AutoEncoders and Generative Adversarial Networks with Python

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  • Author: Luca Arrotta
  • Level: Intermediate
  • Study time: 3-7 hours
  • Video time: 1.2 hours
  • Exams: 1
Course overview
The Marktechpost "Autoencoders and GANs with Python" course covers theoretical explanations about the different types of Autoencoders and GANs. Hence, you will learn how to build these machine learning solutions with Python.
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Certification included
After completing lecture videos and the quiz with a minimum score of 75% correct, you will be able to download your training completion certificate.

Luca Arrotta

Machine Learning Researcher (Italy)
ABOUT THE INSTRUCTOR
Luca is Ph.D. student at the Department of Computer Science of the University of Milan. His interests are Machine Learning, Data Analysis, IoT, Mobile Programming, and Indoor Positioning. His research currently focuses on Pervasive Computing, Context-awareness, Explainable AI, and Human Activity Recognition in smart environments.
What you are going to learn

A few more words about this course

The Marktechpost "Autoencoders and GANs with Python" course covers theoretical explanations about the different types of Autoencoders and GANs. Hence, you will learn how to build these machine learning solutions with Python.
Throughout this course you will learn:

  • Introduction to machine learning
  • AutoEncoders (AE): Theory
  • AutoEncoders (AE): Lab
  • Generative Adversarial Networks (GAN):Theory
  • Generative Adversarial Networks (GAN):Lab
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