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A Review on Conventional Machine Learning vs Deep Learning

机译:常规机器学习与深度学习的回顾

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摘要

In now days, deep learning has become a prominent and emerging research area in computer vision applications. Deep learning permits the multiple layers models for computation to learn representations of data by processing in their original form while it is not possible in conventional machine learning. These methods surprisingly improved the accuracy of various image processing domains such as speech recognition, face recognition, object detection and in biomedical applications. Deep neural networks (DNN) such as convolutional neural network (CNN) provide tremendous results in processing of images and videos, while another approach of deep network i.e. recurrent neural network (RNN) gives better performance with sequential data such as text and speech.
机译:如今,深度学习已成为计算机视觉应用程序中一个突出且新兴的研究领域。深度学习允许用于计算的多层模型通过以其原始形式进行处理来学习数据表示,而常规机器学习则不可能。这些方法令人惊讶地提高了各种图像处理领域的准确性,例如语音识别,面部识别,物体检测以及生物医学应用。诸如卷积神经网络(CNN)之类的深度神经网络(DNN)在图像和视频的处理方面提供了巨大的成果,而深度网络的另一种方法,即递归神经网络(RNN)在诸如文本和语音之类的顺序数据方面表现出更好的性能。

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