Deep learning has achieved formidable results in the image analysis field in recent years, in many cases exceeding human performance. This success opens paths for new applications, entrepreneurship and research, while making the field very competitive. This course aims at providing the students with the theoretical and practical basis for understanding and using deep learning for image analysis applications. Program to be followed The course will be composed of lectures and practical sessions. Moreover, experts from industry will present practical applications of deep learning.

Lectures will include:

• Artificial neural networks, back-propagation algorithm
• Convolutional neural network
• Design and optimization of a neural architecture
• Successful architectures (AlexNet, VGG, GoogLeNet, ResNet)
• Analysis of neural network function
• Image classification and segmentation
• Auto-encoders and generative networks
• Current research trends and perspectives During the practical sessions, the students will code in Python, using Keras and Tensorflow. They will be confronted with the practical problems linked to deep learning: architecture design; optimization schemes and hyper-parameter selection; analysis of results. Prerequisites: Linear algebra, basic probability and statistics