Applied Edge AI: Deep Learning Outside of the Cloud

Course contents

• Week 1 - Basics and History of Neural Networks:

  In this week, we provide you with a concise history of neural networks to learn about how the field of deep learning evolved and what challenges have been overcome. Furthermore, we provide you with some basic knowledge about the learning process of deep neural networks. We will also have a practical session, where you are going to implement a few basic neural network layers from scratch!

• Week 2 - CNN Architectures:

  Over time it has been found that specific architectures for neural networks achieve the best results. In this week, we provide you with an extensive overview of neural architectures for image classification and how they evolved over time. In the practical session, we will use PyTorch to train a neural network for image classification on the CIFAR-100 dataset.

• Week 3 - Compact Network Design and Knowledge Distillation:

  In this week, you will learn all about the design of compact neural networks and knowledge distillation. Both methods are of high importance when applying deep neural networks on edge devices because they reduce the memory, computation, and energy requirements of neural networks! In the practical session, we will implement and experiment with knowledge distillation on the CIFAR-100 dataset.

• Week 4 - Advanced Deep Compression Methods:

  In this week, we will investigate further model compression techniques, such as pruning, dynamic networks, and network quantization. The practical session of this week is all about the training of a binary neural network on the CIFAR-100 dataset.

• Week 5 - Edge AI Introduction and Federated Learning:

  In this week, we will perform a deep dive into federated learning. A technique that allows to decentralize the training of a neural network. Such a decentralized training has many advantages, e.g., we do not need to copy the data between hosts and can train directly on an edge device! This weeks practical session is as much an experiment for you as it is for us! Together with all other course participants you will train a model for image classification on the ImageNet dataset in a decentralized manner!

• Week 6 - Edge AI Concepts:

  In this week, we will have a look at further advanced concepts behind Edge AI, we will also investigate specific Edge AI hardware and application scenarios. In the practical session, you will experiment with a collaborative inference task, where edge devices and cloud devices work together.

• Week 7 - Final Exam:

  Final Exam

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Requirements for Proofs of Achievement

• Confirmation of Participation: You will automatically receive a free Confirmation of Participation once you have completed at least 50% of the content.
• Certificate: You will receive your official openHPI Certificate by earning at least 50% of the maximum number of points from all graded assignments.

Find out more on our page for Proofs of Achievement and Guidelines.