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Modern Computer Vision with PyTorch

You're reading from   Modern Computer Vision with PyTorch A practical roadmap from deep learning fundamentals to advanced applications and Generative AI

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Product type Paperback
Published in Jun 2024
Publisher Packt
ISBN-13 9781803231334
Length 746 pages
Edition 2nd Edition
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Authors (2):
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V Kishore Ayyadevara V Kishore Ayyadevara
Author Profile Icon V Kishore Ayyadevara
V Kishore Ayyadevara
Yeshwanth Reddy Yeshwanth Reddy
Author Profile Icon Yeshwanth Reddy
Yeshwanth Reddy
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Toc

Table of Contents (26) Chapters Close

Preface 1. Section 1: Fundamentals of Deep Learning for Computer Vision
2. Artificial Neural Network Fundamentals FREE CHAPTER 3. PyTorch Fundamentals 4. Building a Deep Neural Network with PyTorch 5. Section 2: Object Classification and Detection
6. Introducing Convolutional Neural Networks 7. Transfer Learning for Image Classification 8. Practical Aspects of Image Classification 9. Basics of Object Detection 10. Advanced Object Detection 11. Image Segmentation 12. Applications of Object Detection and Segmentation 13. Section 3: Image Manipulation
14. Autoencoders and Image Manipulation 15. Image Generation Using GANs 16. Advanced GANs to Manipulate Images 17. Section 4: Combining Computer Vision with Other Techniques
18. Combining Computer Vision and Reinforcement Learning 19. Combining Computer Vision and NLP Techniques 20. Foundation Models in Computer Vision 21. Applications of Stable Diffusion 22. Moving a Model to Production 23. Other Books You May Enjoy
24. Index
Appendix

Using a sequential method to build a neural network

So far, we have learned how to implement a neural network using the class of neural networks where we manually built each layer. However, unless we are building a complicated network, the steps to build a neural network architecture are straightforward, where we specify the layers and the sequence with which layers are to be stacked. Let’s move on and learn about a simplified way of defining the neural network architecture using the Sequential class.

We will perform the same steps that we did in the previous sections, except that the class that was used to define the neural network architecture manually will be substituted with a Sequential class to create a neural network architecture. Let’s code up the network for the same toy data that we have worked on in this chapter:

The following code is available as Sequential_method_to_build_a_neural_network.ipynb in the Chapter02 folder of this book’s...

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