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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 FREE CHAPTER 2. Building Blocks of Neural Networks 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

Three kinds of machine learning problems

In all our previous examples, we tried to solve either classification (predicting cats or dogs) or regression (predicting the average time users spend in the platform) problems. All these are examples of supervised learning, where the goal is to map the relationship between training examples and their targets and use it to make predictions on unseen data.

Supervised learning is just one part of machine learning, and there are other different parts of machine learning. There are three different kinds of machine learning:

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

Let's look in detail at the kinds of algorithms.

Supervised learning

Most of the successful...

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