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

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

3 Assessments

71 Playgrounds

11 Challenges

20 Quizzes

This course includes

3 Assessments

71 Playgrounds

11 Challenges

20 Quizzes

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

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

What You'll Learn

Pytorch basics

Course Content

✨Introduction

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

AI is Exploding!

Who Is This Course For?

✨PyTorch Basics

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

Why PyTorch?

Pytorch Tensors

Automatic Gradients with PyTorch

Computation Graphs

📚Learning Points in PyTorch Basics

🖥️ Project: Computation Graph and Gradient Calculation

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

🍀 Challenge: Setting Up a Computation Graph

Solution Review: Setting Up a Computation Graph

🍀 Challenge: Finding Gradient

Solution Review: Finding Gradient

✨First PyTorch Neural Network

Grasp the fundamentals of creating, training, and evaluating a PyTorch neural network using MNIST.

MNIST Image Dataset

Simple Neural Network

Visualizing Training

The MNIST Dataset Class

Training Our Classifier

Querying Our Neural Network

Simple Classifier Performance

📚Learning Points in First PyTorch Neural Network

✨Refinements

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

Loss Function

Activation Function

Optimisation Function

Normalisation

Combined Refinements

📚Learning Points in Refinements

🖥️ Project: Fashion MNIST Classification Model

4 Lessons

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

✨CUDA Basics

4 Lessons

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

Make Your First GAN: Part 1 - Exam

Assessment

✨The GAN Idea

4 Lessons

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

✨Simple 1010 Pattern

6 Lessons

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

10.

🖥️ Project: A Simple 01110 Pattern GAN

6 Lessons

Work your way through creating and training a GAN to generate the 01110 pattern.

11.

✨Handwritten Digits

8 Lessons

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

12.

🖥️ Project: Fashion MNIST GAN

5 Lessons

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

13.

✨Human Faces

7 Lessons

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

Make Your First GAN: Part 2 - Exam

Assessment

14.

✨Convolutional GANs

5 Lessons

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

15.

🖥️ Project: A Fashion MNIST CNN

3 Lessons

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

16.

✨Conditional GANs

6 Lessons

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

17.

🖥️ Project: Fashion MNIST Conditional GAN

5 Lessons

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

Make Your First GAN: Part 3 - Exam

Assessment

18.

✨Conclusion

2 Lessons

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

19.

✨Appendix

7 Lessons

Solve problems in GAN training, using Google Colab, and essential convolution operations with PyTorch.

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Souvik Kundu

Front-end Developer

Eric Downs

Musician/Entrepeneur

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

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