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AI225: Introduction to Deep Learning and AI

Training: Artificial Intelligence

Participants receive a practical introduction to deep learning and AI. The course covers the fundamentals of neural networks, model architectures, and training. It addresses the use of Python and libraries such as TensorFlow and Keras, including pre-trained networks and transfer learning, to develop and implement AI applications more quickly and effectively.

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Possible course languages: German

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Unfortunately there are currently no available appointments.
Would you like to request an appointment? Then click on 'No matching appointment?'

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Agenda:

Introduction to neural networks and deep learning

Key concepts: Artificial neurons, weights, training and architecture

Python and libraries for deep learning: TensorFlow and Keras

Implementation of simple neural networks in various application scenarios

Usage of pre-trained networks (TensorFlow Hub) and transfer learning

Practical examples and exercises for designing, adapting and training models

Summary and final discussion on how to transfer the learned knowledge to one's own context

Objectives:

Understand neural network fundamentals, learn key concepts, design and customize models, work with Python and TensorFlow, utilize pre-trained networks and transfer learning.

Target audience:

Developers
IT professionals
Professionals without programming experience

Prerequisites:

AI200 Introduction to Python for Data Science and AI (alternatively basic knowledge in Python)

Description:

A world where computers learn and solve complex tasks: In the workshop AI225 Introduction to Deep Learning and AI participants will be introduced to the fundamentals of neural networks and deep learning. They will learn important concepts such as artificial neurons, model architectures and model training. Using practical examples, it will be demonstrated how to design, customize and train effective models with Python and libraries such as TensorFlow and Keras.

The course also covers the use of pre-trained networks from TensorFlow Hub and transfer learning to accelerate the development of AI applications. At the end of the course, participants will be able to implement neural networks in existing frameworks themselves and apply them to their problem.