– Bases in linear algebra and analysis

– Bases in Object-oriented coding

–     Understand the fundamental concepts of Deep Learning

–     Comprehend the structure and function of artificial neural networks.

–     Learn about different types of neural networks (e.g., feedforward, convolutional, recurrent).

–     Be able to use deep learning frameworks like Keras

–     Understand the importance of data preprocessing and data augmentation in deep learning.

–     Design and build simple neural networks from scratch.

Day 1: Welcome and introduction to neural networks in Python/Keras
Day 2: Convolutional neural networks
Day 3: Deep architectures, applications
Day 4: Recurrent neural networks
Day 5: Efficiency in Deep Learning, Exam in the afternoon + question time

2h long written exam with open questions.