The six algorithms are: Tiny Images Representation + Classifiers; HOG (Histogram of Oriented Gradients) Features Representation + Classifiers; Bag of SIFT (Scale Invariant Feature Transform) Features Representation + Classifiers; Training a CNN (Convolutional Neural Network) from scratch; Fine Tuning a Pre-Trained Deep Network (AlexNet); and Pre-Trained Deep Network (AlexNet) Features Representation + Classifiers.
The codes were written with Python in Jupyter Notebook, and they could be executed on both CPUs and GPUs.
The six algorithms are: Tiny Images Representation + Classifiers; HOG (Histogram of Oriented Gradients) Features Representation + Classifiers; Bag of SIFT (Scale Invariant Feature Transform) Features Representation + Classifiers; Training a CNN (Convolutional Neural Network) from scratch; Fine Tuning a Pre-Trained Deep Network (AlexNet); and Pre-Trained Deep Network (AlexNet) Features Representation + Classifiers.
The codes were written with Python in Jupyter Notebook, and they could be executed on both CPUs and GPUs.

Image Classification: Step-by-step Classifying Images with Python and Techniques of Computer Vision and Machine Learning
112
Image Classification: Step-by-step Classifying Images with Python and Techniques of Computer Vision and Machine Learning
112Product Details
ISBN-13: | 9781987073652 |
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Publisher: | Barnes & Noble Press |
Publication date: | 05/15/2019 |
Series: | Computer Vision and Machine Learning , #1 |
Pages: | 112 |
Product dimensions: | 6.00(w) x 9.00(h) x 0.30(d) |