Administrative Information
| Title | Batch Processing |
| Duration | 60 mins |
| Module | B |
| Lesson Type | Tutorial |
| Focus | Technical - Deep Learning |
| Topic | Batch processing |
Keywords
batch processing,back propagation,
Learning Goals
- Understand mechanisms behind batch processing and back propagation
- Learn batch processing by modifying existing Python code
Expected Preparation
Learning Events to be Completed Before
Obligatory for Students
None.
Optional for Students
None.
References and background for students
None.
Recommended for Teachers
None.
Lesson materials
Instructions for Teachers
The tutorial involves finishing code that apply variations of Gradient Descent on data. Students program in python and are provided with data to show the (dis-)advantages of different batching strategies. Programming should complete in about an hour.
Outline
Instructors can choose to let students work individually or in groups on the practical notebook for the entire class (60 mins) or spend time discussing the results immediately in class.
Acknowledgements
The Human-Centered AI Masters programme was Co-Financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.