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Contact
Trainings
Training

Deep Learning
Bootcamp

  • Target Audience Beginner
  • Training location Online
  • Dates 1x per quarter
  • Duration Up to 5 days
  • Previous knowledge Python basic knowledge
  • Costs By arrangement

Contents

In our five-day deep learning bootcamp, you learn theory as well as practical applications of deep learning models in an interactive and practical workshop format. 

Deep learning is without a doubt one of the most exciting fields of machine learning and AI. Industry and researchers use deep learning methods to develop groundbreaking applications such as robot control, autonomous driving, or real-time processing of image, text, and voice recognition. However, it is not easy to enter the field of deep learning, particularly for data science practitioners.

We offer a new, practical format with our deep learning bootcamp, where the most important theoretical concepts in deep learning and its specific implementation in Python and TensorFlow are taught over five days. On the one hand, it covers important fundamentals such as the structure of neural networks, gradient descent, and backpropagation. On the other hand, it also covers practical aspects such as developing models in TensorFlow and Keras, deep learning workflows, and best practices that are important in day-to-day work with deep learning models. Furthermore, advanced topics such as GANs (Generative Adversarial Networks) and deep reinforcement learning are also covered; these are used in cutting-edge machine learning and AI.

At a glance

  • Deep learning basics
  • TensorFlow and Keras
  • Image and object recognition with deep learning
  • Text and sequences with RNNs and LSTMs
  • Autoencoder, GANs, reinforcement learnin
Deep Learning<br> Bootcamp

Request course

Do you need support with training in the area of data science, machine learning and AI? Or do you already have a precise idea of which competences you would like to build up in your team? Get in touch with us.
david-schlepps
David Schlepps
Head of AI Academy

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