Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Antonio Gulli, Amita Kapoor, Sujit Pal
Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By: Antonio Gulli, Amita Kapoor, Sujit Pal

Overview of this book

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.
Table of Contents (19 chapters)
17
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18
Index

What is regression?

Regression is normally the first algorithm that people in machine learning work with. It allows us to make predictions from data by learning about the relationship between a given set of dependent and independent variables. It has its use in almost every field; anywhere that has an interest in drawing relationships between two or more things will find a use for regression.

Consider the case of house price estimation. There are many factors that can have an impact on the house price: the number of rooms, the floor area, the locality, the availability of amenities, the parking space, and so on. Regression analysis can help us in finding the mathematical relationship between these factors and the house price.

Let us imagine a simpler world where only the area of the house determines its price. Using regression we could determine the relationship between the area of the house (independent variable: these are the variables that do not depend upon any other variables...