Deep Learning (ML II)

About This Course

In this module, the students learn how to interpret, modify and create new deep learning architectures. We look at the different kinds of networking, activation functions and shortcuts, developing a classification of architectural principles that can be used for new projects.

One important aspect is also understanding how to train networks and simplify them later, using self-learning principles.

Learning Objectives

• Students understand the pillars of sustainability and their effects on organizational behavior and performance.
• Students understand business ethics and be able to apply ethical managerial practice in diverse contexts.
• Students practice communication skills.
• Students practice teamwork and problem solving.
• Students are given the opportunity for self-assessment.

Requirements

Students should have prior knowledge of basic machine learning concepts, proficiency in Python, and familiarity with foundational deep learning techniques. Experience with frameworks like TensorFlow or PyTorch is recommended but not required.