Introduction to Deep Learning

In the course, you’ll learn how deep neural networks work and how they are optimized. During our hands-on sessions you will have the opportunity to work on our high-performance systems and train neural networks to solve an image classification problem. We’ll cover various neural network architectures: from a basic fully connected network, to a convolutional neural network and variational auto-encoders (time permitting).

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What will you learn?

In this course you will learn to

• Understand how a neuron and neural network works
• Understand how a neural network is trained
• Explore the effect of hyperparameters on neural network performance
• Work with a high-level machine learning API (Keras)

For whom?

Everyone interested in deep learning, but with no (or little) current experience & knowledge.

Prerequisites

• Python
• Basics of linear algebra
• Basic statistics

Topics

• Neural network: basics
  • How does a neuron work?
  • Fully connected networks
  • Training/optimizing a neural network
• Convolutional neural networks (CNNs)
• (Variational) Auto-Encoders