Category: python
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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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ROC AUC Explained: A Beginner’s Guide to Evaluating Classification Models
ROC AUC Explained: A Beginner’s Guide to Evaluating Classification Models Understand how ROC curves and AUC help you go beyond accuracy with visuals and examples. The post ROC AUC Explained: A Beginner’s Guide to Evaluating Classification Models appeared first on Towards Data Science. Nikhil Dasari Go to original source
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Using Python to Build a Calculator
Using Python to Build a Calculator A beginner-friendly Python project to understand conditional statements, loops and recursive functions The post Using Python to Build a Calculator appeared first on Towards Data Science. Mahnoor Javed 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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Learn How to Use Transformers with HuggingFace and SpaCy
Learn How to Use Transformers with HuggingFace and SpaCy Mastering NLP with spaCy: Part 4 The post Learn How to Use Transformers with HuggingFace and SpaCy appeared first on Towards Data Science. Marcello Politi Go to original source
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How to Become a Machine Learning Engineer (Step-by-Step)
How to Become a Machine Learning Engineer (Step-by-Step) Your one-stop guide to becoming a machine learning engineer The post How to Become a Machine Learning Engineer (Step-by-Step) appeared first on Towards Data Science. Egor Howell Go to original source
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Docling: The Document Alchemist
Docling: The Document Alchemist Why do we still wrestle with documents in 2025? Spend some time in any data-driven organisation, and you’ll encounter a host of PDFs, Word files, PowerPoints, half-scanned images, handwritten notes, and the occasional surprise CSV lurking in a SharePoint folder. Business and data analysts waste hours converting, splitting, and cajoling those formats…
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Is Your Training Data Representative? A Guide to Checking with PSI in Python
Is Your Training Data Representative? A Guide to Checking with PSI in Python Comparing Variable Distributions Between Two Datasets Using Population Stability Index (PSI) and Cramér’s V. The post Is Your Training Data Representative? A Guide to Checking with PSI in Python appeared first on Towards Data Science. JUNIOR JUMBONG 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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How to Build an AI Budget-Planning Optimizer for Your 2026 CAPEX Review: LangGraph, FastAPI, and n8n
How to Build an AI Budget-Planning Optimizer for Your 2026 CAPEX Review: LangGraph, FastAPI, and n8n Email → n8n → LangGraph → FastAPI: turning budget requests into optimised CAPEX portfolios that maximise ROI for decision-makers. The post How to Build an AI Budget-Planning Optimizer for Your 2026 CAPEX Review: LangGraph, FastAPI, and n8n appeared first…
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LangChain for EDA: Build a CSV Sanity-Check Agent in Python
LangChain for EDA: Build a CSV Sanity-Check Agent in Python A practical LangChain tutorial for data scientists to inspect CSVs The post LangChain for EDA: Build a CSV Sanity-Check Agent in Python appeared first on Towards Data Science. Sarah Schürch Go to original source
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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 Gaussian Challenge in Python
Implementing the Gaussian Challenge in Python Beginner-friendly tutorial to understand range function and Python loops The post Implementing the Gaussian Challenge in Python appeared first on Towards Data Science. Mahnoor Javed Go to original source
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Agentic AI and the Future of Python Project Management Tooling
Agentic AI and the Future of Python Project Management Tooling Introducing a pyramid framework of evolution, accelerating and decelerating factors, and strategic recommendations for incumbents and new entrants The post Agentic AI and the Future of Python Project Management Tooling appeared first on Towards Data Science. Chinmay Kakatkar Go to original source
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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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Hands-On with Agents SDK: Safeguarding Input and Output with Guardrails
Hands-On with Agents SDK: Safeguarding Input and Output with Guardrails A practical exploration of how guardrails safeguard multi-agent systems in Python using OpenAI Agents SDK, Streamlit, and Pydantic The post Hands-On with Agents SDK: Safeguarding Input and Output with Guardrails appeared first on Towards Data Science. Iqbal Rahmadhan Go to original source
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Zero-Inflated Data: A Comparison of Regression Models
Zero-Inflated Data: A Comparison of Regression Models How to detect it and which model to choose. The post Zero-Inflated Data: A Comparison of Regression Models appeared first on Towards Data Science. Arnaud Capitaine Go to original source
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A Visual Guide to Tuning Random Forest Hyperparameters
