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Category: hands-on-tutorials

  • AI Agents from Zero to Hero — Part 3

    AI Agents from Zero to Hero — Part 3 Intro In Part 1 of this tutorial series, we introduced AI Agents, autonomous programs that perform tasks, make decisions, and communicate with others.  In Part 2 of this tutorial series, we understood how to make the Agent try and retry until the task is completed through…

  • What Germany Currently Is Up To, Debt-Wise

    What Germany Currently Is Up To, Debt-Wise €1,600 per second. That’s how much interest Germany has to pay for its debts. In total, the German state has debts ranging into the trillions — more than a thousand billion Euros. And the government is planning to make even more, up to one trillion additional debt is…

  • Anatomy of a Parquet File

    Anatomy of a Parquet File In recent years, Parquet has become a standard format for data storage in Big Data ecosystems. Its column-oriented format offers several advantages: Faster query execution when only a subset of columns is being processed Quick calculation of statistics across all data Reduced storage volume thanks to efficient compression When combined…

  • Custom Training Pipeline for Object Detection Models

    Custom Training Pipeline for Object Detection Models What if you want to write the whole object detection training pipeline from scratch, so you can understand each step and be able to customize it? That’s what I set out to do. I examined several well-known object detection pipelines and designed one that best suits my needs…

  • Practical SQL Puzzles That Will Level Up Your Skill

    Practical SQL Puzzles That Will Level Up Your Skill There are some Sql patterns that, once you know them, you start seeing them everywhere. The solutions to the puzzles that I will show you today are actually very simple SQL queries, but understanding the concept behind them will surely unlock new solutions to the queries…

  • Debugging the Dreaded NaN

    Debugging the Dreaded NaN You are training your latest AI model, anxiously watching as the loss steadily decreases when suddenly — boom! Your logs are flooded with NaNs (Not a Number) — your model is irreparably corrupted and you’re left staring at your screen in despair. To make matters worse, the NaNs don’t appear consistently.…

  • The Next AI Revolution: A Tutorial Using VAEs to Generate High-Quality Synthetic Data

    The Next AI Revolution: A Tutorial Using VAEs to Generate High-Quality Synthetic Data What is synthetic data? Data created by a computer intended to replicate or augment existing data. Why is it useful? We have all experienced the success of ChatGPT, Llama, and more recently, DeepSeek. These language models are being used ubiquitously across society…

  • Multimodal Search Engine Agents Powered by BLIP-2 and Gemini

    Multimodal Search Engine Agents Powered by BLIP-2 and Gemini This post was co-authored with Rafael Guedes. Introduction Traditional models can only process a single type of data, such as text, images, or tabular data. Multimodality is a trending concept in the AI research community, referring to a model’s ability to learn from multiple types of…

  • On-Device Machine Learning in Spatial Computing

    On-Device Machine Learning in Spatial Computing The landscape of computing is undergoing a profound transformation with the emergence of spatial computing platforms(VR and AR). As we step into this new era, the intersection of virtual reality, Augmented Reality, and on-device machine learning presents unprecedented opportunities for developers to create experiences that seamlessly blend digital content…

  • Learnings from a Machine Learning Engineer — Part 3: The Evaluation

    Learnings from a Machine Learning Engineer — Part 3: The Evaluation In this third part of my series, I will explore the evaluation process which is a critical piece that will lead to a cleaner data set and elevate your model performance. We will see the difference between evaluation of a trained model (one not yet in…

  • Pandas Can’t Handle This: How ArcticDB Powers Massive Datasets

    Pandas Can’t Handle This: How ArcticDB Powers Massive Datasets Python has grown to dominate data science, and its package Pandas has become the go-to tool for data analysis. It is great for tabular data and supports data files of up to 1GB if you have a large RAM. Within these size limits, it is also…

  • Build a Decision Tree in Polars from Scratch

    Build a Decision Tree in Polars from Scratch Decision Tree algorithms have always fascinated me. They are easy to implement and achieve good results on various classification and regression tasks. Combined with boosting, decision trees are still state-of-the-art in many applications. Frameworks such as sklearn, Lightgbm, xgboost and catboost have done a very good job…

  • 4-Dimensional Data Visualization: Time in Bubble Charts

    4-Dimensional Data Visualization: Time in Bubble Charts Bubble Charts elegantly compress large amounts of information into a single visualization, with bubble size adding a third dimension. However, comparing “before” and “after” states is often crucial. To address this, we propose adding a transition between these states, creating an intuitive user experience. Since we couldn’t find…

