This article was posted by Xiu-Shen Wei. Xiu-Shen Wei is a 2nd-year Ph.D. candidate of Department of Computer Science and Technology in Nanjing University and a member of LAMDA Group.
Deep Neural Networks, especially Convolutional Neural Networks (CNN), allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-arts in visual object recognition, object detection, text recognition and many other domains such as drug discovery and genomics.
In addition, many solid papers have been published in this topic, and some high quality open source CNN software packages have been made available. There are also well-written CNN tutorials or CNN software manuals. However, it might lack a recent and comprehensive summary about the details of how to implement an excellent deep convolutional neural networks from scratch. Thus, they collected and concluded many implementation details for DCNNs.
Here they will introduce these extensive implementation details, i.e., tricks or tips, for building and training your own deep networks.
Table of Contents:
Introduction
Sec. 1: Data Augmentation
Sec. 2: Pre-Processing
Sec. 3: Initializations
Sec. 4: During Training
Sec. 5: Activation Functions
Sec. 6: Regularizations
Sec. 7: Insights from Figures
Sec. 8: Ensemble
Miscellaneous
References & Source Links
To check out all this information, click here. For more articles about Neural Networks, click here.
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Posted 12 April 2021
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