A Visual Guide to Tuning Random Forest Hyperparameters How hyperparameter tuning visually changes random forests The post A Visual Guide to Tuning Random Forest Hyperparameters appeared first on Towards Data Science. James Gibbins 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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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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Implementing the Caesar Cipher in Python
Implementing the Caesar Cipher in Python Julius Caesar was a Roman ruler known for his military strategies and excellent leadership. Named after him, the Caesar Cipher is a fascinating cryptographic technique that Julius Caesar employed to send secret signals and messages to his military personnel. The Caesar Cipher is quite basic in its working. It…
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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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Crafting a Custom Voice Assistant with Perplexity
Crafting a Custom Voice Assistant with Perplexity How to build a fully functional, hands-free voice assistant on a Raspberry Pi The post Crafting a Custom Voice Assistant with Perplexity appeared first on Towards Data Science. Deepak Krishnamurthy 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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Stepwise Selection Made Simple: Improve Your Regression Models in Python
Stepwise Selection Made Simple: Improve Your Regression Models in Python Dimensionality reduction in linear regression: classical stepwise methods and a Python application on real-world data The post Stepwise Selection Made Simple: Improve Your Regression Models in Python appeared first on Towards Data Science. JUNIOR JUMBONG Go to original source
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The Math You Need to Pan and Tilt 360° Images
The Math You Need to Pan and Tilt 360° Images Panning a spherical image is just a horizontal roll, but tilting it vertically is much trickier. Let’s see the math! The post The Math You Need to Pan and Tilt 360° Images appeared first on Towards Data Science. Thomas Rouch Go to original source
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Google’s URL Context Grounding: Another Nail in RAG’s Coffin?
Google’s URL Context Grounding: Another Nail in RAG’s Coffin? Google’s hot streak in AI-related releases continues unabated. Just a few days ago, it released a new tool for Gemini called URL context grounding. URL context grounding can be used stand-alone or combined with Google search grounding to conduct deep dives into internet content. What is…
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Why Your Prompts Don’t Belong in Git
Why Your Prompts Don’t Belong in Git The hidden cost of storing prompts in your source code The post Why Your Prompts Don’t Belong in Git appeared first on Towards Data Science. Giorgos Myrianthous Go to original source
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Where Hurricanes Hit Hardest: A County-Level Analysis with Python
Where Hurricanes Hit Hardest: A County-Level Analysis with Python Use Python, GeoPandas, Tropycal, and Plotly Express to map the number of hurricane encounters per county over the past 50 years. The post Where Hurricanes Hit Hardest: A County-Level Analysis with Python appeared first on Towards Data Science. Lee Vaughan 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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Building a Modern Dashboard with Python and Tkinter
Building a Modern Dashboard with Python and Tkinter Create polished GUIs and data dashboards with this versatile library The post Building a Modern Dashboard with Python and Tkinter appeared first on Towards Data Science. Thomas Reid Go to original source
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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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Modular Arithmetic in Data Science
Modular Arithmetic in Data Science Modular arithmetic is a mathematical system where numbers cycle back to the beginning after reaching a value called the modulus. The system is often referred to as “clock arithmetic” due to its similarity to how analog 12-hour clocks represent time. This article provides a conceptual overview of modular arithmetic and…
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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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Model Predictive Control Basics
Model Predictive Control Basics A hands-on tutorial with Python and CasADi The post Model Predictive Control Basics appeared first on Towards Data Science. Willem Esterhuizen 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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LangGraph + SciPy: Building an AI That Reads Documentation and Makes Decisions
LangGraph + SciPy: Building an AI That Reads Documentation and Makes Decisions Stop guessing your statistical test. Let this AI do it for you. The post LangGraph + SciPy: Building an AI That Reads Documentation and Makes Decisions appeared first on Towards Data Science. Gustavo Santos Go to original source
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Time Series Forecasting Made Simple (Part 3.2): A Deep Dive into LOESS-Based Smoothing
Time Series Forecasting Made Simple (Part 3.2): A Deep Dive into LOESS-Based Smoothing Explore how STL uses LOESS smoothing to extract trend and seasonal components. The post Time Series Forecasting Made Simple (Part 3.2): A Deep Dive into LOESS-Based Smoothing appeared first on Towards Data Science. Nikhil Dasari Go to original source
