In our two-day workshop, you will learn the basics of ‘Deep Learning’ and how to use it to solve numerous problems such as ‘Churn Prediction’ and ‘Image Classification’.
We will start with an intuitive introduction to how neural networks work, followed by an overview of common architectures that can be used for a variety of problems. Furthermore, you will learn how to organize your Deep Learning projects and how to use modern tools like PyTorch, TensorBoard, and PyTorch Lightning profitably. After completing this course you will be able to use Deep Learning independently to solve problems.
Contents
Day 1
- How do neural networks learn?
- Mathematical understanding of feed-forward networks
- PyTorch Basics
- Data processing in PyTorch
- Train and evaluate Feed-Forward networks
- Understanding common problems in neural networks
- Transfer PyTorch to PyTorch-Lightning
- Table-based data modeling with neural networks
Day 2
- Convolutional Neural Networks (CNN) & understand the common architectures
- Image Classification with CNN and Transfer Learning
- Sequence modeling of time series
- Tips and tricks for the modeling of neural networks
- Overview of advanced neural network architectures
Target groups and admission requirements
The course is aimed at:
- Experts, e.g. IT employees, Team Leaders, Project Managers, Process Owners, Innovation Managers,
- Software developers and Data Engineers,
- Data Scientists
Basic Requirements:
- Basic knowledge of Python, Linear Algebra & Probability Theory
