Book Image

Hands-On Neural Networks with TensorFlow 2.0

By : Paolo Galeone
Book Image

Hands-On Neural Networks with TensorFlow 2.0

By: Paolo Galeone

Overview of this book

TensorFlow, the most popular and widely used machine learning framework, has made it possible for almost anyone to develop machine learning solutions with ease. With TensorFlow (TF) 2.0, you'll explore a revamped framework structure, offering a wide variety of new features aimed at improving productivity and ease of use for developers. This book covers machine learning with a focus on developing neural network-based solutions. You'll start by getting familiar with the concepts and techniques required to build solutions to deep learning problems. As you advance, you’ll learn how to create classifiers, build object detection and semantic segmentation networks, train generative models, and speed up the development process using TF 2.0 tools such as TensorFlow Datasets and TensorFlow Hub. By the end of this TensorFlow book, you'll be ready to solve any machine learning problem by developing solutions using TF 2.0 and putting them into production.
Table of Contents (15 chapters)
Free Chapter
Section 1: Neural Network Fundamentals
Section 2: TensorFlow Fundamentals
Section 3: The Application of Neural Networks

Conditional GANs

Mirza et al. in their paper, Conditional Generative Adversarial Nets, introduced a conditional version of the GAN framework. This modification is extremely easy to understand and is the foundation of amazing GAN applications that are widely used in today's world.

Some of the most astonishing GAN applications, such as the generation of a street scene from a semantic label to the colorization of an image given a grayscale input, pass through image super-resolution as specialized versions of the conditional GAN idea.

Conditional GANs are based on the idea that GANs can be extended to a conditional model if both G and D are conditioned on some additional information, y. This additional information can be any kind of additional information, from class labels to semantic maps, or data from other modalities. It is possible to perform this conditioning by feeding...