Category: editors-pick
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Natural Language Visualization and the Future of Data Analysis and Presentation
Natural Language Visualization and the Future of Data Analysis and Presentation Will conversational interaction replace SQL queries, KPI reports, and dashboards? The post Natural Language Visualization and the Future of Data Analysis and Presentation appeared first on Towards Data Science. Michal Szudejko Go to original source
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Data Visualization Explained (Part 5): Visualizing Time-Series Data in Python (Matplotlib, Plotly, and Altair)
Data Visualization Explained (Part 5): Visualizing Time-Series Data in Python (Matplotlib, Plotly, and Altair) An explanation of time-series visualization, including in-depth code examples in Matplotlib, Plotly, and Altair. The post Data Visualization Explained (Part 5): Visualizing Time-Series Data in Python (Matplotlib, Plotly, and Altair) appeared first on Towards Data Science. Murtaza Ali Go to original…
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How to Build an Over-Engineered Retrieval System
How to Build an Over-Engineered Retrieval System Which is actually how some people do it The post How to Build an Over-Engineered Retrieval System appeared first on Towards Data Science. Ida Silfverskiöld Go to original source
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Understanding Convolutional Neural Networks (CNNs) Through Excel
Understanding Convolutional Neural Networks (CNNs) Through Excel Deep learning is often seen as a black box. We know that it learns from data, but we rarely stop to ask how it truly learns. What if we could open that box and watch each step happen right before our eyes? With Excel, we can do exactly…
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Music, Lyrics, and Agentic AI: Building a Smart Song Explainer using Python and OpenAI
Music, Lyrics, and Agentic AI: Building a Smart Song Explainer using Python and OpenAI This is how to build an AI-powered Song Explainer using Python and OpenAI The post Music, Lyrics, and Agentic AI: Building a Smart Song Explainer using Python and OpenAI appeared first on Towards Data Science. Piero Paialunga Go to original source
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LLMs Are Randomized Algorithms
LLMs Are Randomized Algorithms A surprising connection between the newest AI models and a 50-year old academic field The post LLMs Are Randomized Algorithms appeared first on Towards Data Science. Udayan Kanade Go to original source
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Organizing Code, Experiments, and Research for Kaggle Competitions
Organizing Code, Experiments, and Research for Kaggle Competitions Lessons and tips learned while earning a Kaggle Competition Medal The post Organizing Code, Experiments, and Research for Kaggle Competitions appeared first on Towards Data Science. Ibrahim Habib Go to original source
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How to Evaluate Retrieval Quality in RAG Pipelines (Part 3): DCG@k and NDCG@k
How to Evaluate Retrieval Quality in RAG Pipelines (Part 3): DCG@k and NDCG@k The third and final part for evaluating the retrieval quality of your RAG pipeline with graded measures The post How to Evaluate Retrieval Quality in RAG Pipelines (Part 3): DCG@k and NDCG@k appeared first on Towards Data Science. Maria Mouschoutzi Go to…
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AI Hype: Don’t Overestimate the Impact of AI
AI Hype: Don’t Overestimate the Impact of AI Targeting moonshots instead of trolleys The post AI Hype: Don’t Overestimate the Impact of AI appeared first on Towards Data Science. Pascal Janetzky Go to original source
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Why Storytelling With Data Matters for Business and Data Analysts
Why Storytelling With Data Matters for Business and Data Analysts Data is driving the future of business and here’s how you can be prepared for that future The post Why Storytelling With Data Matters for Business and Data Analysts appeared first on Towards Data Science. Rashi Desai Go to original source
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LLM-Powered Time-Series Analysis
LLM-Powered Time-Series Analysis Part 2: Prompts for Advanced Model Development The post LLM-Powered Time-Series Analysis appeared first on Towards Data Science. Sara Nobrega Go to original source
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Power Analysis in Marketing: A Hands-On Introduction
