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Machine Learning Solutions

You're reading from   Machine Learning Solutions Expert techniques to tackle complex machine learning problems using Python

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788390040
Length 566 pages
Edition 1st Edition
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Author (1):
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Jalaj Thanaki Jalaj Thanaki
Author Profile Icon Jalaj Thanaki
Jalaj Thanaki
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Table of Contents (19) Chapters Close

Machine Learning Solutions
Foreword
Contributors
Preface
1. Credit Risk Modeling 2. Stock Market Price Prediction FREE CHAPTER 3. Customer Analytics 4. Recommendation Systems for E-Commerce 5. Sentiment Analysis 6. Job Recommendation Engine 7. Text Summarization 8. Developing Chatbots 9. Building a Real-Time Object Recognition App 10. Face Recognition and Face Emotion Recognition 11. Building Gaming Bot List of Cheat Sheets Strategy for Wining Hackathons Index

Building the baseline model


In this section, we will be looking at the coding part. You can refer to the code given at this GitHub link: https://github.com/jalajthanaki/Real_time_object_detection/tree/master/base_line_model.

First, download the project from the given link and install OpenCV, as per the information given earlier in this chapter. When you download this project folder, there is a pre-trained MobileNet SSD that has been implemented using the caffe library, but here, we are using the pre-trained binary model. We are using OpenCV for loading the pre-trained model as well as streaming the video feeds from the webcam.

In the code, first, we specify the libraries that we need to import and define the command-line arguments that will be used to run the script. We need to provide the parameter file and the pre-trained model. The name of the parameter file is MobileNetSSD_deploy.prototxt.txt and the filename for the pre-trained model is MobileNetSSD_deploy.caffemodel. We have also defined...

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