Name: SO Basics of Neural Networks 2025
Start: 2025-10-16T09:00:00+02:00
End: 2025-10-17T17:00:00+02:00
Location: IAA-CSIC

SO Basics of Neural Networks 2025

from Thursday, October 16, 2025 (9:00 AM) to Friday, October 17, 2025 (5:00 PM)

Monday, October 13, 2025
Tuesday, October 14, 2025
Wednesday, October 15, 2025
Thursday, October 16, 2025
9:00 AM Session 1: Deep Learning fundamentals - Francisco Eduardo Sanchez Karhunen (Universidad de Sevilla)
Session 1: Deep Learning fundamentals
- Francisco Eduardo Sanchez Karhunen (Universidad de Sevilla)
9:00 AM - 12:00 PM
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.
2:00 PM Session 2: Convolutional Neural Networks fundamentals - Francisco Eduardo Sanchez Karhunen (Universidad de Sevilla)
Session 2: Convolutional Neural Networks fundamentals
- Francisco Eduardo Sanchez Karhunen (Universidad de Sevilla)
2:00 PM - 6:30 PM
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.
Friday, October 17, 2025
9:00 AM Session 3: Practical considerations in real-world CNNs - Francisco Eduardo Sanchez Karhunen (Universidad de Sevilla)
Session 3: Practical considerations in real-world CNNs
- Francisco Eduardo Sanchez Karhunen (Universidad de Sevilla)
9:00 AM - 1:00 PM
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.
2:00 PM Session 4: Evaluation - Francisco Eduardo Sanchez Karhunen
Session 4: Evaluation
- Francisco Eduardo Sanchez Karhunen
2:00 PM - 5:00 PM
Room: Salón de Actos