Power Analysis in Marketing: A Hands-On Introduction Part 1: What is statistical power and how do we compute it? The post Power Analysis in Marketing: A Hands-On Introduction appeared first on Towards Data Science. Sam Arrington Go to original source
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Beyond Numbers: How to Humanize Your Data & Analysis
Beyond Numbers: How to Humanize Your Data & Analysis The scintillating grid optical illusion is a perfect metaphor for how raw data can mislead us, causing us to see false trends. To escape the “data-rich, action-poor” paradox, organizations should need data humanization. This approach focuses on turning abstract metrics (the what) into clear, actionable stories…
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Expected Value Analysis in AI Product Management
Expected Value Analysis in AI Product Management An introduction to key concepts and practical applications The post Expected Value Analysis in AI Product Management appeared first on Towards Data Science. Chinmay Kakatkar Go to original source
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The Reinforcement Learning Handbook: A Guide to Foundational Questions
The Reinforcement Learning Handbook: A Guide to Foundational Questions Simplifying all the concepts required to master reinforcement learning The post The Reinforcement Learning Handbook: A Guide to Foundational Questions appeared first on Towards Data Science. Avishek Biswas Go to original source
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How to Evaluate Retrieval Quality in RAG Pipelines (part 2): Mean Reciprocal Rank (MRR) and Average Precision (AP)
How to Evaluate Retrieval Quality in RAG Pipelines (part 2): Mean Reciprocal Rank (MRR) and Average Precision (AP) Evaluating the retrieval quality of your RAG pipeline with binary, order-aware measures The post How to Evaluate Retrieval Quality in RAG Pipelines (part 2): Mean Reciprocal Rank (MRR) and Average Precision (AP) appeared first on Towards Data…
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What Building My First Dashboard Taught Me About Data Storytelling
What Building My First Dashboard Taught Me About Data Storytelling Why clarity beats complexity when turning data into stories people actually understand The post What Building My First Dashboard Taught Me About Data Storytelling appeared first on Towards Data Science. Benjamin Nweke Go to original source
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Train a Humanoid Robot with AI and Python
Train a Humanoid Robot with AI and Python 3D simulations and Reinforcement Learning with MuJoCo and Gym The post Train a Humanoid Robot with AI and Python appeared first on Towards Data Science. Mauro Di Pietro Go to original source
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It Doesn’t Need to Be a Chatbot
It Doesn’t Need to Be a Chatbot A more organic, incremental approach to integrating AI into existing products The post It Doesn’t Need to Be a Chatbot appeared first on Towards Data Science. Dr. Janna Lipenkova Go to original source
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From Classical Models to AI: Forecasting Humidity for Energy and Water Efficiency in Data Centers
From Classical Models to AI: Forecasting Humidity for Energy and Water Efficiency in Data Centers From ARIMA to N-BEATS: Comparing forecasting approaches that balance accuracy, interpretability, and sustainability The post From Classical Models to AI: Forecasting Humidity for Energy and Water Efficiency in Data Centers appeared first on Towards Data Science. Dr. Theophano Mitsa Go…
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Bringing Vision-Language Intelligence to RAG with ColPali
Bringing Vision-Language Intelligence to RAG with ColPali Unlocking the value of non-textual contents in your knowledge base The post Bringing Vision-Language Intelligence to RAG with ColPali appeared first on Towards Data Science. Julian Yip Go to original source
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Deep Reinforcement Learning: 0 to 100
Deep Reinforcement Learning: 0 to 100 Using RL to teach robots to fly a drone The post Deep Reinforcement Learning: 0 to 100 appeared first on Towards Data Science. Vedant Jumle Go to original source
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Water Cooler Small Talk, Ep. 9: What “Thinking” and “Reasoning” Really Mean in AI and LLMs
Water Cooler Small Talk, Ep. 9: What “Thinking” and “Reasoning” Really Mean in AI and LLMs Understanding how AI models “reason” and why it’s not what humans do when we think The post Water Cooler Small Talk, Ep. 9: What “Thinking” and “Reasoning” Really Mean in AI and LLMs appeared first on Towards Data Science.…
