Deep Learning and its Applications

Module code: EE5C16 · Semester: Semester 1 · ECTS: 10

Resources

• Module descriptor (TCD)

Questions by Year

0 of 1 done · tick papers and add notes as you go

• 2023/24

Papers open on tcd.ie (sign in to TCD first).

Topics

• Least squares, logistic regression, decision trees, SVMs LO4
• Feed-forward networks and training LO1, LO6
• Convolutional neural networks LO1, LO5
• Recurrent networks and transformers LO1, LO5
• Large language models LO1
• Keras/TensorFlow in practice; overfitting and debugging LO2–3, LO6
• Ethics and responsible deployment LO9

Learning outcomes

1. Describe the main neural network architectures
2. Describe the parameters in libraries such as Keras
3. Implement neural networks in Python with Keras
4. Evaluate machine learning performance and pitfalls
5. Design architectures for an application
6. Train and debug models, e.g. detect and fix overfitting
7. Plan industry projects that need neural networks
8. Answer typical deep learning interview questions
9. Analyse ethical, social and environmental impacts and design responsibly
10. Use version control and resource planning for DL projects

Summarised from the module descriptor, checked November 2026.