  • Introduction to Minimum Cost Flow Optimization in Python

    Introduction to Minimum Cost Flow Optimization in Python Minimum cost flow optimization minimizes the cost of moving flow through a network of nodes and edges. Nodes include sources (supply) and sinks (demand), with different costs and capacity limits. The aim is to find the least costly way to move volume from sources to sinks while…

  • From Resume to Cover Letter Using AI and LLM, with Python and Streamlit

    From Resume to Cover Letter Using AI and LLM, with Python and Streamlit DISCLAIMER: The idea of doing Cover Letter or even Resume with AI does not obviously start with me. A lot of people have done this before (very successfully) and have built websites and even companies from the idea. This is just a…

  • Show and Tell

    Show and Tell Photo by Ståle Grut on Unsplash Introduction Natural Language Processing and Computer Vision used to be two completely different fields. Well, at least back when I started to learn machine learning and deep learning, I feel like there are multiple paths to follow, and each of them, including NLP and Computer Vision,…

  • How Likely Is a Six Nations Grand Slam in 2025?

    How Likely Is a Six Nations Grand Slam in 2025? Quantifying uncertainty in sports fixtures Photo by Thomas Serer on Unsplash Introduction For rugby fans the long wait is nearly over, like Christmas the Six Nations comes once a year to lift our spirits in the cold winter months. If you’re not very familiar with rugby, the…

  • Building a Regression Model: Delivery Duration Prediction

    Building a Regression Model: Delivery Duration Prediction Building a Regression Model to Predict Delivery Durations: A Practical Guide E2E walkthrough for approaching a regression modeling task In this article, we’re going to walk through the process of building a regression model — from dataset cleaning & preparation, to model training & evaluation. The specific regression task we will…

  • Build a Decision Tree in Polars from Scratch

    Build a Decision Tree in Polars from Scratch Explore decision trees with polars backend Photo by Leonard Laub on Unsplash Decision tree algorithms have always fascinated me. They are easy to implement and achieve good results on various classification and regression tasks. Combined with boosting, decision trees are still state-of-the-art in many applications. Frameworks such as sklearn,…

  • How Cheap Mortgages Transformed Poland’s Real Estate Market

    How Cheap Mortgages Transformed Poland’s Real Estate Market Insights from a synthetic control group Continue reading on Towards Data Science » Lukasz Szubelak Go to original source

  • Deep Learning for Click Prediction in Mobile AdTech

    Deep Learning for Click Prediction in Mobile AdTech Source: https://pixabay.com/illustrations/rays-stars-light-explosion-galaxy-9350519/ Machine Learning for Real-Time Bidding The past few years were a revolution for the mobile advertising and gaming industries, with the broad adoption of neural networks for advertising tasks, including click prediction. This migration occurred prior to the success of Large Language Models (LLMs) and…

  • Satellite Image Classification with Deep Learning — Complete Project

    Satellite Image Classification with Deep Learning — Complete Project A Comprehensive Guide Using PyTorch and CNNs Continue reading on Towards Data Science » Leo Anello Go to original source

  • Learnings from a Machine Learning Engineer — Part 3: The Evaluation

    Learnings from a Machine Learning Engineer — Part 3: The Evaluation Practical insights for a data-driven approach to model optimization Continue reading on Towards Data Science » David Martin Go to original source

  • Hands-On Delivery Routes Optimization (TSP) with AI, Using LKH and Python

    Hands-On Delivery Routes Optimization (TSP) with AI, Using LKH and Python Here’s how to optimize the delivery routes, from theory to code. Continue reading on Towards Data Science » Piero Paialunga Go to original source

  • Exploring New Hyperparameter Dimensions with Laplace Approximated Bayesian Optimization

    Exploring New Hyperparameter Dimensions with Laplace Approximated Bayesian Optimization Is it better than grid search? Image by author from canva When I notice my model is overfitting, I often think, “It is time to regularize”. But how do I decide which regularization method to use (L1, L2) and what parameters to choose? Typically, I perform hyperparameter optimization…

  • Building Visual Agents that can Navigate the Web Autonomously

    Building Visual Agents that can Navigate the Web Autonomously A step-by-step guide to creating visual agents that can navigate the web autonomously Continue reading on Towards Data Science » Luís Roque Go to original source

  • Sustainable Business Strategy with Data Analytics

    Sustainable Business Strategy with Data Analytics Use data analytics to help companies design and implement strategic sustainability roadmaps to reduce their environmental footprint. Sustainable Business Strategy with Analytics — (Image by Samir Saci) Consensus means that everyone agrees to say collectively what no one believes individually. This quote captures a critical issue many companies face during their strategic…

  • Sentiment Analysis with Transformers: A Complete Deep Learning Project — PT. I

    Sentiment Analysis with Transformers: A Complete Deep Learning Project — PT. I Master Fine-Tuning Transformers, Comparing Deep Learning Architectures, and Deploying Sentiment Analysis Models Continue reading on Towards Data Science » Leo Anello Go to original source

  • What to Do If the Logit Decision Boundary Fails?

