Topics and contents:
- Deep Learning as a method of machine learning - basics and overview
- From materials science to deep learning - examples and applications
- Processing of tabular data with Artificial Neural Networks
- Exercise I: Development of a Convolutional Neural Network (CNN) for the classification of table data
- Processing of material science image data using convolution-based neural networks
- Exercise II: Development of convolution-based neural networks (CNN) for the classification of image data
- Exercise III: U-Net architectures for segmentation of material science image data
- Manual and synthetic generation of training data
- After a short introduction, which is not mathematically in-depth, application examples of Deep Learning are developed together.
- You will learn how to implement and apply neural networks with the help of Python and suitable libraries. The focus is on the independent application of the developed models.
- By executing and modifying the provided scripts on your own, you will be able to directly apply the acquired knowledge in practice.
- After the participation you will know the possibilities and problems of machine learning, so that you can efficiently transfer and adapt the learned contents to your own data.
Information on participating / attending:
1st - 5th day 9am-1pm
Required software tools for participation:
PUTTY (participants will receive installation instructions shortly before the training)
04/12/2021 09:00 - 04/16/2021 14:00
Business and commerce, Scientists and scholars
Types of events:
Seminar / workshop / discussion
Kommunikation & Medien
Event is free:
Language of the text:
URL of this event: http://idw-online.de/en/event67857
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