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

Regression

Regression is one of the oldest tools for mathematical modeling, classification, and prediction, yet remains quite a powerful one. Regression finds application in varied fields ranging from engineering, physical science, biology, and the financial market, to the social sciences. It is a fundamental tool in the hands of statisticians and data scientists. In this chapter, we will cover the following topics:

  • Linear regression
  • Different types of linear regression
  • Logistic regression
  • Apply linear regression to estimate the price of a house
  • Apply logistic regression to identify handwritten digits

Let us first start with understanding what regression really is.