Inside GANs — How Machines Learn to Create Lifelike Images
Generative models let computers make new stuff from data, and this tutorial breaks that down into clear steps.
It shows why generative modeling matter, how two parts works together to learn patterns, and why that matter for making realistic images.
You get a simple view of how GANs stack up against other tools, what tricks researchers are exploring next at the research frontiers, and which combos give the best pictures.
The session also gives hands-on exercises so you can try building things yourself, and solution to check your work.
Some ideas are surprising, other ideas don't works well all the time, but they open doors to creative tools that can paint, edit, or imagine new scenes.
Read it if you like how machines learn to make new things, want a friendly overview, or curious about what comes next.
A quick guide for anyone who want a peek under the hood, without heavy math or jargon.
Read article comprehensive review in Paperium.net:
NIPS 2016 Tutorial: Generative Adversarial Networks
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