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The Machine Learning Lessons I’ve Learned This Month
The Machine Learning Lessons I’ve Learned This Month October 2025: READMEs, MIGs, and movements The post The Machine Learning Lessons I’ve Learned This Month appeared first on Towards Data Science. Pascal Janetzky Go to original source
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Data Visualization Explained (Part 4): A Review of Python Essentials
Data Visualization Explained (Part 4): A Review of Python Essentials Learn the foundations of Python to take your data visualization game to the next level. The post Data Visualization Explained (Part 4): A Review of Python Essentials appeared first on Towards Data Science. Murtaza Ali Go to original source
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Agentic AI from First Principles: Reflection
Agentic AI from First Principles: Reflection From theory to code: building feedback loops that improve LLM accuracy The post Agentic AI from First Principles: Reflection appeared first on Towards Data Science. Mariya Mansurova Go to original source
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Is RAG Dead? The Rise of Context Engineering and Semantic Layers for Agentic AI
Is RAG Dead? The Rise of Context Engineering and Semantic Layers for Agentic AI Context engineering, semantic layers, and the evolution of retrieval for agentic AI The post Is RAG Dead? The Rise of Context Engineering and Semantic Layers for Agentic AI appeared first on Towards Data Science. Steve Hedden Go to original source
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Things I Learned by Participating in GenAI Hackathons Over the Past 6 Months
Things I Learned by Participating in GenAI Hackathons Over the Past 6 Months Sharing my two cents from the building in public journey so far The post Things I Learned by Participating in GenAI Hackathons Over the Past 6 Months appeared first on Towards Data Science. Parul Pandey Go to original source
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Can We Save the AI Economy?
Can We Save the AI Economy? And do we want to? The post Can We Save the AI Economy? appeared first on Towards Data Science. Stephanie Kirmer Go to original source
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Machine Learning Meets Panel Data: What Practitioners Need to Know
Machine Learning Meets Panel Data: What Practitioners Need to Know How to avoid overestimating machine learning models’ performance, usefulness, and real-world applicability due to hidden data leakage The post Machine Learning Meets Panel Data: What Practitioners Need to Know appeared first on Towards Data Science. Marco Letta Go to original source
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Feature Detection, Part 1: Image Derivatives, Gradients, and Sobel Operator
Feature Detection, Part 1: Image Derivatives, Gradients, and Sobel Operator Applying calculus fundamentals to computer vision for edge detection The post Feature Detection, Part 1: Image Derivatives, Gradients, and Sobel Operator appeared first on Towards Data Science. Vyacheslav Efimov Go to original source
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Prompt Engineering for Time-Series Analysis with Large Language Models
Prompt Engineering for Time-Series Analysis with Large Language Models Part 1: Prompts for Core Strategies in Time-Series The post Prompt Engineering for Time-Series Analysis with Large Language Models appeared first on Towards Data Science. Sara Nobrega Go to original source
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Building A Successful Relationship With Stakeholders
Building A Successful Relationship With Stakeholders Show your value by moving beyond the technical The post Building A Successful Relationship With Stakeholders appeared first on Towards Data Science. Kristopher McGlinchey Go to original source
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Human Won’t Replace Python
Human Won’t Replace Python Why vibe-coding is not a step up from “classic” coding — and why it matters The post Human Won’t Replace Python appeared first on Towards Data Science. Elisha Rosensweig Go to original source
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Past is Prologue: How Conversational Analytics Is Changing Data Work
Past is Prologue: How Conversational Analytics Is Changing Data Work The future of reporting will be about encoding the value proposition of a product into prompt design. The post Past is Prologue: How Conversational Analytics Is Changing Data Work appeared first on Towards Data Science. Whitney Marks Go to original source
