Neural Network Tutorials - Herong's Tutorial Examples
∟GAN (Generative Adversarial Network)
This chapter provides introductions and tutorials on GAN (Generative Adversarial Network). Topics include introduction to the classical GAN model; 'gan-roy.py' GAN implementation; DCGAN (Deep Convolutional GAN) model.
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What Is GAN (Generative Adversarial Network)
GAN Example with TensorFlow - gan-roy.py
gan-roy.py Code Walk-Through
DCGAN (Deep Convolutional Generative Adversarial Network)
Takeaways:
- GAN (Generative Adversarial Network)? GAN is an extension of the
generative model of neural networks by replacing the error function
with a discriminative neural network.
- Generator is a neural network that takes low-dimensional input vectors
and generates high- dimensional output vectors.
- Discriminator is a neural network that takes high-dimensional input vectors
and generates low-dimensional output vectors.
- In a GAN (Generative Adversarial Network), the generative neural network
plays the generator role to produce outputs, and the discriminative neural network
plays the discriminator role to exam the quality of those outputs.
- DCGAN (Deep Convolutional Generative Adversarial Network)
is an extension of the GAN model by using Deep Convolutional Networks
in both Generator and Discriminator.
- "gan-roy.py" is a GAN implementation example provided by Rahul Roy.
Table of Contents
About This Book
Deep Playground for Classical Neural Networks
Building Neural Networks with Python
Simple Example of Neural Networks
TensorFlow - Machine Learning Platform
PyTorch - Machine Learning Platform
Gradio - ML Demo Platform
CNN (Convolutional Neural Network)
RNN (Recurrent Neural Network)
GNN (Graph Neural Network)
►GAN (Generative Adversarial Network)
Performance Evaluation Metrics
References
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