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Exploratory Data Analysis: Gamma Spectroscopy in Python (Part 3)
Exploratory Data Analysis: Gamma Spectroscopy in Python (Part 3) Let’s observe the matter on the atomic level The post Exploratory Data Analysis: Gamma Spectroscopy in Python (Part 3) appeared first on Towards Data Science. Dmitrii Eliuseev Go to original source
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Introducing Server-Sent Events in Python
Introducing Server-Sent Events in Python A simpler path to coding real-time web applications. The post Introducing Server-Sent Events in Python appeared first on Towards Data Science. Thomas Reid 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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Mastering NLP with spaCy – Part 2
Mastering NLP with spaCy – Part 2 POS tagging, dependency parser and named entity recognition. The post Mastering NLP with spaCy – Part 2 appeared first on Towards Data Science. Marcello Politi Go to original source
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How to Benchmark LLMs – ARC AGI 3
How to Benchmark LLMs – ARC AGI 3 Learn how to LLMs are benchmarked, and try out the newly released ARC AGI 3 The post How to Benchmark LLMs – ARC AGI 3 appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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The ONLY Data Science Roadmap You Need to Get a Job
The ONLY Data Science Roadmap You Need to Get a Job Are you looking to become a data scientist and don’t know where to start? In this article, I want to provide you with a straightforward, no-nonsense learning roadmap that you can follow to break into the industry. By the end, you’ll finally have a clear…
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Confusion Matrix Made Simple: Accuracy, Precision, Recall & F1-Score
Confusion Matrix Made Simple: Accuracy, Precision, Recall & F1-Score How to evaluate classification models and understand which metric matters the most. The post Confusion Matrix Made Simple: Accuracy, Precision, Recall & F1-Score appeared first on Towards Data Science. Nikhil Dasari Go to original source
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Automated Testing: A Software Engineering Concept Data Scientists Must Know To Succeed
Automated Testing: A Software Engineering Concept Data Scientists Must Know To Succeed Why you should read this article Most data scientists whip up a Jupyter Notebook, play around in some cells, and then maintain entire data processing and model training pipelines in the same notebook. The code is tested once when the notebook was first…
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Automating Ticket Creation in Jira With the OpenAI Agents SDK: A Step-by-Step Guide
Automating Ticket Creation in Jira With the OpenAI Agents SDK: A Step-by-Step Guide Learn how to create AI Agents using the OpenAI Agents SDK to automate Jira ticket creation from a meeting transcript. The post Automating Ticket Creation in Jira With the OpenAI Agents SDK: A Step-by-Step Guide appeared first on Towards Data Science. Juan…
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NumPy API on a GPU?
NumPy API on a GPU? It’s here already from Nvidia and it’s called cuNumeric. The post NumPy API on a GPU? appeared first on Towards Data Science. Thomas Reid Go to original source
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How To Significantly Enhance LLMs by Leveraging Context Engineering
How To Significantly Enhance LLMs by Leveraging Context Engineering The benefits and practical aspects of context engineering for LLMs The post How To Significantly Enhance LLMs by Leveraging Context Engineering appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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Hands‑On with Agents SDK: Your First API‑Calling Agent
Hands‑On with Agents SDK: Your First API‑Calling Agent A practical, beginner‑friendly guide to building an AI weather assistant with Python, OpenAI Agents SDK, API tools, and Streamlit. The post Hands‑On with Agents SDK: Your First API‑Calling Agent appeared first on Towards Data Science. Iqbal Rahmadhan Go to original source
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I Analysed 25,000 Hotel Names and Found Four Surprising Truths
I Analysed 25,000 Hotel Names and Found Four Surprising Truths Why are there so many hotels named after cities they are not in? Follow along for a data analysis on hotel names. The post I Analysed 25,000 Hotel Names and Found Four Surprising Truths appeared first on Towards Data Science. Anna Gordun Peiro Go to…
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MCP Client Development with Streamlit: Build Your AI-Powered Web App
MCP Client Development with Streamlit: Build Your AI-Powered Web App MCP client development with Streamlit to enhance the tool calling capabilities of remote MCP servers, from setting up your development environment and securing API keys, handling user input, connecting to remote MCP servers, and displaying AI-generated responses. The post MCP Client Development with Streamlit: Build…
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Exploratory Data Analysis: Gamma Spectroscopy in Python (Part 2)
Exploratory Data Analysis: Gamma Spectroscopy in Python (Part 2) Let’s observe the matter on the atomic level The post Exploratory Data Analysis: Gamma Spectroscopy in Python (Part 2) appeared first on Towards Data Science. Dmitrii Eliuseev Go to original source
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How to Ensure Reliability in LLM Applications
