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Age Estimation With Deep Learning: Designing CNN

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21 Jul 2020CPOL5 min read 7K   110   2  
In this article we’ll guide you through one of the most difficult steps in the DL pipeline: the CNN design.
Here we'll look at: the types of CNN layers like convolutional (CONV), activation (ACT), fully-connected (FC), pooling (POOL), normalization (NORM), and dropout (DROP), and look at our CNN structure.

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This article is part of the series 'Age Estimation with Deep Learning View All

License

This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)


Written By
Team Leader VIPAKS
Russian Federation Russian Federation
EDUCATION:

Master’s degree in Mechanics.

PhD degree in Mathematics and Physics.



PROFESSIONAL EXPERIENCE:

15 years’ experience in developing scientific programs
(C#, C++, Delphi, Java, Fortran).



SCIENTIFIC INTERESTS:

Mathematical modeling, symbolic computer algebra, numerical methods, 3D geometry modeling, artificial intelligence, differential equations, boundary value problems.

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