Abstract
In this chapter, we will discuss the basic architecture of neural networks including activation functions, forward propagation, and backpropagation. We will also create a simple neural network model from scratch using the sigmoid activation function. In particular, this chapter will discuss:
-
Neural network architecture.
-
Activation functions used in neural networks.
-
Forward propagation.
-
Loss function of neural networks.
-
Backpropagation.
Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI (Artificial Intelligence) will transform in the next several years.
Andrew Ng
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
CNNs are attributed to its inventor, Yann LeCun.
- 2.
In vector calculus, a Jacobian matrix is computed from the first-order partial derivatives of a vector function. When the output is a square matrix, it is named as a Jacobian.
- 3.
downloaded from https://www.kaggle.com/c/dogs-vs-cats/data on April 02, 2018, 07:40 IST.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Ghatak, A. (2019). Introduction to Neural Networks . In: Deep Learning with R. Springer, Singapore. https://doi.org/10.1007/978-981-13-5850-0_2
Download citation
DOI: https://doi.org/10.1007/978-981-13-5850-0_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-5849-4
Online ISBN: 978-981-13-5850-0
eBook Packages: Computer ScienceComputer Science (R0)