How to Ensure Reliability in LLM Applications Learn how to make your LLM applications more robust The post How to Ensure Reliability in LLM Applications appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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Deploy a Streamlit App to AWS
Deploy a Streamlit App to AWS Using the Elastic Beanstalk service The post Deploy a Streamlit App to AWS appeared first on Towards Data Science. Thomas Reid Go to original source
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Topic Model Labelling with LLMs
Topic Model Labelling with LLMs Python tutorial for reproducible labeling of cutting-edge topic models with GPT4-o-mini. The post Topic Model Labelling with LLMs appeared first on Towards Data Science. Petr Koráb Go to original source
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Hitchhiker’s Guide to RAG: From Tiny Files to Tolstoy with OpenAI’s API and LangChain
Hitchhiker’s Guide to RAG: From Tiny Files to Tolstoy with OpenAI’s API and LangChain Scaling a simple RAG pipeline from simple notes to full books The post Hitchhiker’s Guide to RAG: From Tiny Files to Tolstoy with OpenAI’s API and LangChain appeared first on Towards Data Science. Maria Mouschoutzi Go to original source
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How to Perform Effective Data Cleaning for Machine Learning
How to Perform Effective Data Cleaning for Machine Learning Learn how you can improve your machine learning models using effective data cleaning The post How to Perform Effective Data Cleaning for Machine Learning appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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Run Your Python Code up to 80x Faster Using the Cython Library
Run Your Python Code up to 80x Faster Using the Cython Library A four-step plan for C language speed where it matters most The post Run Your Python Code up to 80x Faster Using the Cython Library appeared first on Towards Data Science. Thomas Reid Go to original source
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POSET Representations in Python Can Have a Huge Impact on Business
POSET Representations in Python Can Have a Huge Impact on Business Discover how POSET indicators transform data into coherent scoring systems, enabling meaningful comparisons while preserving the data’s multi-dimensional semantic structure. The post POSET Representations in Python Can Have a Huge Impact on Business appeared first on Towards Data Science. Andrea D’Agostino Go to original…
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Taking ResNet to the Next Level
Taking ResNet to the Next Level Understanding how ResNeXt improves upon ResNet, with a comprehensive PyTorch implementation guide The post Taking ResNet to the Next Level appeared first on Towards Data Science. Muhammad Ardi Go to original source
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Interactive Data Exploration for Computer Vision Projects with Rerun
Interactive Data Exploration for Computer Vision Projects with Rerun Analyse dynamic signals in a computer vision pipeline in Python using OpenCV and Rerun The post Interactive Data Exploration for Computer Vision Projects with Rerun appeared first on Towards Data Science. Florian Trautweiler Go to original source
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How to Access NASA’s Climate Data — And How It’s Powering the Fight Against Climate Change Pt. 1
How to Access NASA’s Climate Data — And How It’s Powering the Fight Against Climate Change Pt. 1 From architectural design to food security. The post How to Access NASA’s Climate Data — And How It’s Powering the Fight Against Climate Change Pt. 1 appeared first on Towards Data Science. Marco Hening Tallarico Go to…
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STOP Building Useless ML Projects – What Actually Works
STOP Building Useless ML Projects – What Actually Works How to find machine learning projects that will get you hired. The post STOP Building Useless ML Projects – What Actually Works appeared first on Towards Data Science. Egor Howell Go to original source
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From Pixels to Plots
From Pixels to Plots How I built an AI-powered prototype to turn images into insights The post From Pixels to Plots appeared first on Towards Data Science. Jens Winkelmann Go to original source
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Become a Better Data Scientist with These Prompt Engineering Tips and Tricks
Become a Better Data Scientist with These Prompt Engineering Tips and Tricks Part 1: prompt engineering for planning, cleaning, and EDA The post Become a Better Data Scientist with These Prompt Engineering Tips and Tricks appeared first on Towards Data Science. Sara Nobrega Go to original source
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Hitchhiker’s Guide to RAG with ChatGPT API and LangChain
Hitchhiker’s Guide to RAG with ChatGPT API and LangChain Build a simple Python RAG pipeline using your local files as context The post Hitchhiker’s Guide to RAG with ChatGPT API and LangChain appeared first on Towards Data Science. Maria Mouschoutzi Go to original source
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Data Science: From School to Work, Part V
Data Science: From School to Work, Part V How to profile your Python project The post Data Science: From School to Work, Part V appeared first on Towards Data Science. Vincent Margot Go to original source
