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Deep Learning from the Basics

You're reading from   Deep Learning from the Basics Python and Deep Learning: Theory and Implementation

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Product type Paperback
Published in Mar 2021
Publisher Packt
ISBN-13 9781800206137
Length 316 pages
Edition 1st Edition
Languages
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Authors (2):
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Shigeo Yushita Shigeo Yushita
Author Profile Icon Shigeo Yushita
Shigeo Yushita
Koki Saitoh Koki Saitoh
Author Profile Icon Koki Saitoh
Koki Saitoh
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Toc

Table of Contents (11) Chapters Close

Preface Introduction 1. Introduction to Python FREE CHAPTER 2. Perceptrons 3. Neural Networks 4. Neural Network Training 5. Backpropagation 6. Training Techniques 7. Convolutional Neural Networks 8. Deep Learning Appendix A

Typical CNNs

CNNs of various architectures have been proposed so far. In this section, we will look at two important networks. One is LeNet (Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner (1998): Gradient-based learning applied to document recognition. Proceedings of the IEEE 86, 11 (November 1998), 2278 – 2324. DOI: (http://dx.doi.org/10.1109/5.726791)). It was one of the first CNNs and was first proposed in 1998. The other is AlexNet (Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton (2012): ImageNet Classification with Deep Convolutional Neural Networks. In F. Pereira, C. J. C. Burges, L. Bottou, & K. Q. Weinberger, eds. Advances in Neural Information Processing Systems 25. Curran Associates, Inc., 1097 – 1105). It was proposed in 2012 and drew attention to deep learning.

LeNet

LeNet is a network for handwritten digit recognition that was proposed in 1998. In the network, a convolution layer and a pooling layer (i.e., a subsampling layer that only...

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