Applied Deep Learning in Physics and Engineering

Fundamentals of Deep Learning. Generalization, Regularization and Validation, Optimization and Hyperparameter Tuning, Convolutional Neutral Networks, Recurrent Neural Networks, and Graph Neural Networks. Classification and Regression Tasks. Visualization & Advanced Computer Vision Methods. Autoencoders. Generative models, variational autoencoders, generative adversarial networks. Applications in physics and engineering, for example, image recognition, analysis of time series data, pulse shape discrimination, real-time low-power on-device computing (IoT applications); Practical skills of using the TensorFlow framework via the high-level Keras python interface; Methods to verify neural network predictions, e.g., through independent experimental data that is obtained in a lab assignment. **Outline for distance course:** In the distance course, communication between teachers and students is done using the learning management system and e-meeting tools. A computer with a stable internet connection and webcam is required for participating in the course and examination.