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Applied Deep Learning and Computer Vision for Self-Driving Cars

You're reading from  Applied Deep Learning and Computer Vision for Self-Driving Cars

Product type Book
Published in Aug 2020
Publisher Packt
ISBN-13 9781838646301
Pages 332 pages
Edition 1st Edition
Languages
Authors (2):
Sumit Ranjan Sumit Ranjan
Profile icon Sumit Ranjan
Dr. S. Senthamilarasu Dr. S. Senthamilarasu
Profile icon Dr. S. Senthamilarasu
View More author details

Table of Contents (18) Chapters

Preface 1. Section 1: Deep Learning Foundation and SDC Basics
2. The Foundation of Self-Driving Cars 3. Dive Deep into Deep Neural Networks 4. Implementing a Deep Learning Model Using Keras 5. Section 2: Deep Learning and Computer Vision Techniques for SDC
6. Computer Vision for Self-Driving Cars 7. Finding Road Markings Using OpenCV 8. Improving the Image Classifier with CNN 9. Road Sign Detection Using Deep Learning 10. Section 3: Semantic Segmentation for Self-Driving Cars
11. The Principles and Foundations of Semantic Segmentation 12. Implementing Semantic Segmentation 13. Section 4: Advanced Implementations
14. Behavioral Cloning Using Deep Learning 15. Vehicle Detection Using OpenCV and Deep Learning 16. Next Steps 17. Other Books You May Enjoy

The hyperbolic tangent activation function

Finally, we have another function, called the Hyperbolic Tangent Activation (tanh) function, which looks as follows:

Fig 2.16: Hyperbolic tangent

The tanh function is very similar to the sigmoid function; the range of a tanh function is (-1,1). Tanh functions are also S-shaped, like sigmoid functions. The advantage of the tanh function is that a positive will be mapped as strongly positive, a negative will be mapped as strongly negative, and 0 will be mapped to 0, as shown in Fig 2.16.

For more information about the performance of the hyperbolic function (tanh), refer to http://proceedings.mlr.press/v15/glorot11a/glorot11a.pdf.

In the next section of this chapter, we will learn about the cost function.

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