Deep Learning für die industrielle Fehlererkennung

Automated quality control is transforming modern manufacturing, yet deploying reliable AI models on the factory floor requires a solid understanding of both deep learning and practical optimization. This course guides you through the foundational concepts of computer vision applied specifically to industrial inspection. You will transition from understanding basic image classification to preparing industrial datasets, training robust defect detection models, and applying optimization techniques for real-world deployment. Through step-by-step written explanations and clear code snippets, you will learn how to build efficient pipelines that identify anomalies with high accuracy. What you'll learn: - Understand the core terminology of computer vision, image classification, and defect detection. - Prepare and preprocess industrial image datasets, including handling class imbalance. - Train a deep learning classifier using PyTorch to detect surface anomalies and manufacturing defects. - Apply modern optimization techniques to accelerate model inference for edge devices. - Evaluate model performance using key metrics like precision, recall, and F1-score. - Configure basic MLOps pipelines to monitor and maintain model accuracy over time. The course begins with foundational definitions of industrial imaging and deep learning architecture before moving into hands-on dataset curation and model training. You will then explore practical optimization strategies to ensure your models run efficiently in production environments. Designed for beginners, this text-based course requires no prior experience in deep learning, though a basic familiarity with Python is helpful. Start reading today to build your first industrial inspection model and automate quality control processes.