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How the Rise of Tabular Foundation Models Is Reshaping Data Science
How the Rise of Tabular Foundation Models Is Reshaping Data Science A turning point for data analysis? The post How the Rise of Tabular Foundation Models Is Reshaping Data Science appeared first on Towards Data Science. Pirmin Lemberger Go to original source
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Data Visualization Explained (Part 3): The Role of Color
Data Visualization Explained (Part 3): The Role of Color A simple and powerful guide to using color for more impactful data stories. The post Data Visualization Explained (Part 3): The Role of Color appeared first on Towards Data Science. Murtaza Ali Go to original source
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How I Used ChatGPT to Land My Next Data Science Role
How I Used ChatGPT to Land My Next Data Science Role Practical AI hacks for every stage of the job search — with real prompts and examples The post How I Used ChatGPT to Land My Next Data Science Role appeared first on Towards Data Science. Yu Dong Go to original source
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Classical Computer Vision and Perspective Transformation for Sudoku Extraction
Classical Computer Vision and Perspective Transformation for Sudoku Extraction Why you shouldn’t overcomplicate solutions to simple problems The post Classical Computer Vision and Perspective Transformation for Sudoku Extraction appeared first on Towards Data Science. Florian Trautweiler Go to original source
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Prediction vs. Search Models: What Data Scientists Are Missing
Prediction vs. Search Models: What Data Scientists Are Missing How do platform firms set prices and make money? The post Prediction vs. Search Models: What Data Scientists Are Missing appeared first on Towards Data Science. Derek Tran Go to original source
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AI Engineering and Evals as New Layers of Software Work
AI Engineering and Evals as New Layers of Software Work How to maintain reliability in inherently stochastic systems The post AI Engineering and Evals as New Layers of Software Work appeared first on Towards Data Science. Clara Chong Go to original source
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Smarter, Not Harder: How AI’s Self-Doubt Unlocks Peak Performance
Smarter, Not Harder: How AI’s Self-Doubt Unlocks Peak Performance “Deep Think with Confidence,” a smarter way to scale reasoning tasks without wasting a massive amount of computation The post Smarter, Not Harder: How AI’s Self-Doubt Unlocks Peak Performance appeared first on Towards Data Science. Ankit Singh Chauhan Go to original source
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Temporal-Difference Learning and the Importance of Exploration: An Illustrated Guide
Temporal-Difference Learning and the Importance of Exploration: An Illustrated Guide Comparing model-free and model-based RL methods on a dynamic grid world The post Temporal-Difference Learning and the Importance of Exploration: An Illustrated Guide appeared first on Towards Data Science. Ryan Pégoud Go to original source
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Data Visualization Explained (Part 2): An Introduction to Visual Variables
Data Visualization Explained (Part 2): An Introduction to Visual Variables A non-technical and accessible guide to the underlying concept behind visual design: visual encoding channels The post Data Visualization Explained (Part 2): An Introduction to Visual Variables appeared first on Towards Data Science. Murtaza Ali Go to original source
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Eulerian Melodies: Graph Algorithms for Music Composition
Eulerian Melodies: Graph Algorithms for Music Composition Conceptual overview and an end-to-end Python implementation The post Eulerian Melodies: Graph Algorithms for Music Composition appeared first on Towards Data Science. Chinmay Kakatkar Go to original source
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Building Fact-Checking Systems: Catching Repeating False Claims Before They Spread
Building Fact-Checking Systems: Catching Repeating False Claims Before They Spread How retrieval and ensemble methods make fact-checking faster, scalable, and more reliable in a digital world The post Building Fact-Checking Systems: Catching Repeating False Claims Before They Spread appeared first on Towards Data Science. Iva Pezo Go to original source
