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Mastering PyTorch

You're reading from   Mastering PyTorch Build powerful neural network architectures using advanced PyTorch 1.x features

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781789614381
Length 450 pages
Edition 1st Edition
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Author (1):
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Ashish Ranjan Jha Ashish Ranjan Jha
Author Profile Icon Ashish Ranjan Jha
Ashish Ranjan Jha
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Table of Contents (20) Chapters Close

Preface 1. Section 1: PyTorch Overview
2. Chapter 1: Overview of Deep Learning using PyTorch FREE CHAPTER 3. Chapter 2: Combining CNNs and LSTMs 4. Section 2: Working with Advanced Neural Network Architectures
5. Chapter 3: Deep CNN Architectures 6. Chapter 4: Deep Recurrent Model Architectures 7. Chapter 5: Hybrid Advanced Models 8. Section 3: Generative Models and Deep Reinforcement Learning
9. Chapter 6: Music and Text Generation with PyTorch 10. Chapter 7: Neural Style Transfer 11. Chapter 8: Deep Convolutional GANs 12. Chapter 9: Deep Reinforcement Learning 13. Section 4: PyTorch in Production Systems
14. Chapter 10: Operationalizing PyTorch Models into Production 15. Chapter 11: Distributed Training 16. Chapter 12: PyTorch and AutoML 17. Chapter 13: PyTorch and Explainable AI 18. Chapter 14: Rapid Prototyping with PyTorch 19. Other Books You May Enjoy

Evolution of CNN architectures

CNNs have been in existence since 1989, when the first multilayered CNN, called ConvNet, was developed by Yann LeCun. This model could perform visual cognition tasks such as identifying handwritten digits. In 1998, LeCun developed an improved ConvNet model called LeNet. Due to its high accuracy in optical recognition tasks, LeNet was adopted for industrial use soon after its invention. Ever since, CNNs have been one of the most successful machine learning models, both in industry as well as academia. The following diagram shows a brief timeline of architectural developments in the lifetime of CNNs, starting from 1989 all the way to 2020:

Figure 3.5 – CNN architecture evolution – a broad picture

Figure 3.5 – CNN architecture evolution – a broad picture

As we can see, there is a significant gap between the years 1998 and 2012. This was primarily because there wasn't a dataset big and suitable enough to demonstrate the capabilities of CNNs, especially deep CNNs....

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