Book Image

The TensorFlow Workshop

By : Matthew Moocarme, Abhranshu Bagchi, Anthony So, Anthony Maddalone
Book Image

The TensorFlow Workshop

By: Matthew Moocarme, Abhranshu Bagchi, Anthony So, Anthony Maddalone

Overview of this book

Getting to grips with tensors, deep learning, and neural networks can be intimidating and confusing for anyone, no matter their experience level. The breadth of information out there, often written at a very high level and aimed at advanced practitioners, can make getting started even more challenging. If this sounds familiar to you, The TensorFlow Workshop is here to help. Combining clear explanations, realistic examples, and plenty of hands-on practice, it’ll quickly get you up and running. You’ll start off with the basics – learning how to load data into TensorFlow, perform tensor operations, and utilize common optimizers and activation functions. As you progress, you’ll experiment with different TensorFlow development tools, including TensorBoard, TensorFlow Hub, and Google Colab, before moving on to solve regression and classification problems with sequential models. Building on this solid foundation, you’ll learn how to tune models and work with different types of neural network, getting hands-on with real-world deep learning applications such as text encoding, temperature forecasting, image augmentation, and audio processing. By the end of this deep learning book, you’ll have the skills, knowledge, and confidence to tackle your own ambitious deep learning projects with TensorFlow.
Table of Contents (13 chapters)
Preface

Introduction

In the previous chapter, you learned about regression problems where the target variable is continuous. A continuous variable can take any value between a minimum and maximum value. You learned how to train such models with TensorFlow.

In this chapter, you will look at another type of supervised learning problem called classification, where the target variable is discrete — meaning it can only take a finite number of values. In industry, you will most likely encounter such projects where variables are aggregated into groups such as product tiers, or classes of users, customers, or salary ranges. The objective of a classifier is to learn the patterns from the data and predict the right class for observation.

For instance, in the case of a loan provider, a classification model will try to predict whether a customer is most likely to default in the coming year based on their profile and financial position. This outcome can only take two possible values (yes...