GANs are a powerful deep learning technique for generating realistic synthetic data. This course covers the core concepts of GANs, including the architecture of generator and discriminator networks. You'll delve into the adversarial training process, learn to implement GANs from scratch, and explore various applications beyond image generation. The course also addresses challenges like mode collapse and training instability, providing strategies for overcoming them. Finally, you'll gain insights into evaluating GAN performance and ethical considerations in GAN development. Numerical examples are provided on implementing GANs, including case studies on real-world applications and techniques for handling training challenges. Begin your journey into AI generation by enrolling today!
Why GAN is important?
How do GAN works?
Adversarial Training
What is the Generator?
How does the Generator Work?
Adversarial Training of Generator
What is the Discriminator?
How does the Discriminator Work?
Adversarial Training of Discriminator
Xavier/Glorot Initialization
He Initialization
Scaled Random Initialization
Learning Rate
Sigmoid
tanh
ReLU
MSE
RMSE
Entropy
MAE
Huber Loss
Gradient descent
Adam
SGDM
Python
Imran is a distinguished academic professional with a Doctorate in Computer Science and Engineering, boasting 16 years of extensive experience in teaching and administration. His academic journey began with a Bachelor of Engineering in Information Technology from Rajiv Gandhi Technical University (2001–2005), followed by a Master of Technology in Computer Science and Engineering from the same university (2007–2009). He then earned his PhD from Dr. K.N. Modi University (2011–2015). Imran’s career in academia spans over 16 years, during which he has held various significant positions. He started as a lecturer at All Saints College of Technology (2005–2008) and Gyan Ganga Institute of Technology and Management (2008–2009). He then served as an assistant professor at the Technocrats Institute of Technology (2009–2010) before becoming the head of departments at the Trinity Institute of Technology and Research (2010–2012) and Sagar Institute of Science, Technology, and Research (2012–2016). Since November 2016, he has been a professor at the University of Technology and Applied Sciences in the Sultanate of Oman. Imran’s contributions to academia are further highlighted by his selection in the Falling Wall Lab, Oman, for two consecutive years (2018 and 2019) and receiving the Best Head of the Department award at Sagar Group of Institutions in 2015. He has published 19 research papers in prestigious international journals, including IEEE and Scopus-indexed journals, and authored a textbook on wireless sensor networks published by Wiley Publications, UK, in 2022. Additionally, he is a key member of the Computer Engineering Specialization Committee at his current university, responsible for curriculum development and benchmarking against globally ranked universities.
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This course includes:
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Validity:60 days
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Validity:60 days
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Category:Deep Learning
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Lessons:13
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Students:1908
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Language:English
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Certificates:Yes
Payment details
Generative Adversarial Network
Expert Understanding with numerical examples and case studies.
