Session 1: Deep Learning fundamentals

• Francisco Eduardo Sanchez Karhunen (Universidad de Sevilla)

Room: Salón de Actos - Roots of deep learning techniques. Reasons for layer stacking. Role of weights and activation functions. Layer as a map between representation spaces. Model parameters. Basic structure for classification tasks. Network training as an optimization problem. Weight initialization techniques. Typical loss functions and optimizers. Learning-rate scheduling. - Hands-on lab: Build from scratch a basic multilayer neural network using the Tensorflow-2 library. Sequential mode of layer stacking. Model training to tackle a classical basic image classification problem.

Session 2: Convolutional Neural Networks fundamentals

• Francisco Eduardo Sanchez Karhunen (Universidad de Sevilla)

Room: Salón de Actos - Drawbacks of classical multilayer networks in image classification tasks. Human brain image handling. Convolutional layers: padding and stride. Types of pooling layer. Kernels for feature map extraction. Kernel stacking. CNNs as an extension of classical stacked layer models. Top layers in CNNs. - Hands-on lab. Build from scratch a basic CNN for image classification using the Galaxy10 dataset.

Session 3: Practical considerations in real-world CNNs

• Francisco Eduardo Sanchez Karhunen (Universidad de Sevilla)

Room: Salón de Actos - Overfitting in CNNs. Techniques for overfitting reduction: data augmentation and drop-out. Types of data augmentation. Drop-out rates. Transfer learning: concept and usage. Top Pre-Trained models for image classification. Handling large image datasets: ImageDataGenerators. - Hands-on lab: Use of ImageDataGenerators combined with realistic folder structures in image classification problems. Inclusion of data augmentation in our preprocessing pipelines. Add drop-out layers to the CNN design in session 2. Use of transfer learning in a real situation.