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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

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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This course includes

19 Playgrounds

12 Quizzes

Course Overview

What You'll Learn

Course Content

Recommendations

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

This course focuses on generative adversarial networks (GANs), which have reshaped machine learning and deep learning landscapes...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

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

Course Content

Getting Started

1 Lessons

Get familiar with GANs using Keras, tackling image synthesis, training techniques, and applications.

About the Course

Deep Learning Basics and Environment Test

5 Lessons

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

Introduction to Generative Models

7 Lessons

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

Implementing Our First GAN

6 Lessons

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

Evaluating Our First GAN

6 Lessons

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

Improving Our First GAN

8 Lessons

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

Overview: Improving Our First GAN

Challenges in Training GANs

Tricks of the Trade

GAN Model Architectures

GAN Algorithms and Loss Functions

Wasserstein GAN with Gradient Penalty

Relativistic GAN

Digest and Test: Improving Our First GAN

Synthesizing and Manipulating Images with GANs

7 Lessons

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

Overview: Synthesizing and Manipulating Images with GANs

Image-to-Image Translation

Experimental Setup: Image Synthesis and Manipulation

Implementation of pix2pix

Implementation of pix2pixHD

Training: Image Synthesis and Manipulation

Digest and Test: Synthesizing and Manipulating Images with GANs

Progressive Growing of GANs

6 Lessons

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

Overview: Progressive Growing of GANs

Progressive Growing of GANs

Experimental Setup: PGGANs

Custom Layer Implementation

Model Implementation: PGGANs

Digest and Test: Progressive Growing of GANs

Generation of Discrete Sequences Using GANs

7 Lessons

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

Overview: Generation of Discrete Sequences Using GANs

Natural Language Generation with GANs

Experimental Setup: Generating Sequences with GANs

Training: Generating Sequences with GANs

Model Implementation: Generating Sequences with GANs

Inference: Generating Sequences with GANs

Digest and Test: Generation of Discrete Sequences Using GANs

10.

Text-to-Image Synthesis with GANs

7 Lessons

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

Overview: Text-to-Image Synthesis with GANs

Text-to-Image Synthesis

Training: Text-to-Image GAN Synthesis

Model Implementation: Text-to-Image GAN Synthesis

Improving the Baseline Model

Inference: Text-to-Image GAN Synthesis

Digest and Test: Text-to-Image Synthesis with GANs

11.

Speech Enhancement with GANs

6 Lessons

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

Overview: Speech Enhancement with GANs

Audio Enhancement

Experimental Setup: SEGAN

Model Implementation: SEGAN

Qualitative and Quantitative Evaluation

Digest and Test: Speech Enhancement with GANs

12.

TequilaGAN—Identifying GAN Samples

6 Lessons

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

Overview: TequilaGAN - Identifying GAN Samples

Identifying GAN Samples

Feature Extraction

Metrics

Experiments

Digest and Test: TequilaGAN—Identifying GAN Samples

13.

What’s Next in GANs

6 Lessons

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

Overview: What’s Next in GANs

What We’ve Learned So Far

Unanswered Questions in GANs

Artistic GANs

Recent and Yet-to-Be-Explored GAN Topics

Digest and Test: What’s Next in GANs

14.

Conclusion

1 Lessons

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

Wrap Up

15.

Appendix

1 Lessons

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

Deep Learning Environment Setup

Course Author

Packt

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