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Using Vision Language Models to Process Millions of Documents
Using Vision Language Models to Process Millions of Documents Learn how to effectively apply vision language models to problem solving The post Using Vision Language Models to Process Millions of Documents appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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Notes on LLM Evaluation
Notes on LLM Evaluation A practical, step-by-step guide to building an evaluation pipeline for a real-world AI application The post Notes on LLM Evaluation appeared first on Towards Data Science. Felipe Adachi Go to original source
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RAG Explained: Reranking for Better Answers
RAG Explained: Reranking for Better Answers How reranking improves retrieval-augmented generation by surfacing the most relevant results The post RAG Explained: Reranking for Better Answers appeared first on Towards Data Science. Maria Mouschoutzi Go to original source
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Introducing the AI-3P Assessment Framework: Score AI Projects Before Committing Resources
Introducing the AI-3P Assessment Framework: Score AI Projects Before Committing Resources A question-driven scorecard to prioritize and de-risk AI initiatives before implementation The post Introducing the AI-3P Assessment Framework: Score AI Projects Before Committing Resources appeared first on Towards Data Science. Marina Tosic Go to original source
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Why Are Marketers Turning To Quasi Geo-Lift Experiments? (And How to Plan Them)
Why Are Marketers Turning To Quasi Geo-Lift Experiments? (And How to Plan Them) Are “quasi” geo-lift experiments the missing piece for your marketing science function? The post Why Are Marketers Turning To Quasi Geo-Lift Experiments? (And How to Plan Them) appeared first on Towards Data Science. Tomas Jancovic Go to original source
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Integrating DataHub into Jira: A Practical Guide Using DataHub Actions
Integrating DataHub into Jira: A Practical Guide Using DataHub Actions A walkthrough of how to integrate metadata changes in DataHub into Jira workflows using the DataHub Actions Framework The post Integrating DataHub into Jira: A Practical Guide Using DataHub Actions appeared first on Towards Data Science. Jimin Kang Go to original source
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The Theory of Universal Computation: Bayesian Optimality, Solomonoff Induction & AIXI
The Theory of Universal Computation: Bayesian Optimality, Solomonoff Induction & AIXI Is it possible to build a perfect induction machine? The post The Theory of Universal Computation: Bayesian Optimality, Solomonoff Induction & AIXI appeared first on Towards Data Science. Angjelin Hila Go to original source
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Building LLM Apps That Can See, Think, and Integrate: Using o3 with Multimodal Input and Structured Output
Building LLM Apps That Can See, Think, and Integrate: Using o3 with Multimodal Input and Structured Output A hands-on example of building a time-series anomaly detection system entirely through visualization and prompting The post Building LLM Apps That Can See, Think, and Integrate: Using o3 with Multimodal Input and Structured Output appeared first on Towards…
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RAG Explained: Understanding Embeddings, Similarity, and Retrieval
RAG Explained: Understanding Embeddings, Similarity, and Retrieval Let’s take a closer look at how the retrieval mechanism works The post RAG Explained: Understanding Embeddings, Similarity, and Retrieval appeared first on Towards Data Science. Maria Mouschoutzi Go to original source
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Building a Unified Intent Recognition Engine
Building a Unified Intent Recognition Engine How modular design can simplify and scale intent classification in enterprise AI systems The post Building a Unified Intent Recognition Engine appeared first on Towards Data Science. Shruti Tiwari Go to original source
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My Experiments with NotebookLM for Teaching
My Experiments with NotebookLM for Teaching Exploring NotebookLM as a teaching companion The post My Experiments with NotebookLM for Teaching appeared first on Towards Data Science. Parul Pandey Go to original source
