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The Deep Learning with PyTorch Workshop

You're reading from The Deep Learning with PyTorch Workshop

Product type Book

Published in Jul 2020

Publisher Packt

ISBN-13 9781838989217

Pages 330 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Hyatt Saleh

Table of Contents (8) Chapters

Preface

1. Introduction to Deep Learning and PyTorch

2. Building Blocks of Neural Networks

3. A Classification Problem Using DNN

4. Convolutional Neural Networks

5. Style Transfer

6. Analyzing the Sequence of Data with RNNs

Appendix

Dealing with an Underfitted or Overfitted Model

Building a deep learning solution is not just a matter of defining an architecture and then training a model using the input data; on the contrary, most would agree that this is the easy part. The art of creating high-tech models entails achieving high levels of accuracy that surpass human performance. This section will introduce the topic of error analysis, which is commonly used to diagnose a trained model to discover what actions are more likely to have a positive impact on the performance of the model.

Error Analysis

Error analysis refers to the initial analysis of the error rate over the training and validation sets of data. This analysis is then used to determine the best course of action to improve the performance of the model.

In order to perform error analysis, it is necessary to determine the Bayes error (also known as the irreducible error), which is the minimum achievable error. Several decades ago, the Bayes error...

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Authors (1)

Hyatt Saleh

Hyatt Saleh discovered the importance of data analysis for understanding and solving real-life problems after graduating from college as a business administrator. Since then, as a self-taught person, she not only works as a machine learning freelancer for many companies globally, but has also founded an artificial intelligence company that aims to optimize everyday processes. She has also authored Machine Learning Fundamentals, by Packt Publishing.

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