    What to Do If the Logit Decision Boundary Fails? Feature engineering for classification models using Bayesian Machine Learning Continue reading on Towards Data Science » Lukasz Gatarek Go to original source

  • How to Run Jupyter Notebooks and Generate HTML Reports with Python Scripts

    How to Run Jupyter Notebooks and Generate HTML Reports with Python Scripts A step-by-step guide to automating Jupyter Notebook execution and report generation using Python Continue reading on Towards Data Science » Amanda Iglesias Moreno Go to original source

  • How to Build an AI Agent for Data Analytics Without Writing SQL

    How to Build an AI Agent for Data Analytics Without Writing SQL Create a comprehensive AI agent from the ground up utilizing LangChain and DuckDB Continue reading on Towards Data Science » Chengzhi Zhao Go to original source

  • Awesome Plotly with Code Series (Part 7): Cropping the y-axis in Bar Charts

    Awesome Plotly with Code Series (Part 7): Cropping the y-axis in Bar Charts Is there ever a good reason for starting a bar chart above zero? Continue reading on Towards Data Science » Jose Parreño Go to original source

  • The Next Frontier in LLM Accuracy

    The Next Frontier in LLM Accuracy Exploring the Power of Lamini Memory Tuning Image generated by DALL-E 3 Accuracy is often critical for LLM applications, especially in cases such as API calling or summarisation of financial reports. Fortunately, there are ways to enhance precision. The best practices to improve accuracy include the following steps: You can start…

  • Sensor Fusion — KITTI — ‘Lidar-based Obstacle Detection’ — Part-1

    Sensor Fusion — KITTI — ‘Lidar-based Obstacle Detection’ — Part-1 Mastering Sensor Fusion: LiDAR Obstacle Detection with KITTI Data — Part 1 How to use Lidar data for obstacle detection with unsupervised learning Sensor fusion, multi-modal perception, autonomous vehicles — if these keywords pique your interest, this Medium blog is for you. Join me as I explore the fascinating world of LiDAR and color image-based environment…

  • AI-Powered Information Extraction and Matchmaking

    AI-Powered Information Extraction and Matchmaking Developing an application for extracting key profile information from CVs and recommending jobs aligned with the profile Continue reading on Towards Data Science » Umair Ali Khan Go to original source

  • Transforming Data into Solutions: Building a Smart App with Python and AI

    Transforming Data into Solutions: Building a Smart App with Python and AI Some financial analysts worry that artificial intelligence may not justify the massive investments being made in the field. While I understand their concerns, I see things differently. I’m neither an AI Boomer nor an AI Doomer — I believe AI has the potential to drive…

  • Building a Custom AI Jira Agent

    Building a Custom AI Jira Agent How I used Google Mesop, Django, LangChain Agents, CO-STAR & Chain-of-Thought (CoT) prompting combined with the Jira API to better automate Jira Photo by Google DeepMind on Unsplash The inspiration for this project came from hosting a Jira ticket creation tool on a web application I had developed for internal users.…

  • Introducing n-Step Temporal-Difference Methods

    Introducing n-Step Temporal-Difference Methods Dissecting “Reinforcement Learning” by Richard S. Sutton with custom Python implementations, Episode V Continue reading on Towards Data Science » Oliver S Go to original source

  • Master Bots Before Starting with AI Agents: Simple Steps to Create a Mastodon Bot with Python

    Master Bots Before Starting with AI Agents: Simple Steps to Create a Mastodon Bot with Python I recently published a post on Mastodon that was shared by six other accounts within two minutes. Curious, I visited the profiles and… Continue reading on Towards Data Science » Sarah Lea Go to original source

  • Creating a WhatsApp AI Agent with GPT-4o

    Creating a WhatsApp AI Agent with GPT-4o Created with DALL-E How to use the Meta API to build your own LLM-powered Whatsapp chatbot A game-changer in the field of AI and business management is the integration of AI agents with widely used communication tools. Think of having a familiar chat interface with real-time data requests, updates, and…