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How to Train a Chatbot Using RAG and Custom Data
How to Train a Chatbot Using RAG and Custom Data Retrieval-Augmented Generation made easy with Llama The post How to Train a Chatbot Using RAG and Custom Data appeared first on Towards Data Science. Haden Pelletier Go to original source
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Building A Modern Dashboard with Python and Taipy
Building A Modern Dashboard with Python and Taipy A guide to building a front-end data application. The post Building A Modern Dashboard with Python and Taipy appeared first on Towards Data Science. Thomas Reid Go to original source
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From Configuration to Orchestration: Building an ETL Workflow with AWS Is No Longer a Struggle
From Configuration to Orchestration: Building an ETL Workflow with AWS Is No Longer a Struggle A step-by-step guide to leverage AWS services for efficient data pipeline automation The post From Configuration to Orchestration: Building an ETL Workflow with AWS Is No Longer a Struggle appeared first on Towards Data Science. Jiayan Yin Go to original…
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What PyTorch Really Means by a Leaf Tensor and Its Grad
What PyTorch Really Means by a Leaf Tensor and Its Grad The secret life of leaves, gradients, and the mighty requires_grad flag The post What PyTorch Really Means by a Leaf Tensor and Its Grad appeared first on Towards Data Science. Maciej J. Mikulski Go to original source
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Animating Linear Transformations with Quiver
Animating Linear Transformations with Quiver A useful tool in your quiver The post Animating Linear Transformations with Quiver appeared first on Towards Data Science. Artemij Lehmann Go to original source
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Abstract Classes: A Software Engineering Concept Data Scientists Must Know To Succeed
Abstract Classes: A Software Engineering Concept Data Scientists Must Know To Succeed Simple concepts that differentiate a professional from amateurs. The post Abstract Classes: A Software Engineering Concept Data Scientists Must Know To Succeed appeared first on Towards Data Science. Benjamin Lee Go to original source
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Apply Sphinx’s Functionality to Create Documentation for Your Next Data Science Project
Apply Sphinx’s Functionality to Create Documentation for Your Next Data Science Project Three cases to use the Sphinx tool as a pro The post Apply Sphinx’s Functionality to Create Documentation for Your Next Data Science Project appeared first on Towards Data Science. Radmila Mandzhieva Go to original source
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Grad-CAM from Scratch with PyTorch Hooks
Grad-CAM from Scratch with PyTorch Hooks A hands-on look at an explainable AI (XAI) technique that helps reveal why a convolutional neural network (CNN) made a particular decision The post Grad-CAM from Scratch with PyTorch Hooks appeared first on Towards Data Science. Conor O’Sullivan Go to original source
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Build an AI Agent to Explore Your Data Catalog with Natural Language
Build an AI Agent to Explore Your Data Catalog with Natural Language Leverage LLMs to query your Databricks Data Catalog The post Build an AI Agent to Explore Your Data Catalog with Natural Language appeared first on Towards Data Science. Fabiana Clemente Go to original source
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User Authorisation in Streamlit With OIDC and Google
User Authorisation in Streamlit With OIDC and Google Log in to a Streamlit app with a Google email account The post User Authorisation in Streamlit With OIDC and Google appeared first on Towards Data Science. Thomas Reid Go to original source
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Model Context Protocol (MCP) Tutorial: Build Your First MCP Server in 6 Steps
Model Context Protocol (MCP) Tutorial: Build Your First MCP Server in 6 Steps A beginner-friendly tutorial of MCP architecture, with the focus on MCP server components and applications, guiding through the process of building a custom MCP server that enables code-to-diagram. The post Model Context Protocol (MCP) Tutorial: Build Your First MCP Server in 6…
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Mobile App Development with Python
Mobile App Development with Python Build iOS & Android Apps with Kivy The post Mobile App Development with Python appeared first on Towards Data Science. Mauro Di Pietro Go to original source
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10,000x Faster Bayesian Inference: Multi-GPU SVI vs. Traditional MCMC
10,000x Faster Bayesian Inference: Multi-GPU SVI vs. Traditional MCMC Using GPU acceleration to speed up Bayesian Inference from months to minutes… The post 10,000x Faster Bayesian Inference: Multi-GPU SVI vs. Traditional MCMC appeared first on Towards Data Science. Derek Tran Go to original source
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Exploratory Data Analysis: Gamma Spectroscopy in Python
Exploratory Data Analysis: Gamma Spectroscopy in Python Let’s observe the matter on the atomic level The post Exploratory Data Analysis: Gamma Spectroscopy in Python appeared first on Towards Data Science. Dmitrii Eliuseev Go to original source
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Prescriptive Modeling Unpacked: A Complete Guide to Intervention With Bayesian Modeling.
