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In this course, you will learn:
- Understand the fundamentals and vocabulary of Deep Learning.
- Determine which kind of neural networks should be used to tackle certain challenges.
- Practical and instructive workshops will help you become more familiar with Deep Learning libraries.
Syllabus:
1. Machine Learning (ML) and Experimental Protocol
- Introduction to ML
- ML Tools
2. Introduction to Deep Learning
- Modular Approaches
- Backpropagation
- Optimization
3. Intro to Convolutional Neural Networks (CNN)
- Introduction to CNN
- CNN Architectures
4. Introduction to Recurrent Neural Networks
- Sequence to Sequence Models
- Concepts in Natural Language Processing
5. Bias and Discrimination in ML
- Differences of Fairness
- Fairness in Pre- In- and Post-Processing
