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Deep Learning for Beginners

You're reading from  Deep Learning for Beginners

Product type Book
Published in Sep 2020
Publisher Packt
ISBN-13 9781838640859
Pages 432 pages
Edition 1st Edition
Languages
Author (1):
Dr. Pablo Rivas Dr. Pablo Rivas
Profile icon Dr. Pablo Rivas

Table of Contents (20) Chapters

Preface 1. Section 1: Getting Up to Speed
2. Introduction to Machine Learning 3. Setup and Introduction to Deep Learning Frameworks 4. Preparing Data 5. Learning from Data 6. Training a Single Neuron 7. Training Multiple Layers of Neurons 8. Section 2: Unsupervised Deep Learning
9. Autoencoders 10. Deep Autoencoders 11. Variational Autoencoders 12. Restricted Boltzmann Machines 13. Section 3: Supervised Deep Learning
14. Deep and Wide Neural Networks 15. Convolutional Neural Networks 16. Recurrent Neural Networks 17. Generative Adversarial Networks 18. Final Remarks on the Future of Deep Learning 19. Other Books You May Enjoy

Introduction to PyTorch

At the time of writing this book, PyTorch is the third most popular overall deep learning framework. Its popularity has been increasing in spite of being relatively new in the world compared to TensorFlow. One of the interesting things about PyTorch is that it allows some customizations that TensorFlow does not. Furthermore, PyTorch has the support of Facebook™.

Although this book covers TensorFlow and Keras, I think it is important for all of us to remember that PyTorch is a good alternative and it looks very similar to Keras. As a mere reference, here is how the exact same shallow neural network we showed earlier would look if coded in PyTorch:

import torch

device = torch.device('cpu')

model = torch.nn.Sequential(
torch.nn.Linear(10, 10),
torch.nn.ReLU(),
torch.nn.Linear(10, 8),
torch.nn.ReLU(),
torch.nn.Linear(8, 2),
torch.nn.Softmax(2)
).to(device)

The similarities are many. Also,...

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