Ever wondered how to generate fake human faces with just a click? Well, wonder no more! Get ready to dive into the world of GANs at our upcoming workshop, 'GAN you do it?', an exciting workshop on GANs for beginners.

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#GAN
you do it?

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Intro
#Supervised_Unsupervised Learning

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● Machine learning networks
that simulate the neural
structure of the brain
● Consist of:
○ Node/Neuron
○ Weights
○ Bias
○ Activation function
What are #neural_networks?

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What is #ComputerVision?
CV is a type of AI that enables computers and systems to derive meaningful
information from images and videos.

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An introduction to #CNN
CNNs are crucial in the field of neural networks because they help in recognizing patterns
They consist of:
1.Convolutional Layer.
2.Activation operation following each convolutional layer.
3.Pooling layer especially Max Pooling layer and also others based on the requirement.
4.Finally Fully Connected Layer.

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Parameters:
tf.keras.layers.Conv2D(
filters,
kernel_size,
padding="valid",
activation=None )

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WHAT IS A GAN ?
● Generative Adversarial Networks is approach to generative modeling using deep
learning methods, such as convolutional neural networks
● Generative modeling is an unsupervised learning task in machine learning
● GANs train generative models by framing the problem as a supervised learning
problem with two sub-models : generator and discriminator
● The generator model generates new examples and the discriminator model tries to
classify generated examples as either real (from the domain) or fake (generated)

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GENERATOR ARCHITECTURE

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DISCRIMINATOR ARCHITECTURE

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SOME USE CASES OF GANS
https://thispersondoesnotexist.com/

11. WHAT HAPPENS INSIDE THE GENERATOR
AND DISCRIMINATOR?

12. TRAINING GANS

13. UPSAMPLING
VS
DOWNSAMPLING

14. IMPLEMENTATION AND EXPLANATION
USING CODE

15. FEEDBACK