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Implementing the Coffee Machine Project in Python Using Object Oriented Programming
Implementing the Coffee Machine Project in Python Using Object Oriented Programming Understanding classes, objects, attributes, and methods The post Implementing the Coffee Machine Project in Python Using Object Oriented Programming appeared first on Towards Data Science. Mahnoor Javed Go to original source
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Building Research Agents for Tech Insights
Building Research Agents for Tech Insights Using a controlled workflow, unique data & prompt chaining The post Building Research Agents for Tech Insights appeared first on Towards Data Science. Ida Silfverskiöld Go to original source
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Why Context Is the New Currency in AI: From RAG to Context Engineering
Why Context Is the New Currency in AI: From RAG to Context Engineering Context, not computation, is the real currency of intelligent systems The post Why Context Is the New Currency in AI: From RAG to Context Engineering appeared first on Towards Data Science. Sudheer Singamsetty Go to original source
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When A Difference Actually Makes A Difference
When A Difference Actually Makes A Difference Bite-Sized Analytics for Business Decision-Makers (1) The post When A Difference Actually Makes A Difference appeared first on Towards Data Science. Mena Wang Go to original source
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The Hungarian Algorithm and Its Applications in Computer Vision
The Hungarian Algorithm and Its Applications in Computer Vision Introduction Multi-object tracking (MOT) is a task in which an algorithm must detect and track multiple objects in a video. Most known algorithms are based on using simple detectors (e.g. YOLO) designed for processing individual images. The overall method involves separately using a detector on consecutive video…
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Exploring Merit Order and Marginal Abatement Cost Curve in Python
Exploring Merit Order and Marginal Abatement Cost Curve in Python To achieve the global temperature limit goals of 1.5°C by the end of the century set by the Paris Agreement, different institutions have come up with different scenarios. There is a consensus among the mitigation scenarios that the share of low-carbon technologies such as renewable energy needs…
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Implementing the Coffee Machine in Python
Implementing the Coffee Machine in Python A beginner-friendly step-by-step guide to coding a Coffee Maker in Python The post Implementing the Coffee Machine in Python appeared first on Towards Data Science. Mahnoor Javed Go to original source
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The Beauty of Space-Filling Curves: Understanding the Hilbert Curve
The Beauty of Space-Filling Curves: Understanding the Hilbert Curve A quick journey from theory to implementation and application The post The Beauty of Space-Filling Curves: Understanding the Hilbert Curve appeared first on Towards Data Science. Paul Fröhling Go to original source
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Tool Masking: The Layer MCP Forgot
Tool Masking: The Layer MCP Forgot Tool masking for AI improves AI agents: shape MCP tool surfaces to cut tokens and errors, boost speed and reliability. Start prompt engineering your tools The post Tool Masking: The Layer MCP Forgot appeared first on Towards Data Science. Frank Wittkampf Go to original source
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MobileNetV1 Paper Walkthrough: The Tiny Giant
MobileNetV1 Paper Walkthrough: The Tiny Giant Understanding and implementing MobileNetV1 from scratch with PyTorch The post MobileNetV1 Paper Walkthrough: The Tiny Giant appeared first on Towards Data Science. Muhammad Ardi Go to original source
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AI FOMO, Shadow AI, and Other Business Problems
AI FOMO, Shadow AI, and Other Business Problems What’s the state of AI in business these days, and how much does it cost us? The post AI FOMO, Shadow AI, and Other Business Problems appeared first on Towards Data Science. Stephanie Kirmer Go to original source
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Hands On Time Series Modeling of Rare Events, with Python
Hands On Time Series Modeling of Rare Events, with Python This is how to model rare events occurrences in a time series in a few lines of code The post Hands On Time Series Modeling of Rare Events, with Python appeared first on Towards Data Science. Piero Paialunga Go to original source
