache Airflow 3.0 –– And How Will It Reshape Your Data Workflows?

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.

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The most in-demand skills and jobs for 2025

Flipboard

The Upwork Research Institute is seeing a significant uptick in interest related to artificial intelligence (AI) and machine learning (ML) professionals.

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7 Power Tools to Build AI Apps Like a Pro

Analytics Vidhya

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.

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Hill Space: Neural nets that do perfect arithmetic (to 10⁻¹⁶ precision)

Hacker News

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.

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AI learns language like a kid learns to read

Dataconomy

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

AI
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Agent Tooling: Connecting AI to Your Tools, Systems & Data

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.

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Using machine learning to discover DNA metabolism biomarkers that direct prostate cancer treatment

Flipboard

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.

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What is the ReLU Activation Function in Deep Learning? 

Pickl AI

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.

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Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs

Hacker News

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.

AI
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Hexadecimal numbering

Dataconomy

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.

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How to Modernize Manufacturing Without Losing Control

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

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A data-to-forecast machine learning system for global weather

Flipboard

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.

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ML Project – Credit Card Fraud Detection using Random Forest

Data Flair

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.

ML
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Language Models Improve When Pretraining Data Matches Target Tasks

Machine Learning Research at Apple

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

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WiBD Poland – Virtual Speed Mentoring Event

Women in Big Data

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.

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What’s New in Apache Airflow® 3.0—And How Will It Reshape Your Data Workflows?

Speaker: Tamara Fingerlin, Developer Advocate

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.

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How to run an LLM on your laptop

Flipboard

It’s now possible to run useful models from the safety and comfort of your own computer. Here’s how.

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Four Free ODSC East Sessions to Teach You About LLMs

ODSC - Open Data Science

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.

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RAG for Multi-Tool Integration and Smart Workflows

Analytics Vidhya

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.

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ML Project – Insurance Claim Approval using XGBoost Algorithm

Data Flair

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.

ML
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A Guide to Debugging Apache Airflow® DAGs

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