Book Image

Hands-On Neural Networks with TensorFlow 2.0

By : Galeone
Book Image

Hands-On Neural Networks with TensorFlow 2.0

By: 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

Fine-tuning

Fine-tuning is a different approach to transfer learning. Both share the same goal of transferring the knowledge learned on a dataset on a specific task to a different dataset and a different task. Transfer learning, as shown in the previous section, reuses the pre-trained model without making any changes to its feature extraction part; in fact, it is considered a non-trainable part of the network.

Fine-tuning, instead, consists of fine-tuning the pre-trained network weights by continuing backpropagation.

When to fine-tune

Fine-tuning a network requires having the correct hardware; backpropagating the gradients through a deeper network requires you to load more information in memory. Very deep networks have been...