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:
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Neural network architecture.
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Activation functions used in neural networks.
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Forward propagation.
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Loss function of neural networks.
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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
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Notes
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CNNs are attributed to its inventor, Yann LeCun.
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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.
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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
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DOI: https://doi.org/10.1007/978-981-13-5850-0_2
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Publisher Name: Springer, Singapore
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Online ISBN: 978-981-13-5850-0
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