Kalin Generative Adversarial Networks Cookbook

Over 100 recipes to build generative models using Python, TensorFlow, and Keras
1. Auflage 2018
ISBN: 978-1-78913-958-7
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 268 Seiten

No detailed description available for "Generative Adversarial Networks Cookbook".

Autoren/Hrsg.

Kalin, Josh

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke

Inhaltsverzeichnis

Table of Contents - What is a Generative Adversarial Network?

- Data First - How to prepare your dataset
- My First GAN in under 100 lines
- Dreaming new Kitchens using DCGAN
- Pix2Pix Image-to-Image Translation
- Style Transfering Your image using CycleGAN
- Use Simulated Images to Create Photo Realistic Eyeballs using simGAN
- From Image to 3D Models using GANs

Über Autor(innen)

Kalin Josh:

Josh Kalin is a Physicist and Technologist focused on the intersection of robotics and machine learning. Josh works on advanced sensors, industrial robotics, machine learning, and automated vehicle research projects. Josh holds degrees in Physics, Mechanical Engineering, and Computer Science. In his free time, he enjoys working on cars (has owned 36 vehicles and counting), building computers, and learning new techniques in robotics and machine learning (like writing this book).