Guides

YOLO for Object Detection: A Practical Guide

What you need to know before starting an object detection project, choosing the approach, preparing data, and running it in the field.

Machine Learning: A Practical Guide Before You Start

When machine learning is genuinely needed, when it is not, and what decides whether a project ships or stalls halfway.

Deep Learning: When It Is Actually Needed

Deep learning solves problems nothing else can touch, but demands data and hardware that are not trivial. Here is how to weigh it.

OpenCV: Image Processing for Real Needs

Many computer vision problems can be solved without training any model at all, faster, cheaper, and easier to inspect.

Data Science: From Raw Data to Decisions

Useful analysis is not the most sophisticated, it is the kind that changes a decision. Here is how to do it properly.

Django: Building Web Applications with Python

A framework that brings nearly everything a web application needs from day one. Here is how to weigh it and use it.

ANSYS: Simulation Results You Can Defend

Simulation is easy to run and hard to trust. This guide is about making sure the numbers actually mean something.

Rhino 3D: Precision Modelling for Design and Production

Rhino excels where shapes are complex but dimensions must be exact, jewellery, products, façades, hulls.

Unity: From Idea to Something Playable

The hard part of making a game is not making it, it is finishing it. This guide is about avoiding that trap.

Unreal Engine: Realistic Visuals and What They Cost

Unreal delivers image quality that is hard to match, with hardware and effort demands worth planning for up front.

Blender: A Practical Guide from Modelling to Render

One free application covering almost the entire 3D pipeline. Here is how to use it effectively.

MATLAB: Technical Computing and System Simulation

MATLAB is strong in specific places: engineering computation, signal processing, and control system modelling.