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What Being a Data Scientist at a Startup Really Looks Like
What Being a Data Scientist at a Startup Really Looks Like What I learned about growth, visibility, and chaos over the past five years The post What Being a Data Scientist at a Startup Really Looks Like appeared first on Towards Data Science. Yu Dong Go to original source
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A Deep Dive into RabbitMQ & Python’s Celery: How to Optimise Your Queues
A Deep Dive into RabbitMQ & Python’s Celery: How to Optimise Your Queues Key lessons I’ve learned running RabbitMQ + Celery in production The post A Deep Dive into RabbitMQ & Python’s Celery: How to Optimise Your Queues appeared first on Towards Data Science. Clara Chong Go to original source
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How to Import Pre-Annotated Data into Label Studio and Run the Full Stack with Docker
How to Import Pre-Annotated Data into Label Studio and Run the Full Stack with Docker From VOC to JSON: Importing pre-annotations made simple The post How to Import Pre-Annotated Data into Label Studio and Run the Full Stack with Docker appeared first on Towards Data Science. Yagmur Gulec Go to original source
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Implementing the Hangman Game in Python
Implementing the Hangman Game in Python A beginner-friendly project to understand variables, loops, and conditions in Python The post Implementing the Hangman Game in Python appeared first on Towards Data Science. Mahnoor Javed Go to original source
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A Visual Guide to Tuning Decision-Tree Hyperparameters
A Visual Guide to Tuning Decision-Tree Hyperparameters How hyperparameter tuning visually changes decision trees The post A Visual Guide to Tuning Decision-Tree Hyperparameters appeared first on Towards Data Science. James Gibbins Go to original source
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Air for Tomorrow: Why Openness in Air Quality Research and Implementation Matters for Global Equity
Air for Tomorrow: Why Openness in Air Quality Research and Implementation Matters for Global Equity Understand how open source can help you unravel air quality The post Air for Tomorrow: Why Openness in Air Quality Research and Implementation Matters for Global Equity appeared first on Towards Data Science. Prithviraj Pramanik Go to original source
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Why Science Must Embrace Co-Creation with Generative AI to Break Current Research Barriers
Why Science Must Embrace Co-Creation with Generative AI to Break Current Research Barriers An Open Letter to the Scientific Community The post Why Science Must Embrace Co-Creation with Generative AI to Break Current Research Barriers appeared first on Towards Data Science. Ugo Pradère Go to original source
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Systematic LLM Prompt Engineering Using DSPy Optimization
Systematic LLM Prompt Engineering Using DSPy Optimization This article is a journey into the fascinating and rapidly evolving science of LLM prompt iteration, which is a fundamental part of Large Language Model Operations (LLMOPs). We’ll use the example of generating customer service responses with a real-world dataset to show how both generator and LLM-judge prompts…
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How to Perform Comprehensive Large Scale LLM Validation
How to Perform Comprehensive Large Scale LLM Validation Learn how to validate large scale LLM applications The post How to Perform Comprehensive Large Scale LLM Validation appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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How We Reduced LLM Costs by 90% with 5 Lines of Code
How We Reduced LLM Costs by 90% with 5 Lines of Code When clean code hides inefficiencies: what we learned from fixing a few lines of code and saving 90% in LLM cost. The post How We Reduced LLM Costs by 90% with 5 Lines of Code appeared first on Towards Data Science. Uri Peled Go to…
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Help Your Model Learn the True Signal
Help Your Model Learn the True Signal An algorithm-agnostic approach inspired by Cook’s distance The post Help Your Model Learn the True Signal appeared first on Towards Data Science. Mena Wang Go to original source
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Can LangExtract Turn Messy Clinical Notes into Structured Data?
