The course provides an introduction to key concepts, architectures, and algorithms for Deep Learning and its applications. It covers the following topics:
Part One: Multilayer Peceptron and Backpropagation
From a Single Layer Perceptron to Deep Learning: a historical perspective
Algorithms for training MLPs: Stochastic Gradient Descent and its variants; Backpropagation
Alternative activation and loss functions; Initialization, Regularization, Dropout, Batch Normalization
Introduction to GPU-computing, Keras, TensorFlow; Hyperparameter Tuning
Part Two: Deep Learning for computer vision and language processing
Convolutional Networks: key architectures and applications; Transfer Learning
Recurrent Networks: from Backpropagation Through Time to Attention Mechanism and Transformers
Part Three: Generative Networks
Autoencoders
Generative Adversarial Networks (GAN's)
Diffusion Models
During the course several state-of-the-art applications of Deep Learning to image recognition, computer vision, language modeling, game playing programs, etc., will be discussed. The course consists of weekly lectures, three programming assignments (in Python, TensorFlow) and the final written exam.
Outcome: Not Provided
