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Hands-On Generative Adversarial Networks with Keras

Hands-On Generative Adversarial Networks with Keras

Gain insights into GANs with Keras. Delve into deep learning, experiment with real-world GAN applications, train high-resolution image GANs, and learn advances in computer vision, NLP, and audio.

Beginner

80 Lessons

8h

Certificate of Completion

Gain insights into GANs with Keras. Delve into deep learning, experiment with real-world GAN applications, train high-resolution image GANs, and learn advances in computer vision, NLP, and audio.

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Explanations

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This course includes

19 Playgrounds
12 Quizzes

This course includes

19 Playgrounds
12 Quizzes
Course Overview
What You'll Learn
Course Content

Course Overview

This course focuses on generative adversarial networks (GANs), which have reshaped machine learning and deep learning landscapes. This course also aims to provide a comprehensive introduction to GANs, starting with a deep dive into deep learning and generative models and their extensive applications in artificial intelligence. You will start by getting an overview of deep learning and generative models. You will then explore the wide-ranging applications where GANs can be utilized. You will get hands-on ex...Show More

What You'll Learn

An understanding of generative adversarial networks and its application to artificial intelligence

Working knowledge of developing, evaluating, and improving GANs

Hands-on experience in training GAN models to produce and improve high-resolution images

The ability to generate natural language and speech enhancement using GAN models

What You'll Learn

An understanding of generative adversarial networks and its application to artificial intelligence

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Course Content

1.

Getting Started

Get familiar with GANs using Keras, tackling image synthesis, training techniques, and applications.
About the Course
2.

Deep Learning Basics and Environment Test

Look at deep learning principles, setup, neural architectures, and validating with practical exercises.
Overview: Deep Learning Basics and Environment Test
Deep Learning Basics
Neural Network Architecture
The Deep Learning Environment Test
Digest and Test: Deep Learning Basics and Environment Test
3.

Introduction to Generative Models

Examine generative models, their types, and introduce GANs, covering their strengths and weaknesses.
Overview: Introduction to Generative Models
Discriminative and Generative Models Compared
Generative Models
Generative Adversarial Networks
GANs: Building Blocks
GANs: Strengths and Weaknesses
Digest and Test: Introduction to Generative Models
4.

Implementing Our First GAN

Apply your skills to implementing and training GANs using DCGAN, CIFAR-10, and auxiliary functions.
Overview: Implementing Our First GAN
Implementing Our First GAN
Discriminator
Auxiliary Functions
Training Our GAN
Digest and Test: Implementing Our First GAN
5.

Evaluating Our First GAN

Dig into evaluating GANs, covering image quality, variety, and qualitative and quantitative methods.
Overview: Evaluating Our First GAN
The Evaluation of GANs
Qualitative Methods
Quantitative Methods
GANs and the Birthday Paradox
Digest and Test: Evaluating Our First GAN
6.

Improving Our First GAN

8 Lessons

Follow the process of enhancing GANs by addressing training challenges, architectures, and advanced algorithms.
7.

Synthesizing and Manipulating Images with GANs

7 Lessons

Practice using GANs for image synthesis and manipulation with pix2pix and pix2pixHD.
8.

Progressive Growing of GANs

6 Lessons

Try out PGGANs to enhance image synthesis with progressive resolution growth and custom layers.
9.

Generation of Discrete Sequences Using GANs

7 Lessons

Walk through generating discrete sequences using GANs for natural language tasks.
10.

Text-to-Image Synthesis with GANs

7 Lessons

Work your way through GAN implementation for text-to-image synthesis and model enhancements.
11.

Speech Enhancement with GANs

6 Lessons

Apply your skills to implement and evaluate a SEGAN model for speech enhancement.
12.

TequilaGAN—Identifying GAN Samples

6 Lessons

Take a closer look at techniques to evaluate and identify samples generated by GANs.
13.

What’s Next in GANs

6 Lessons

Tackle GAN advancements, applications in arts, biology, and audio, while exploring open research questions.
14.

Conclusion

1 Lesson

Build on deep learning principles and practical GAN applications for advanced model deployment.
15.

Appendix

1 Lesson

Get familiar with setting up a deep learning environment using Anaconda, CUDA, TensorFlow, and Keras.

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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