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)

Grid searches

In this section, we will explore grid searches.

We'll talk a bit about optimization versus grid searching, setting up a model generator function, setting up a parameter grid and doing a grid search with cross-validation, and finally, reporting the outcomes of our grid search so we can pick the best model.

So why, fundamentally, are there two different kinds of machine learning activities here? Well, optimization solves for parameters with feedback from a loss function: it's highly optimized. Specifically, a solver doesn't have to try every parameter value in order to work. It uses a mathematical relationship with partial derivatives in order to move along what is called a gradient. This lets it go essentially downhill mathematically to find the right answer.

Grid searching isn't quite so smart. In fact, it's completely brute force. When we...