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Deep Learning for Time Series Cookbook

You're reading from   Deep Learning for Time Series Cookbook Use PyTorch and Python recipes for forecasting, classification, and anomaly detection

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Product type Paperback
Published in Mar 2024
Publisher Packt
ISBN-13 9781805129233
Length 274 pages
Edition 1st Edition
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Authors (2):
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Luís Roque Luís Roque
Author Profile Icon Luís Roque
Luís Roque
Vitor Cerqueira Vitor Cerqueira
Author Profile Icon Vitor Cerqueira
Vitor Cerqueira
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Table of Contents (12) Chapters Close

Preface 1. Chapter 1: Getting Started with Time Series 2. Chapter 2: Getting Started with PyTorch FREE CHAPTER 3. Chapter 3: Univariate Time Series Forecasting 4. Chapter 4: Forecasting with PyTorch Lightning 5. Chapter 5: Global Forecasting Models 6. Chapter 6: Advanced Deep Learning Architectures for Time Series Forecasting 7. Chapter 7: Probabilistic Time Series Forecasting 8. Chapter 8: Deep Learning for Time Series Classification 9. Chapter 9: Deep Learning for Time Series Anomaly Detection 10. Index 11. Other Books You May Enjoy

Training a feedforward neural network

This recipe walks you through the process of building a feedforward neural network using PyTorch.

Getting ready

Feedforward neural networks, also known as multilayer perceptrons (MLPs), are one of the simplest types of artificial neural networks. The data flows from the input layer to the output layer, passing through hidden layers without any loop. In this type of neural network, all hidden units in one layer are connected to the units of the following layer.

How to do it…

Let’s create a simple feedforward neural network using PyTorch. First, we need to import the necessary PyTorch modules:

import torch
import torch.nn as nn

Now, we can define a simple feedforward neural network with one hidden layer:

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(10...
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Deep Learning for Time Series Cookbook
Published in: Mar 2024
Publisher: Packt
ISBN-13: 9781805129233
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