https://cw.fel.cvut.cz/b251/ 2026-04-26T06:29:58+0200 https://cw.fel.cvut.cz/b251/ https://cw.fel.cvut.cz/b251/lib/tpl/bulma-cw/images/favicon.ico text/html 2025-10-14T13:55:47+0200 Anonymous (anonymous@undisclosed.example.com) https://cw.fel.cvut.cz/b251/courses/becm33mle/tutorials/lab_datasets?rev=1760442947&do=diff Lab 03: Datasets In this lab we’ll build a small, clean, and reproducible dataset from end to end. You’ll practice sourcing images from public repositories, labeling efficiently with Label Studio (assisted by SAM2), running a pre-processing pipeline text/html 2025-11-19T13:53:19+0200 Anonymous (anonymous@undisclosed.example.com) https://cw.fel.cvut.cz/b251/courses/becm33mle/tutorials/lab_deployment?rev=1763556799&do=diff Lab 07: Containerization and deployment In this lab you will learn about the concepts of containerization and dockerize a simple Python Flet app. Use arrow keys (including up/down) to move in the presentation slides. text/html 2025-12-16T10:19:55+0200 Anonymous (anonymous@undisclosed.example.com) https://cw.fel.cvut.cz/b251/courses/becm33mle/tutorials/lab_final?rev=1765876795&do=diff Lab 13: Final Presentation During the final lab on 06.01.2026, we will hold the Final Project Presentations (Investor Pitch Day). Please make sure you have submitted the final progress report and finalized your project source code and README on GitLab no later than text/html 2025-10-14T08:31:08+0200 Anonymous (anonymous@undisclosed.example.com) https://cw.fel.cvut.cz/b251/courses/becm33mle/tutorials/lab_flet?rev=1760423468&do=diff Lab 02: Flet GUI basics Flet is an opensource Python framework for building real, reactive user interfaces—web, desktop, and mobile—using simple Python code. It wraps Google’s Flutter engine under the hood, so you get native-feeling UI, smooth animations, and a rich set of ready-made controls without touching Dart. You write components, manage state, and handle events in Python, then run the same app in a browser, as a desktop app, or packaged for phones. It’s great for quickly turning scripts … text/html 2025-11-13T10:19:44+0200 Anonymous (anonymous@undisclosed.example.com) https://cw.fel.cvut.cz/b251/courses/becm33mle/tutorials/lab_implementation?rev=1763025584&do=diff Lab 06: Implementation details This lab is purely theoretical. We discuss some of the challenges of developing and deploying production ready ML based app. Use arrow keys (including up/down) to move in the presentation slides. text/html 2025-12-01T07:27:05+0200 Anonymous (anonymous@undisclosed.example.com) https://cw.fel.cvut.cz/b251/courses/becm33mle/tutorials/lab_market?rev=1764570425&do=diff Lab 08: Going to market This final lab explores how to place your ML project on the wast competitive market. We learn how to define concrete success metrics, experiments to test whether anyone cares, and perform a short go-to-market experiment. The focus is on validating problem-solution fit of your apps and audience interest, not just the measurable performance. text/html 2025-11-04T10:58:59+0200 Anonymous (anonymous@undisclosed.example.com) https://cw.fel.cvut.cz/b251/courses/becm33mle/tutorials/lab_pytorch?rev=1762250339&do=diff Lab 05: PyTorch In this lab you’ll practice a deep learning workflow in PyTorch. We will go through the following topics: Part 01 - PyTorch Basics:Part 02 - Pre-bundled Models:Part 03 - Training Loops:Part 04 - ONNX Export:torch.onnx.exportonnxruntime text/html 2025-10-21T11:02:53+0200 Anonymous (anonymous@undisclosed.example.com) https://cw.fel.cvut.cz/b251/courses/becm33mle/tutorials/lab_scikit?rev=1761037373&do=diff Lab 04: Pandas & scikit-learn Foundations In this lab you will practice a compact, reproducible workflow for working with tabular data and building baseline ML models: Part 01 - Pandas:Part 02 - scikit-learn:RandomForestclassification reportsconfusion matrices text/html 2025-09-01T10:24:15+0200 Anonymous (anonymous@undisclosed.example.com) https://cw.fel.cvut.cz/b251/courses/becm33mle/tutorials/start?rev=1756715055&do=diff Tutorials Login to the lab's computer Please, follow these links to establish the login to the lab's computers: https://cyber.felk.cvut.cz/study/computer-labs/https://cw.felk.cvut.cz/password/ Remote connection to a lab's computer 1. Connect to the turtle server using the login student and password xxx: tomas@laptop:~$ ssh student@turtle.felk.cvut.cz