• Complain
LitArk » Books » Children

Micheal Lanham - From Autoencoders and Adversarial Networks to Deepfakes

Here you can read online Micheal Lanham - From Autoencoders and Adversarial Networks to Deepfakes full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. year: 2021, publisher: Apress, genre: Children. Description of the work, (preface) as well as reviews are available. Best literature library LitArk.com created for fans of good reading and offers a wide selection of genres:

Romance novel Science fiction Adventure Detective Science History Home and family Prose Art Politics Computer Non-fiction Religion Business Children Humor

Choose a favorite category and find really read worthwhile books. Enjoy immersion in the world of imagination, feel the emotions of the characters or learn something new for yourself, make an fascinating discovery.

Micheal Lanham From Autoencoders and Adversarial Networks to Deepfakes
  • Book:
    From Autoencoders and Adversarial Networks to Deepfakes
  • Author:
  • Publisher:
    Apress
  • Genre:
  • Year:
    2021
  • Rating:
    4 / 5
  • Favourites:
    Add to favourites
  • Your mark:
    • 80
    • 1
    • 2
    • 3
    • 4
    • 5

From Autoencoders and Adversarial Networks to Deepfakes: summary, description and annotation

We offer to read an annotation, description, summary or preface (depends on what the author of the book "From Autoencoders and Adversarial Networks to Deepfakes" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.

From Autoencoders and Adversarial Networks to Deepfakes — read online for free the complete book (whole text) full work

Below is the text of the book, divided by pages. System saving the place of the last page read, allows you to conveniently read the book "From Autoencoders and Adversarial Networks to Deepfakes" online for free, without having to search again every time where you left off. Put a bookmark, and you can go to the page where you finished reading at any time.

Light

Font size:

↓
↑
Reset

Interval:

↓
↑

Bookmark:

Make
1234567...66
Contents
Landmarks
Book cover of Generating a New Reality Micheal Lanham Generating a New - photo 1
Book cover of Generating a New Reality
Micheal Lanham
Generating a New Reality
From Autoencoders and Adversarial Networks to Deepfakes
1st ed.
Logo of the publisher Micheal Lanham Calgary AB Canada ISBN - photo 2
Logo of the publisher
Micheal Lanham
Calgary, AB, Canada
ISBN 978-1-4842-7091-2 e-ISBN 978-1-4842-7092-9
https://doi.org/10.1007/978-1-4842-7092-9
Micheal Lanham 2021
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This Apress imprint is published by the registered company APress Media, LLC part of Springer Nature.

The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

To my true loves: knowledge, my children, and Rhonda.

Introduction

We live in an era of fake news and uncertain reality. Its a world where reality has become blurred by digital wizardry artists and artificial intelligence practitioners. Its a digital reality now populated with fake news, images, and people. For many, the uncertainty and confusion are overwhelming. Yet, others, like yourself, search to embrace this new era of digital fakery to explore new opportunities.

This book takes an in-depth look at the technology that powers this new digital fake reality. The broad name for this technology or form of AI/ML is generative modeling (GM). It is a form of AI/ML modeling that looks to understand what something represents, as opposed to other forms of modeling that look to classify or predict something.

Generative modeling is not a new concept, but one that has emerged from the application of deep learning. The introduction of deep learning has launched the field into the mainstream. Unfortunately, not all mainstream use of GM is flattering or showcases the power of this diverse technology.

There is a real and speculated fear for most outside and inside the field of GM on what is possible. For many, the application of GM to produce fake anything is abhorrent and nonessential, but the broad applications GM introduces can benefit many industries across many tasks.

In this book, we begin with the assumption you have limited or little knowledge of deep learning and generative modeling. You have a basic knowledge of programming Python and applying data science, including the typical fundamental math knowledge in calculus, linear algebra, and statistics used in data science.

We will cover a wide range of GM techniques and applications in this book, starting with the fundamentals of building a deep learning network and then progressing to GM. Here is a brief overview of the chapters we will explore:

  1. The Basics of Deep Learning: We begin by introducing the basic concepts of deep learning, autoencoders, and how to build simple models with PyTorch. The examples in this chapter demonstrate simple concepts we will apply throughout this book and should not be missed by newcomers.

  2. Unleashing Generative Adversarial Networks: This chapter moves to the fundamentals of explaining the generative adversarial network (GAN) and how it can be used to generate new and novel content. Examples in this chapter explore the applications of GANs from generating fashion to faces.

  3. Exploring the Latent Space: Fundamental to generative modeling is the concept of learning the latent or hidden representation of something. In this chapter, we explore how the latent space is defined and how we can better control it through hyperparameters, loss function, and network configuration.

  4. GANs, GANs, and More GANs: This book explores several variations of GANs, and in this chapter we look at five forms that attempt to learn the latent space differently. We build on knowledge from previous chapters to explore key differences in the way GANs learn and generate content.