  • Conditional Variational Autoencoders for Text to Image Generation

    Conditional Variational Autoencoders for Text to Image Generation Investigating an early generative architecture and applying it to image generation from text input Recently I was tasked with text-to-image synthesis using a conditional variational autoencoder (CVAE). Being one of the earlier generative structures, it has its limitations but is easily implementable. This article will cover CVAEs at…

  • Synthetic Control Sample for Before and After A/B Test

    Synthetic Control Sample for Before and After A/B Test Learn a simple way to use linear regression to create a synthetic control sample for your A/B test Continue reading on Towards Data Science » Gustavo R Santos Go to original source

  • Transform Customer Feedback into Actionable Insights with CrewAI and Streamlit

    Transform Customer Feedback into Actionable Insights with CrewAI and Streamlit Build an AI-powered app to analyze unstructured feedback, generate insightful reports, and create interactive visualizations Continue reading on Towards Data Science » Alan Jones Go to original source

  • Will Your Christmas Be White? Ask An AI Weather Model!

    Will Your Christmas Be White? Ask An AI Weather Model! Learn how to visualize AI weather and create your own forecast for the holidays Continue reading on Towards Data Science » Caroline Arnold Go to original source

  • USGS DEM Files: How to Load, Merge, and Crop with Python

    USGS DEM Files: How to Load, Merge, and Crop with Python A quick guide to prepping digital elevation data Continue reading on Towards Data Science » Lee Vaughan Go to original source

  • Addressing the Butterfly Effect: Data Assimilation Using Ensemble Kalman Filter

    Addressing the Butterfly Effect: Data Assimilation Using Ensemble Kalman Filter Learn how to implement the Ensemble Kalman Filter for data assimilation, with mathematical details step-by-step code. Continue reading on Towards Data Science » Wencong Yang, PhD Go to original source

  • Sentiment analysis template: A complete data science project

    Sentiment analysis template: A complete data science project 10 essential steps, from data exploration to model deployment. Continue reading on Towards Data Science » Leo Anello Go to original source

  • How to Use Structured Generation for LLM-as-a-Judge Evaluations

    How to Use Structured Generation for LLM-as-a-Judge Evaluations Structured generation is fundamental to building complex, multi-step reasoning agents in LLM evaluations — especially for open source models Source: Generated with SDXL 1.0 Disclosure: I am a maintainer of Opik, one of the open source projects used later in this article. For the past few months, I’ve been working on LLM-based…

  • Uncertainty Quantification in Time Series Forecasting

    Uncertainty Quantification in Time Series Forecasting A deep dive into EnbPI, a Conformal Prediction approach for time series forecasting Continue reading on Towards Data Science » Jonte Dancker Go to original source

  • AI, My Holiday Elf: Building a Gift Recommender for the Perfect Christmas

    AI, My Holiday Elf: Building a Gift Recommender for the Perfect Christmas How I used AI and Streamlit to create a festive and fun gift recommendation app Continue reading on Towards Data Science » Shuqing Ke Go to original source

  • Multimodal RAG: Process Any File Type with AI

    Multimodal RAG: Process Any File Type with AI A beginner-friendly guide with example (Python) code This is the third article in a larger series on multimodal AI. In the previous posts, we discussed multimodal LLMs and embedding models, respectively. In this article, we will combine these ideas to enable the development of multimodal RAG systems. I’ll…

  • GPS Interpolation Using Maps and Kinematics

    GPS Interpolation Using Maps and Kinematics How do you apply dead reckoning to your geospatial dataset? The picture above illustrates the GPS interpolation process. The red dots represent the known and repeated GPS locations, with more than one location per dot, while the blue dots represent the inferred locations of the repeated points along the…

  • Step-by-Step Guide for Building Bump Charts in Plotly

    Step-by-Step Guide for Building Bump Charts in Plotly Learn how to create custom bump charts in Python using Plotly for data visualization Continue reading on Towards Data Science » Amanda Iglesias Moreno Go to original source

  • 3D Clustering with Graph Theory: The Complete Guide

    3D Clustering with Graph Theory: The Complete Guide Python Tutorial for Euclidean Clustering of 3D Point Clouds with Graph Theory. Fundamental concepts and sequential workflow for… Continue reading on Towards Data Science » Florent Poux, Ph.D. Go to original source

  • How to Prune LLaMA 3.2 and Similar Large Language Models

    How to Prune LLaMA 3.2 and Similar Large Language Models This article explores a structured pruning technique for state-of-the-art models, that uses a GLU architecture, enabling the creation of… Continue reading on Towards Data Science » Pere Martra Go to original source