Study Guide

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

Evaluation scale

0 - 5

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.

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.

Prerequisites

Deep learning 1