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Deep Learning with TensorFlow 2 and Keras

You're reading from   Deep Learning with TensorFlow 2 and Keras Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781838823412
Length 646 pages
Edition 2nd Edition
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Authors (3):
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Dr. Amita Kapoor Dr. Amita Kapoor
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Dr. Amita Kapoor
Sujit Pal Sujit Pal
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Sujit Pal
Antonio Gulli Antonio Gulli
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Antonio Gulli
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Table of Contents (19) Chapters Close

Preface 1. Neural Network Foundations with TensorFlow 2.0 2. TensorFlow 1.x and 2.x FREE CHAPTER 3. Regression 4. Convolutional Neural Networks 5. Advanced Convolutional Neural Networks 6. Generative Adversarial Networks 7. Word Embeddings 8. Recurrent Neural Networks 9. Autoencoders 10. Unsupervised Learning 11. Reinforcement Learning 12. TensorFlow and Cloud 13. TensorFlow for Mobile and IoT and TensorFlow.js 14. An introduction to AutoML 15. The Math Behind Deep Learning 16. Tensor Processing Unit 17. Other Books You May Enjoy
18. Index

Classification tasks and decision boundaries

In the preceding section, we learned about the task of regression or prediction. In this section we will talk about another important task: the task of classification. Let us first understand the difference between regression (also sometimes referred to as prediction) and classification:

  • In classification the data is grouped into classes/categories, while in regression the aim is to get a continuous numerical value for given data.
  • For example, identifying the number of handwritten digits is a classification task; all handwritten digits will belong to one of the ten numbers lying between [0-9]. The task of predicting the price of the house depending upon different input variables is a regression task.
  • In the classification task, the model finds the decision boundaries separating one class from another. In the regression task, the model approximates a function that fits the input-output relationship.
  • Classification is a...
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