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Deep Learning with PyTorch

You're reading from   Deep Learning with PyTorch A practical approach to building neural network models using PyTorch

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781788624336
Length 262 pages
Edition 1st Edition
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Author (1):
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Vishnu Subramanian Vishnu Subramanian
Author Profile Icon Vishnu Subramanian
Vishnu Subramanian
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Table of Contents (11) Chapters Close

Preface 1. Getting Started with Deep Learning Using PyTorch 2. Building Blocks of Neural Networks FREE CHAPTER 3. Diving Deep into Neural Networks 4. Fundamentals of Machine Learning 5. Deep Learning for Computer Vision 6. Deep Learning with Sequence Data and Text 7. Generative Networks 8. Modern Network Architectures 9. What Next? 10. Other Books You May Enjoy

Workflow of a machine learning project

In this section, we will formalize a solution framework that can be used to solve any machine learning problem by bringing together the problem statement, evaluation, feature engineering, and avoidance of overfitting.

Problem definition and dataset creation

To define the problem, we need two important things; namely, the input data and the type of problem.

What will be our input data and target labels? For example, say we want to classify restaurants based on their speciality—say Italian, Mexican, Chinese, and Indian food—from the reviews given by the customers. To start working with this kind of problem, we need to manually hand annotate the training data as one of the...

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