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Hands-On Machine Learning with C++

You're reading from   Hands-On Machine Learning with C++ Build, train, and deploy end-to-end machine learning and deep learning pipelines

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Product type Paperback
Published in May 2020
Publisher Packt
ISBN-13 9781789955330
Length 530 pages
Edition 1st Edition
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Author (1):
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Kirill Kolodiazhnyi Kirill Kolodiazhnyi
Author Profile Icon Kirill Kolodiazhnyi
Kirill Kolodiazhnyi
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Toc

Table of Contents (19) Chapters Close

Preface 1. Section 1: Overview of Machine Learning
2. Introduction to Machine Learning with C++ FREE CHAPTER 3. Data Processing 4. Measuring Performance and Selecting Models 5. Section 2: Machine Learning Algorithms
6. Clustering 7. Anomaly Detection 8. Dimensionality Reduction 9. Classification 10. Recommender Systems 11. Ensemble Learning 12. Section 3: Advanced Examples
13. Neural Networks for Image Classification 14. Sentiment Analysis with Recurrent Neural Networks 15. Section 4: Production and Deployment Challenges
16. Exporting and Importing Models 17. Deploying Models on Mobile and Cloud Platforms 18. Other Books You May Enjoy

Understanding image classification using the LeNet architecture

In this section, we'll implement a convolutional neural network for image classification. We are going to use the famous dataset of handwritten digits called the Modified National Institute of Standards and Technology (MNIST), which can be found at http://yann.lecun.com/exdb/mnist/. The dataset is a standard that was proposed by the US National Institute of Standards and Technology to calibrate and compare image recognition methods using machine learning, primarily based on neural networks.

The creators of the dataset used a set of samples from the US Census Bureau, with some samples written by students of American universities added later. All the samples are normalized, anti-aliased grayscale images of 28 x 28 pixels. The MNIST database contains 60,000 images for training and 10,000 images for testing. There...

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