#012 CNN Convolutional Neural Networks – An Overview

#012 CNN Convolutional Neural Networks – An Overview

Last time we saw we are Convolutions very useful when we include them in our neural networks. In this post we will give just an overview of the 11 Convolutional Neural Network architectures.


Convolutional Neural Netwok Academic Paper Authors Python experiments



” Gradient-based Learning Applied to Document Recognition “ Yann LeChun, Leon Bottou, Youshua Bengaio, Patric Haffner  
 AlexNet  “ImageNet Classification with Deep Convolutional Neural Networks” Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton  
 VGG16 and   VGG19 ” Very Deep Convolutional Neural Networks for Large Scale Image Recognition “ Karen Simonyan, Andrew Zisserman  
 Residual   Networks ” Deep Residual Learning for Image Recognition “ Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun  
 Inception   Network ” Going deeper with convolutions “ Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent  
 Squeez Net “SqueezeNet: AlexNet-Level accuracy with 50x fever parameters and <0.5MB model size”  Forrest N. Iandola
, Song Han
, Matthew W. Moskewicz
, Khalid Ashraf
,William J. Dally
, Kurt Keutzer
 Spatial   Transformer   Networks ” Spatial Transformer Networks “ Max Jaderberg, Karen Simonyan , Andrew Zisserman, Koray Kavukcuoglu  
 DenseNet ” Densely Connected Convolutional Networks “  Gao Huang, Zhuang Liu, Laurens van der Maaten  
 Siamese network ” Learning a Similarity Metric Discriminatively, with Application to Face Verification “ Sumit Chopra, Raia Hadsell, Yann LeCun  
 The Triplet   Network ” Deep metric learning using Triplet network “ Elad Hoffer, Nir Ailon  
 Face Net ” FaceNet: A Unified Embedding for Face Recognition and Clustering “

Florian Schroff, Dmitry Kalenichenko, James Philbin


In the next post we will start talking about LeNet-5 .

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