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AI420: Quality Control in Manufacturing with Image Recognition Models

Training: Artificial Intelligence

Participants learn how to develop and train image recognition models to automatically distinguish defective from non-defective products in manufacturing. They train deep neural networks with TensorFlow and Keras, evaluate performance using test data, and consolidate their knowledge through practice-oriented exercises in quality control.

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Appointments in German language


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Possible course languages: German

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Unfortunately there are currently no available appointments.
Would you like to request an appointment? Then click on 'No matching appointment?'

Request prefered appointment period:

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Agenda:

Data motivation and preprocessing:
- Introduction to quality control challenges in manufacturing, data acquisition and preprocessing for model training

Deep neural networks for image recognition and alternative options for model utilization:
- Fundamentals of deep neural networks, various image recognition models and their applications, selection of appropriate model

Training a neural network with TensorFlow and Keras:
- Introduction to TensorFlow and Keras, creation and training of an image recognition model, optimization of model performance

Model evaluation using test data:
- Methods for evaluating model performance, analysis of results, improvement possibilities and model adjustments

Application of learned concepts to real-world scenarios, discussion of best practices and potential implementation challenges

Objectives:

Learning the application of image recognition for quality control, creation and training of models for product classification, evaluation of model performance.

Target audience:

Developers
IT Professionals

Prerequisites:

AI200 Introduction to Python for Data Science and AI (alternatively basic knowledge in Python)
AI220 Introduction to Machine Learning
AI225 Introduction to Deep Learning and AI

Description:

In the workshop AI420 Quality Control in Manufacturing with Image Recognition Models you will learn how to distinguish defective from non-defective products in manufacturing and how to create and train image recognition models to perform this classification automatically.

Participants learn how to train a deep neural network with TensorFlow and Keras and how to evaluate the model's performance using test data. The course provides both a theoretical foundation and practical exercises to provide participants with the necessary knowledge and skills for effective application of image recognition models in quality control.