Prescriptive Modeling Unpacked: A Complete Guide to Intervention With Bayesian Modeling. Learn how to move beyond prediction and actively make intervention through prescriptive modeling. This in-depth guide walks you through Bayesian approaches to system intervention, with practical examples in predictive maintenance. The post Prescriptive Modeling Unpacked: A Complete Guide to Intervention With Bayesian Modeling. appeared…
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How I Automated My Machine Learning Workflow with Just 10 Lines of Python
How I Automated My Machine Learning Workflow with Just 10 Lines of Python Use LazyPredict and PyCaret to skip the grunt work and jump straight to performance. The post How I Automated My Machine Learning Workflow with Just 10 Lines of Python appeared first on Towards Data Science. Himanshu Sharma Go to original source
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The Journey from Jupyter to Programmer: A Quick-Start Guide
The Journey from Jupyter to Programmer: A Quick-Start Guide Explore the real benefits of ditching the notebook The post The Journey from Jupyter to Programmer: A Quick-Start Guide appeared first on Towards Data Science. Lucy Dickinson Go to original source
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Building a Modern Dashboard with Python and Gradio
Building a Modern Dashboard with Python and Gradio Data insights made simple The post Building a Modern Dashboard with Python and Gradio appeared first on Towards Data Science. Thomas Reid Go to original source
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LLMs + Pandas: How I Use Generative AI to Generate Pandas DataFrame Summaries
LLMs + Pandas: How I Use Generative AI to Generate Pandas DataFrame Summaries Local Large Language Models can convert massive DataFrames to presentable Markdown reports — here’s how. The post LLMs + Pandas: How I Use Generative AI to Generate Pandas DataFrame Summaries appeared first on Towards Data Science. Dario Radečić Go to original source
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Hands-On Attention Mechanism for Time Series Classification, with Python
Hands-On Attention Mechanism for Time Series Classification, with Python This is how to use the attention mechanism in a time series classification framework The post Hands-On Attention Mechanism for Time Series Classification, with Python appeared first on Towards Data Science. Piero Paialunga Go to original source
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Multi-Agent Communication with the A2A Python SDK
Multi-Agent Communication with the A2A Python SDK The Agent Card helps discover agents, but how does communication between agents actually work in practice? The post Multi-Agent Communication with the A2A Python SDK appeared first on Towards Data Science. Deborah Mesquita Go to original source
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JAX: Is This Google’s NumPy killer?
JAX: Is This Google’s NumPy killer? Auto differentiation and JIT compilation make a compelling case. The post JAX: Is This Google’s NumPy killer? appeared first on Towards Data Science. Thomas Reid Go to original source
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Reinforcement Learning Made Simple: Build a Q-Learning Agent in Python
Reinforcement Learning Made Simple: Build a Q-Learning Agent in Python Inspired by AlphaGo’s Move 37 — learn how agents explore, exploit, and win The post Reinforcement Learning Made Simple: Build a Q-Learning Agent in Python appeared first on Towards Data Science. Sarah Schürch Go to original source
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How to Generate Synthetic Data: A Comprehensive Guide Using Bayesian Sampling and Univariate Distributions
How to Generate Synthetic Data: A Comprehensive Guide Using Bayesian Sampling and Univariate Distributions Data makes the engine run in many organisations. But what if the number of observations is too low or there is only expert knowledge? I will demonstrate how to generate synthetic data with applications in predictive maintenance. The post How to…
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Prototyping Gradient Descent in Machine Learning
Prototyping Gradient Descent in Machine Learning Mathematical theorem and credit transaction prediction using Stochastic / Batch GD The post Prototyping Gradient Descent in Machine Learning appeared first on Towards Data Science. Kuriko Iwai Go to original source
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Do More with NumPy Array Type Hints: Annotate & Validate Shape & Dtype
Do More with NumPy Array Type Hints: Annotate & Validate Shape & Dtype Improve static analysis and run-time validation with full generic specification The post Do More with NumPy Array Type Hints: Annotate & Validate Shape & Dtype appeared first on Towards Data Science. Christopher Ariza Go to original source
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Inheritance: A Software Engineering Concept Data Scientists Must Know To Succeed
Inheritance: A Software Engineering Concept Data Scientists Must Know To Succeed Coding concepts that distinguish an amateur from a professional data scientist The post Inheritance: A Software Engineering Concept Data Scientists Must Know To Succeed appeared first on Towards Data Science. Benjamin Lee Go to original source