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

Semi-supervised learning

Semi-supervised learning algorithms fall between supervised and unsupervised learning algorithms.

They rely upon the assumption that we can exploit the information of the labeled data to improve the result of unsupervised learning algorithms and vice versa.

Being able to use semi-supervised learning algorithms depends on the available data: if we have only labeled data, we can use supervised learning; if we don't have any labeled data, we must go with unsupervised learning methods. However, let's say we have the following:

  • Labeled and unlabeled examples
  • Examples that are all labeled with the same class

If we have these, then we can use a semi-supervised approach to solve the problem.

The scenario in which we have all the examples labeled with the same class could look like a supervised learning problem, but it isn't.

If the aim of the...