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Generative Adversarial Networks (GAN) The Complete Guide
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English
+ srt | Duration: 20 lectures (3h 47m) | Size: 1.58 GB
Generative Adversarial Networks in Python
What you'll learn
Learn the basic principles of generative models
Build a GAN (Generative Adversarial Network) in Tensorflow
Tensorflow
DCGAN
WGAN
Requirements
Calculus
Probability
Object-oriented programming
Python coding: if/else, loops, lists, dicts, sets
Basic deep learning
GANs have been one of the most interesting developments in deep learning and machine learning recently.
Yann LeCun, a deep learning pioneer, has said that the most important development in recent years has been adversarial training, referring to GANs.
GAN stands for generative adversarial network, where 2 neural networks compete with each other.
What is unsupervised learning?
Unsupervised learning means we're not trying to map input data to targets, we're just trying to learn the structure of that input data.
This course is a comprehensive guide to Generative Adversarial Networks (GANs). The theories are explained in-depth and in a friendly manner. After each theoretical lesson, we will dive together into a hands-on session, where we will be learning how to code different types of GANs in PyTorch and Tensorflow, which is a very advanced and powerful deep learning framework!
In this first course, You will learn
GAN
DCGAN
WGAN
"If you can't implement it, you don't understand it"
Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".
My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times.
Who this course is for
Anyone who wants to improve their deep learning knowledge
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