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Category: tips-and-tricks

  • Data Science: From School to Work, Part III

    Data Science: From School to Work, Part III Introduction Writing code is about solving problems, but not every problem is predictable. In the real world, your software will encounter unexpected situations: missing files, invalid user inputs, network timeouts, or even hardware failures. This is why handling errors isn’t just a nice-to-have; it’s a critical part…

  • 7 Powerful DBeaver Tips and Tricks to Improve Your SQL Workflow

    7 Powerful DBeaver Tips and Tricks to Improve Your SQL Workflow DBeaver is the most powerful open-source SQL IDE, but there are several features people don’t know about. In this post, I will share with you several features to speed up your workflow, with zero fluff. I’ve learned these as I’m currently digging deeper into…

  • Comprehensive Guide to Dependency Management in Python

    Comprehensive Guide to Dependency Management in Python Introduction When learning Python, many beginners focus solely on the language and its libraries while completely ignoring virtual environments. As a result, managing Python projects can become a mess: dependencies installed for different projects may have conflicting versions, leading to compatibility issues. Even when I studied Python, nobody…

  • Data Science: From School to Work, Part II

    Data Science: From School to Work, Part II In my previous article, I highlighted the importance of effective project management in Python development. Now, let’s shift our focus to the code itself and explore how to write clean, maintainable code — an essential practice in professional and collaborative environments.  Readability & Maintainability: Well-structured code is easier to…

  • Nine Rules for SIMD Acceleration of Your Rust Code (Part 1)

    Nine Rules for SIMD Acceleration of Your Rust Code (Part 1) Thanks to Ben Lichtman (B3NNY) at the Seattle Rust Meetup for pointing me in the right direction on SIMD. SIMD (Single Instruction, Multiple Data) operations have been a feature of Intel/AMD and ARM CPUs since the early 2000s. These operations enable you to, for example,…

  • Six Ways to Control Style and Content in Diffusion Models

    Six Ways to Control Style and Content in Diffusion Models Stable Diffusion 1.5/2.0/2.1/XL 1.0, DALL-E, Imagen… In the past years, Diffusion Models have showcased stunning quality in image generation. However, while producing great quality on generic concepts, these struggle to generate high quality for more specialised queries, for example generating images in a specific style,…

  • On a Time Crunch but Still Want to Learn to Develop Multi-Agent AI?

    On a Time Crunch but Still Want to Learn to Develop Multi-Agent AI? These 3 starter projects only take a weekend (and a few cups of coffee, maybe) Continue reading on Towards Data Science » Thuwarakesh Murallie Go to original source

  • Harmonizing and Pooling Datasets for Health Research in R

    Harmonizing and Pooling Datasets for Health Research in R R code to extract data from unique datasets and combine them in one harmonized dataset ready for seamless analysis Continue reading on Towards Data Science » Rodrigo M Carrillo Larco, MD, PhD Go to original source

  • A Practical Exploration of Sora — Intuitively and Exhaustively Explained

    A Practical Exploration of Sora — Intuitively and Exhaustively Explained A new cutting edge video generation tool, and the theory behind it Continue reading on Towards Data Science » Daniel Warfield Go to original source

  • How to Tell Among Two Regression Models with Statistical Significance

    How to Tell Among Two Regression Models with Statistical Significance Diving into the F-test for nested models with algorithms, examples and code Continue reading on Towards Data Science » LucianoSphere (Luciano Abriata, PhD) Go to original source

  • How To Start A Data Science Blog on Medium

    How To Start A Data Science Blog on Medium Tips on how to get started, write your first article, and get noticed Continue reading on Towards Data Science » Haden Pelletier Go to original source

  • How (and Where) ML Beginners Can Find Papers

    How (and Where) ML Beginners Can Find Papers From conferences to surveys Continue reading on Towards Data Science » Pascal Janetzky Go to original source

  • Top 3 Strategies to Search Your Data

    Top 3 Strategies to Search Your Data Strategies from traditional index seek to AI based semantic search that every software engineer should know! Continue reading on Towards Data Science » Shawn Shi Go to original source

  • A Design Researcher’s Guide to Publishing

    A Design Researcher’s Guide to Publishing A Guide to Publishing Human-Computer Interaction (HCI) and Design Research Papers Turn ‘publish or perish’ into ‘learn, write, and share’ When I first started my PhD three years ago, I was very new to the world of academia and the process of publishing in journals and conferences. Coming from Computer Engineering,…

  • How I’d Learn AI in 2025 (If I Knew Nothing)

    How I’d Learn AI in 2025 (If I Knew Nothing) A 5-step roadmap for today’s landscape Today, more people than ever are trying to learn AI. Although there are countless free learning resources online, navigating this rapidly evolving landscape can be overwhelming (especially as a beginner). In this article, I discuss how I’d approach learning…

  • 5 Essential Tips to Build Business Dashboards Stakeholders Love

    5 Essential Tips to Build Business Dashboards Stakeholders Love A practical guide to designing clear, effective, and actionable dashboards for decision-making Continue reading on Towards Data Science » Yu Dong Go to original source

  • Awesome Plotly with Code Series (Part 5): The Order in Bar Charts Matters

    Awesome Plotly with Code Series (Part 5): The Order in Bar Charts Matters And it is not always simply ordering by highest to lowest Continue reading on Towards Data Science » Jose Parreño Go to original source

  • Machine Learning Experiments Done Right

    Machine Learning Experiments Done Right A detailed guideline for designing machine learning experiments that produce reliable, reproducible results. Photo by Vedrana Filipović on Unsplash Machine learning (ML) practitioners run experiments to compare the effectiveness of methods for both specific applications and for general types of problems. The validity of experimental results hinges on how practitioners design,…

  • When Not to Use the Streamlit AgGrid Component

    When Not to Use the Streamlit AgGrid Component Streamlit-AgGrid is amazing. But there are 2 scenarios where its use is not recommended. Continue reading on Towards Data Science » Jose Parreño Go to original source

  • Model Validation Techniques, Explained: A Visual Guide with Code Examples

    Model Validation Techniques, Explained: A Visual Guide with Code Examples MODEL EVALUATION & OPTIMIZATION 12 must-know methods to validate your machine learning Every day, machines make millions of predictions — from detecting objects in photos to helping doctors find diseases. But before trusting these predictions, we need to know if they’re any good. After all, no one would…

  • Dunder Methods: The Hidden Gems of Python

    Dunder Methods: The Hidden Gems of Python Real-world examples on how actively using special methods can simplify coding and improve readability. Dunder methods, though possibly a basic topic in Python, are something I have often noticed being understood only superficially, even by people who have been coding for quite some time. Disclaimer: This is a forgivable…

  • Level Up Your Coding Skills with Python Threading

    Level Up Your Coding Skills with Python Threading Learn how to use queues, daemon threads, and events in a Machine Learning project Continue reading on Towards Data Science » Marcello Politi Go to original source