Learn the fundamentals of deep learning by building and training your first neural networks with Keras in this hands-on introductory workshop.
This course is offered by HIDA
Data Science Training at HIDA
HIDA provides diverse continuing training programs in Information and Data Science, drawing from the entire Helmholtz Association.
Through specialized data science courses, AI training for administration and management, as well as lectures and events, HIDA enhances professional expertise and fosters interdisciplinary exchange.
This course provides a hands-on introduction to deep learning for learners with basic experience in Python and machine learning. It introduces the core concepts behind neural networks and the typical deep-learning workflow, using practical examples implemented with Keras. Participants will learn how to prepare data, build, train, and evaluate simple neural network models, and understand the role of different network layers. The goal of the course is to establish a solid foundation in deep learning, enabling learners to continue exploring the topic independently or progress to more advanced, in-depth courses.
This course is based on lesson plans provided by The Carpentries and will be taught by a certified carpentries instructor. To get an idea of the topics covered in this workshop, you can have a look at the example lesson plans here. But please be aware that the exact content covered in each course can vary slightly, depending on the course instructors and time constraints. You will get more information on this course closer to the starting date.
Learning Goals
At the end of the course, you will be able to
- Understand the basic concepts and workflow of deep learning and neural networks
- Build and train simple neural network models in Python using Keras
- Recognize suitable use cases for deep learning and identify next learning steps
Course date
Register now:
For more information on how to register, please follow the link on the course date.
Prerequisites
- Basic Python programming skills and familiarity with the Pandas package.
- Basic knowledge on machine learning, including the following concepts: Data cleaning, train & test split, type of problems (regression, classification), overfitting & underfitting, metrics (accuracy, recall, etc.).
Target Group
The course is appropriate for students, researchers and professionals who have basic experience in Python and machine learning and would like to gain a practical introduction to deep learning.
This course is free of charge.