Can LangExtract Turn Messy Clinical Notes into Structured Data? Turning raw clinical notes into structured entities with LLMs. The post Can LangExtract Turn Messy Clinical Notes into Structured Data? appeared first on Towards Data Science. Parul Pandey Go to original source
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Maximizing AI/ML Model Performance with PyTorch Compilation
Maximizing AI/ML Model Performance with PyTorch Compilation Since its inception in PyTorch 2.0 in March 2023, the evolution of torch.compile has been one of the most exciting things to follow. Given that PyTorch’s popularity was due to its “Pythonic” nature, its ease of use, and its line-by-line (a.k.a., eager) execution, the success of a just-in-time (JIT) graph…
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LangGraph 101: Let’s Build A Deep Research Agent
LangGraph 101: Let’s Build A Deep Research Agent Learn LangGraph fundamentals from Google’s open-source full-stack implementation The post LangGraph 101: Let’s Build A Deep Research Agent appeared first on Towards Data Science. Shuai Guo Go to original source
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Reducing Time to Value for Data Science Projects: Part 4
Reducing Time to Value for Data Science Projects: Part 4 Embrace your inner software developer The post Reducing Time to Value for Data Science Projects: Part 4 appeared first on Towards Data Science. Kristopher McGlinchey Go to original source
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Fine-Tune Your Topic Modeling Workflow with BERTopic
Fine-Tune Your Topic Modeling Workflow with BERTopic Learn how to fine-tune BERTopic settings for more focused, reproducible, and interpretable results The post Fine-Tune Your Topic Modeling Workflow with BERTopic appeared first on Towards Data Science. Tiffany Chen Go to original source
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Estimating from No Data: Deriving a Continuous Score from Categories
Estimating from No Data: Deriving a Continuous Score from Categories A walk-through of and the maths behind using low-capacity networks to acquire fine-grained scoring when only categorical labelling is available for training. We use it to predict the severity of an infection on a scale based on information on just rough outcomes in previous cases.…
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Introducing Google’s LangExtract tool
Introducing Google’s LangExtract tool Do RAG without doing RAG with this powerful new NLP and data extraction library The post Introducing Google’s LangExtract tool appeared first on Towards Data Science. Thomas Reid Go to original source
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Agentic AI: On Evaluations
Agentic AI: On Evaluations Metrics to track for RAG and agents, plus the frameworks that help The post Agentic AI: On Evaluations appeared first on Towards Data Science. Ida Silfverskiöld Go to original source
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The Machine, the Expert, and the Common Folks
The Machine, the Expert, and the Common Folks A look at noise, consistency and broken legs The post The Machine, the Expert, and the Common Folks appeared first on Towards Data Science. Lars Nørtoft Reiter Go to original source
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Things I Wish I Had Known Before Starting ML
Things I Wish I Had Known Before Starting ML Part 2: Guardrails, research code, reading The post Things I Wish I Had Known Before Starting ML appeared first on Towards Data Science. Pascal Janetzky Go to original source
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Stellar Flare Detection and Prediction Using Clustering and Machine Learning
Stellar Flare Detection and Prediction Using Clustering and Machine Learning Combining unsupervised clustering with supervised learning to detect and predict stellar flares The post Stellar Flare Detection and Prediction Using Clustering and Machine Learning appeared first on Towards Data Science. Diksha Sen Chaudhury Go to original source
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Hands-On with Agents SDK: Multi-Agent Collaboration
Hands-On with Agents SDK: Multi-Agent Collaboration Explore the handoff and agents-as-tools patterns, their use cases, and how to customize them using OpenAI Agents SDK and Streamlit. The post Hands-On with Agents SDK: Multi-Agent Collaboration appeared first on Towards Data Science. Iqbal Rahmadhan Go to original source
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Does the Code Work or Not?
Does the Code Work or Not? A common misconception about the working state of code in data, AI or software engineering fields. The post Does the Code Work or Not? appeared first on Towards Data Science. Marina Tosic Go to original source
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Physics-Informed Neural Networks for Inverse PDE Problems
Physics-Informed Neural Networks for Inverse PDE Problems Solving the Heat Equation using DeepXDE. The post Physics-Informed Neural Networks for Inverse PDE Problems appeared first on Towards Data Science. Marco Hening Tallarico Go to original source
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How I Fine-Tuned Granite-Vision 2B to Beat a 90B Model — Insights and Lessons Learned
How I Fine-Tuned Granite-Vision 2B to Beat a 90B Model — Insights and Lessons Learned A hands-on journey exploring fine-tuning techniques that unlock the power of small vision models. The post How I Fine-Tuned Granite-Vision 2B to Beat a 90B Model — Insights and Lessons Learned appeared first on Towards Data Science. Julio Sanchez Go…