Deep learning 2Laajuus (5 cr)

Objective

The student can apply the methods used in deep learning in the Keras/TensorFlow environment. In addition, the student knows how to use GPU computing and CSC supercomputers in the training of neural networks and can use already trained neural networks in a web browser.

Content

- Use of Keras/TensorFlow environment - Distributed training on multiple GPUs - The use of CSC's supercomputers in the training of neural networks - Basics of large language models (LLM). - Running neural networks in different environments (deployment) - Implementation of a trained neural network in a web browser - MLops basics - Artificial intelligence and ethics

Qualifications

Deep learning 1

Assessment criteria, satisfactory (1)

A grade of 1 requires the return of all assignments and 50% of the course points. In addition, the returned course exercises must show that the student knows how to use the Keras/Tensorflow environment, load a model into it, and teach and use it.

Assessment criteria, excellent (5)

For a grade of 5, approx. 90% of the points in the course exercises are required. In practice, this means that all returned code works and the reflection sections of the exercises are commendably done.