Description:

This course provides an introduction to deep learning, one of the most powerful and rapidly evolving areas of machine learning. It covers the fundamental concepts, core methodologies, and current trends in deep learning. The course aims to equip students with the theoretical understanding and practical skills needed to apply deep learning techniques effectively and to design and implement models that can be executed on cluster machines.

Skills:

By the end of the course, each student will be able to:

• Understand the key principles and architectures of deep learning
• Identify and analyze relevant real-world use cases
• Implement and train deep learning models using PyTorch
• Design experiments suitable for execution on cluster computing environments

Target Audience:

Researchers and programmers interested in applying deep learning techniques in their work.

Pre-requisites:

A basic understanding of Python programming is fundamental. Prior knowledge of machine learning fundamentals is helpful but not mandatory.