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
1
Section 1: Neural Network Fundamentals
4
Section 2: TensorFlow Fundamentals
8
Section 3: The Application of Neural Networks

Supervised learning

Supervised learning algorithms work by extracting knowledge from a knowledge base (KB), that is, the dataset that contains labeled instances of the concept we need to learn about.

Supervised learning algorithms are two-phase algorithms. Given a supervised learning problem—let's say, a classification problem—the algorithm tries to solve it during the first phase, called the training phase, and its performance is measured in the second phase, called the testing phase.

The three dataset splits (train, validation, and test), as defined in the previous section, and the two-phase algorithm should sound an alarm: why do we have a two-phase algorithm and three dataset splits?

Because the first phase (should—in a well-made pipeline) uses two datasets. In fact, we can define the stages:

  • Training and validation: The algorithm analyzes the dataset...