Course outline / demo syllabus
What this learning path covers
This page explains what the topic means for real AI jobs, what a learner should practice and how the result can be evidenced in a CV.
Example course structure
This outline shows what a useful course can cover. The actual course may be expanded depending on level and provider.
- Modul 1: Tensoren und Autograd
Grundoperationen, Shapes, Gradienten und typische Fehlerquellen. - Modul 2: Dataset und DataLoader
Daten vorbereiten, Batches bauen, Trainings- und Validierungsdaten trennen. - Modul 3: Trainingsloop
Forward Pass, Loss, Backpropagation, Optimizer, Epochen und Logging. - Modul 4: Modellbewertung
Validierung, Overfitting, Metriken, Confusion Matrix und Fehlerbeispiele. - Modul 5: Projekt dokumentieren
Architektur, Trainingsdaten, Ergebnis und Grenzen so aufbereiten, dass sie im CV belegbar sind.
Practical transfer
Mini project for portfolio and CV
Build a small portfolio project and document problem, method, tools, result and limitations.
The important result is not only a certificate but a credible project reference for applications.
Relevant target roles
- Deep Learning Intern
- Computer Vision Engineer Junior
- Research Engineer Assistant
- ML Engineer