Deep Learning
RWTH Aachen University
"Deep Learning" gives an overview of the basic types of neural networks and how to train them. Starting with Multi-Layer Perceptrons (MLPs), the course covers the most important training tricks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), as well as Transformers, and gives an outlook on effective training and efficient fine-tuning of large multi-modal models.
About this Course
Dive into Deep Learning - Master the Foundations of Neural Networks with our exciting MOOC! Artificial neural networks form the foundation of modern AI systems. “Deep Learning” offers participants a comprehensive introduction to the core principles and fundamental building blocks used in today’s neural networks. The course covers the most important types of neural networks, like MLPs, CNNs, RNNs, and Transformers, as well as practical techniques for efficient training and the reuse large pre-trained models. Throughout the course, students will gain a robust understanding of the general training process and key differences between different network types, as well as practical knowledge through hands-on programming exercises. By the end of the course, students will be equipped with the knowledge and skills to understand, train, and apply deep neural networks to a variety of problems, laying a strong foundation for advanced exploration of the field. Enroll now to embark on your journey into deep learning! 36:Tb3
What You'll Learn
- Multi-layer perceptrons
- Efficient optimization methods
- Convolutional Neural Networks
- Recurrent Neural Networks
- Attention & Transformers
- Large-scale learning & efficient fine-tuning
Prerequisites
- You will need an introductory knowledge of linear algebra and machine learning. For the programming exercises, you will need a working knowledge of Python.
Instructors
Prof. Dr. Bastian Leibe
Head of Computer Vision Group
Christian Schmidt M.Sc.
Doctoral Student at the Computer Vision Group