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Deep Learning By Example

You're reading from  Deep Learning By Example

Product type Book
Published in Feb 2018
Publisher Packt
ISBN-13 9781788399906
Pages 450 pages
Edition 1st Edition
Languages
Toc

Table of Contents (18) Chapters close

Preface 1. Data Science - A Birds' Eye View 2. Data Modeling in Action - The Titanic Example 3. Feature Engineering and Model Complexity – The Titanic Example Revisited 4. Get Up and Running with TensorFlow 5. TensorFlow in Action - Some Basic Examples 6. Deep Feed-forward Neural Networks - Implementing Digit Classification 7. Introduction to Convolutional Neural Networks 8. Object Detection – CIFAR-10 Example 9. Object Detection – Transfer Learning with CNNs 10. Recurrent-Type Neural Networks - Language Modeling 11. Representation Learning - Implementing Word Embeddings 12. Neural Sentiment Analysis 13. Autoencoders – Feature Extraction and Denoising 14. Generative Adversarial Networks 15. Face Generation and Handling Missing Labels 16. Implementing Fish Recognition 17. Other Books You May Enjoy

Different types of errors

In machine learning, there are two types of errors, and as a newcomer to data science, you need to understand the crucial difference between both of them. If you end up minimizing the wrong type of error, the whole learning system will be useless and you won’t be able to use it in practice over unseen data. To minimize this kind of misunderstanding between practitioners about these two types of errors, we are going to explain them in the following two sections.

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