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Make Your First GAN Using PyTorch

Gain insights into Generative Adversarial Networks with PyTorch. Delve into neural networks, adversarial training, and building GANs for image generation, including conditional GANs for class-specific data.

Advanced

97 Lessons

13h

Certificate of Completion

Gain insights into Generative Adversarial Networks with PyTorch. Delve into neural networks, adversarial training, and building GANs for image generation, including conditional GANs for class-specific data.
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Explanations

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This course includes

3 Assessments
71 Playgrounds
11 Challenges
20 Quizzes
Course Overview
What You'll Learn
Course Content
Apply Your Skills
Recommendations

Course Overview

This course is an introduction to Generative Adversarial Networks (GANs) and a practical step-by-step tutorial on making your own with PyTorch. Through this course, you will learn how to build GANs with industry-standard tools. In the first section, you will dive into PyTorch and refresh your understanding of neural networks by building a simple image classifier. In the second section of this course, you will explore the idea of adversarial training and build progressively more sophisticated GANs; first by...Show More
This course is an introduction to Generative Adversarial Networks (GANs) and a practical step-by-step tutorial on making your own with PyTorch. Through this course, you will learn how to build GANs with industry-standard tools. In the first section, you w...Show More

TAKEAWAY SKILLS

Convolutional Gan

Conditional Gan

Pytorch Basics

Cuda Basics

Neural Networks

Refinements

Human Faces Gan

Basics Of Gan

Handwritten Digits Gan

Simple 1010 Gan

What You'll Learn

Pytorch basics
Making a neural network with PyTorch
Refining the neural network output
Basics of CUDA
The basic idea of GAN
Learning a simple 1010 pattern using GAN
Learning handwritten digits using GAN
Learning human faces using GAN
Convolutional GANs
Conditional GANs
Pytorch basics

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Course Content

1.

✨Introduction

2 Lessons

Get familiar with GANs, their potential, applications, and hands-on creation using Python and PyTorch.

2.

✨PyTorch Basics

5 Lessons

Get started with PyTorch basics, utilizing tensors and automatic gradients to simplify neural network training.

3.

🖥️ Project: Computation Graph and Gradient Calculation

4 Lessons

Go hands-on with setting up computation graphs and calculating gradients in PyTorch.

5.

✨Refinements

6 Lessons

Deepen your knowledge of refining GANs with loss functions, activation functions, optimizers, and normalization.

6.

🖥️ Project: Fashion MNIST Classification Model

4 Lessons

Follow the process of building and evaluating a Fashion MNIST classification model in PyTorch.

7.

✨CUDA Basics

4 Lessons

Build on CUDA fundamentals, showcasing GPUs' efficiency in parallel processing for ML tasks.

9.

✨The GAN Idea

4 Lessons

Learn how to use GANs for generating realistic images through adversarial training.

10.

✨Simple 1010 Pattern

6 Lessons

Unpack the core of developing and training a simple GAN for generating 10x10 patterns.

12.

✨Handwritten Digits

8 Lessons

Grasp the fundamentals of training GANs for handwritten digit generation using the MNIST dataset.

13.

🖥️ Project: Fashion MNIST GAN

5 Lessons

Solve problems in building and training GANs using PyTorch with Fashion MNIST data.

14.

✨Human Faces

7 Lessons

Follow the process of generating human faces using GANs, managing datasets, and optimizing training.

16.

✨Convolutional GANs

5 Lessons

Master convolutional GANs for enhanced image generation, efficient pattern recognition, and experimental optimization.

17.

🖥️ Project: A Fashion MNIST CNN

3 Lessons

Try out defining, reviewing, and evaluating a CNN architecture for the Fashion MNIST dataset.

18.

✨Conditional GANs

6 Lessons

Discover the logic behind Conditional GANs to generate class-specific and diverse images.

19.

🖥️ Project: Fashion MNIST Conditional GAN

5 Lessons

Lay out a solution for building and training a Fashion MNIST conditional GAN.

21.

✨Conclusion

2 Lessons

Break down complex ideas with PyTorch fundamentals, GAN applications, and ethical considerations.

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