Descriptions
Generative Adversarial Networks (GANs): The Complete Guide. GANs have recently become one of the most interesting developments in the field of deep learning and machine learning. Yann LeCun, a pioneer of deep learning, said that the most important development in recent years has been adversarial learning, referring to GANs. GAN stands for Generative Adversarial Network, in which two neural networks compete with each other. Unsupervised learning means that we are not trying to map inputs to targets, we are simply trying to learn the structure of those inputs. This course is a comprehensive guide to generative adversarial networks (GANs). The theories are explained in detail and in a friendly manner. After each theory lesson, we’ll dive into a hands-on session together where we’ll learn how to code different types of GANs in PyTorch and Tensorflow, which are very advanced and powerful deep learning frameworks!
What will you learn
- Learn the basic principles of generative models.
- Create a GAN (Generative Adversarial Network) in Tensorflow
- tensorflow
- DKGAN
- RGAN
Who is this course for?
- Anyone who wants to improve their knowledge of deep learning
Characteristics of Generative Adversarial Networks (GANs): A Complete Guide
- Publisher: Udemy
- Teacher: Hoang Quy La
- English language
- Level: Intermediate
- Number of courses: 20
- Duration: 3 hours 47 minutes.
Contents of Generative Adversarial Networks (GANs): A Complete Guide
Requirements
- Calculus
- Probability
- Object-oriented programming
- Python Coding: if/else, loops, lists, dictionaries, sets
- Basic Deep Learning
Images
Sample clip
Installation instructions
Extract the files and watch on your favorite player
Subtitles: English
Quality: 720p
Download links
Password file(s): free download software
file size
1.36 GB