Book Image

Hands-On Deep Learning for Images with TensorFlow

By : Will Ballard
Book Image

Hands-On Deep Learning for Images with TensorFlow

By: Will Ballard

Overview of this book

TensorFlow is Google’s popular offering for machine learning and deep learning, quickly becoming a favorite tool for performing fast, efficient, and accurate deep learning tasks. Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations of real-world projects, teaching you how to leverage TensorFlow’s capabilities to perform efficient image processing using the power of deep learning. With the help of this book, you will get to grips with the different paradigms of performing deep learning such as deep neural nets and convolutional neural networks, followed by understanding how they can be implemented using TensorFlow. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow and Keras.
Table of Contents (7 chapters)

Classical Neural Network

Now that we've prepared our image data, it's time to take what we've learned and use it to build a classical, or dense neural network. In this chapter, we will cover the following topics:

  • First, we'll look at classical, dense neural networks and their structure.
  • Then, we'll talk about activation functions and nonlinearity.
  • When we come to actually classify, we need another piece of math, softmax. We'll discuss why this matters later in this chapter.
  • We'll look at training and testing data, as well as Dropout and Flatten, which are new network components, designed to make the networks work better.
  • Then, we'll look at how machine learners actually solve.
  • Finally, we'll learn about the concepts of hyperparameters and grid searches in order to fine-tune and build the best neural network that we can.