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This article will explain in detail about Python-OpenCV deep learning example analysis, Xiaobian think it is quite practical, so share it with you as a reference, I hope you can gain something after reading this article.

1. Introduction to Deep Learning in Computer Vision

Deep learning is driving profound changes in the field of computer vision, and we begin by explaining key concepts in deep learning to better understand the vast world of deep learning.

1.1 Features of Deep Learning

Deep learning outperforms traditional machine learning methods for many computer vision tasks, but when choosing which method to complete a particular computational task, the difference between deep learning and traditional machine learning methods should be clear to choose the appropriate method:

Traditional machine learning algorithms can mostly run on low-end machines, while deep learning algorithms require higher computing power to train correctly, and usually these calculations can be optimized using GPU parallel computing.

When there is a lack of domain understanding of feature engineering, deep learning techniques will be the preferred approach, since in deep learning the task of finding relevant features is part of the algorithm, which automates feature engineering by reducing the problem. Feature engineering is the process of applying domain knowledge to create feature detectors and extractors with the goal of reducing the complexity of data so that traditional machine learning methods can learn correctly. Thus, the performance of traditional machine learning algorithms depends on how accurately they recognize and extract features, whereas deep learning techniques attempt to automatically extract high-level features from data.

Both traditional machine learning and deep learning are capable of processing massive data sets. But the main difference between the two approaches is the degree to which their performance varies as the data size increases. For example, when dealing with small data sets, deep learning algorithms struggle to find mapping relationships in the data and may therefore perform poorly, as deep learning typically requires large amounts of data to adjust its internal parameters. Empirically, deep learning outperforms other techniques if the dataset is large, while traditional machine learning algorithms are preferable when the dataset is small.

The following diagram summarizes the main differences between machine learning and deep learning:

As can be seen from the above figure, the key selection points of machine learning and deep learning are as follows:

Compute Resources (Deep Learning)

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