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Mobile Artificial Intelligence Projects

You're reading from  Mobile Artificial Intelligence Projects

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781789344073
Pages 312 pages
Edition 1st Edition
Languages
Authors (3):
Karthikeyan NG Karthikeyan NG
Profile icon Karthikeyan NG
Arun Padmanabhan Arun Padmanabhan
Profile icon Arun Padmanabhan
Matt Cole Matt Cole
Profile icon Matt Cole
View More author details

Table of Contents (12) Chapters

Preface 1. Artificial Intelligence Concepts and Fundamentals 2. Creating a Real-Estate Price Prediction Mobile App 3. Implementing Deep Net Architectures to Recognize Handwritten Digits 4. Building a Machine Vision Mobile App to Classify Flower Species 5. Building an ML Model to Predict Car Damage Using TensorFlow 6. PyTorch Experiments on NLP and RNN 7. TensorFlow on Mobile with Speech-to-Text with the WaveNet Model 8. Implementing GANs to Recognize Handwritten Digits 9. Sentiment Analysis over Text Using LinearSVC 10. What is Next? 11. Other Books You May Enjoy

Building a feedforward neural network to recognize handwritten digits, version one

In this section, we will use the knowledge that we gained from the last two chapters to tackle a problem that has unstructured data – image classification. The idea is to take a dive into solving a Computer Vision task with the current setup and the basics of neural networks that we are familiar with. We have seen that feedforward neural networks can be used for prediction using structured data; let's try that on images to classify handwritten digits.

To solve this task, we are going to leverage the MNSIT database and use the handwritten digits dataset. MNSIT stands for Modified National Institute of Standards and Technology. It is a large database that's commonly used for training, testing, and benchmarking image-related tasks in Computer Vision.

The MNSIT digits dataset contains...

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