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Computer Vision Projects with OpenCV and Python 3

You're reading from   Computer Vision Projects with OpenCV and Python 3 Six end-to-end projects built using machine learning with OpenCV, Python, and TensorFlow

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789954555
Length 182 pages
Edition 1st Edition
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Author (1):
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Matthew Rever Matthew Rever
Author Profile Icon Matthew Rever
Matthew Rever
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Table of Contents (9) Chapters Close

Preface 1. Setting Up an Anaconda Environment FREE CHAPTER 2. Image Captioning with TensorFlow 3. Reading License Plates with OpenCV 4. Human Pose Estimation with TensorFlow 5. Handwritten Digit Recognition with scikit-learn and TensorFlow 6. Facial Feature Tracking and Classification with dlib 7. Deep Learning Image Classification with TensorFlow 8. Other Books You May Enjoy

Evaluating the results

After we finished training, as we can see from the following screenshot, we get a result of over 99%, so that is significantly better than what we got with softmax or our SVM:

Deep learning is probably the most powerful machine learning technique, due to its ability to learn very complex pattern recognition. It's just dominating everything else for advanced computer vision, speech processing, and more—stuff that conventional machine learning techniques haven't been all that successful at. However, that doesn't necessarily mean that you want to use deep learning for everything. Deep learning generally acquires a large number of examples—many thousands, if not millions sometimes—and it can also be very computationally expensive. So, it's not always the best solution, although it is very powerful, as we have seen right...

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