  5. Image to Image Content Generation: This chapter covers the advanced application of GANs to enhance the generation of content by learning through understanding translations. The examples in this chapter focus on showcasing paired and unpaired image translation using a variety of powerful GANs.

  6. Residual Network GANs: Throughout this book we will constantly struggle with the generative ability to produce diverse and realistic features. The GANs in this chapter all use residual networks to help identify and learn more realistic feature generation.

  7. Attention Is All We Need: This chapter explores the attention mechanism introduced into deep learning through the application of natural language processing. Attention provides a unique capability to identify and map relevant features with other features. The examples in this chapter demonstrate the power of using an attention mechanism with a GAN.

  8. Advanced Generators: This chapter dives into the deep end and explores the current class of best-performing GANs. The examples in this chapter work from several open-source repositories that showcase how far the field of GM has come in a short time.

  9. Deepfakes and Face Swapping: In this chapter, we switch gears and explore the application of GM for producing deepfakes. Where this whole chapter is dedicated to showcasing the ease of which you can produce a deepfake freely available open-source desktop software.

  10. Cracking Deepfakes: From creating deepfakes and fake content for most of the book, we move on to understanding how generated content can be detected. This chapter looks at the techniques and research currently being done to expose fake content. In the future, these tools will be critical to controlling the digital reality we embrace and understanding what is real.

This book covers a wide range of complex subjects presented in a practical hands-on and technically friendly manner. To get the most out of this book, it is recommended that you engage and work with several of the 40+ examples. All the examples in this book have been tested and run to completion using Google Colab, the recommended platform for this book. While some examples in this book may take up to days to train, most can be run in under an hour.

Next page
1234567...66
Light

Font size:

↓
↑
Reset

Interval:

↓
↑

Bookmark:

Make

Similar books «From Autoencoders and Adversarial Networks to Deepfakes»

Look at similar books to From Autoencoders and Adversarial Networks to Deepfakes. We have selected literature similar in name and meaning in the hope of providing readers with more options to find new, interesting, not yet read works.


Melinda Metz - The Rebel
The Rebel
Melinda Metz
The Rebel
Navin K. Manaswi - Generative Adversarial Networks with Industrial Use Cases: Learning How to Build GAN Applications for Retail, Healthcare, Telecom, Media, Education, and HRTech
Generative Adversarial Networks with Industrial Use Cases: Learning How to Build GAN Applications for Retail, Healthcare, Telecom, Media, Education, and HRTech
Navin K. Manaswi
Generative Adversarial Networks with Industrial Use Cases: Learning How to Build GAN Applications for Retail, Healthcare, Telecom, Media, Education, and HRTech
No cover
No cover
Nina Schick
Deepfakes: The Coming Infocalypse
No cover
No cover
Ravichandiran
Hands-On Deep Learning Algorithms with Python
No cover
No cover
Micheal Lanham
Hands-On Game AI with Python: Implement self-learning AI agents in games using reinforcement learning algorithms and techniques
Micheal Lanham - Practical AI on the Google Cloud Platform: Learn How to Use the Latest AI Cloud Services on the Google Cloud Platform
Practical AI on the Google Cloud Platform: Learn How to Use the Latest AI Cloud Services on the Google Cloud Platform
Micheal Lanham
Practical AI on the Google Cloud Platform: Learn How to Use the Latest AI Cloud Services on the Google Cloud Platform
Jakub Langr - GANs in Action: Deep learning with Generative Adversarial Networks
GANs in Action: Deep learning with Generative Adversarial Networks
Jakub Langr
GANs in Action: Deep learning with Generative Adversarial Networks
Katy Warr - Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery
Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery
Katy Warr
Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery
Micheal Lanham - Learn Unity ML-Agents - Fundamentals of Unity Machine Learning
Learn Unity ML-Agents - Fundamentals of Unity Machine Learning
Micheal Lanham
Learn Unity ML-Agents - Fundamentals of Unity Machine Learning
Micheal Lanham - Game Audio Development with Unity 5.X
Game Audio Development with Unity 5.X
Micheal Lanham
Game Audio Development with Unity 5.X
Ganguly - Learning generative adversarial networks : next-generation deep learning simplified
Learning generative adversarial networks : next-generation deep learning simplified
Ganguly
Learning generative adversarial networks : next-generation deep learning simplified
Antonio Gulli - Deep learning with Keras
Deep learning with Keras
Antonio Gulli
Deep learning with Keras
Reviews about «From Autoencoders and Adversarial Networks to Deepfakes»

Discussion, reviews of the book From Autoencoders and Adversarial Networks to Deepfakes and just readers' own opinions. Leave your comments, write what you think about the work, its meaning or the main characters. Specify what exactly you liked and what you didn't like, and why you think so.