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Deep Learning

Deep Learning: GoogLeNet Explained

One of the initial convolutional neural network that dared to go deeper

Richmond Alake
Towards Data Science
8 min readDec 23, 2020

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GoogLeNet is a 22-layer deep convolutional neural network that’s a variant of the Inception Network, a Deep Convolutional Neural Network developed by researchers at Google.

The GoogLeNet architecture presented in the ImageNet Large-Scale Visual Recognition Challenge 2014(ILSVRC14) solved computer vision tasks such as image classification and object detection — find out how well it performed at the conclusion section of this article.

Today GoogLeNet is used for other computer vision tasks such as face detection and recognition, adversarial training etc.

What to expect: This article explores the architectural details of the GoogLeNet network.

Who is this article for?

Deep learning practitioners of all levels can follow the contents and information presented in this article with relative ease. There are definitions and clear explanations wherever technical terms are introduced.

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Towards Data Science
Towards Data Science

Published in Towards Data Science

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Richmond Alake
Richmond Alake

Written by Richmond Alake

Machine Learning Content Creator with 1M+ views— Computer Vision Engineer. Interested in gaining and sharing knowledge on Technology and Finance

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