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Autoencoders has been in the deep learning literature for a long time now, most popular for data compression tasks. With their easy structure and not so complicated underlying mathematics, they became one of the first choices when it comes to dimensionality reduction in simple data. However, using basic fully connected…


Autoencoders are unsupervised neural network models that are designed to learn to represent multi-dimensional data with fewer parameters. Data compression algorithms have been known for a long time however, learning the nonlinear operations for mapping the data into lower dimensions has been the contribution of autoencoders into the literature.

A general scheme of autoencoders (Figure is taken from[1])

Introduction

Autoencoders…


Convolutional Neural Networks or ConvNets or even in shorter CNNs are a family of neural networks that are commonly implemented in computer vision tasks, however the use cases are not limited to that. …


ResNet owes its name to its residual blocks with skip connections that enable the model to be extremely deep. …


InceptionV1 or with a more remarkable name GoogLeNet is one of the most successful models of the earlier years of convolutional neural networks. Szegedy et al. from Google Inc. published the model in their paper named Going Deeper with Convolutions[1] and won ILSVRC-2014 with a large margin. …


VGG owes its name to the Visual Geometry Group of Oxford University. After being submitted to ILSVRC in 2014[1], the model VGGNet became as popular as the group itself. It is mostly considered as one step further from AlexNet due to deeper architecture and smaller kernel sizes.

VGGNet Architecture (Illustration is taken from [2].)

For the previous…


AlexNet is an important milestone in the visual recognition tasks in terms of available hardware utilization and several architectural choices. After its publication in 2012 by Alex Krizhevsky et al.[1] the popularity of deep learning and, in specific, the popularity of CNNs grew drastically. …


LeNet is considered to be the ancestor of convolutional neural networks and is a well-known model among the computer vision community.

LeNet for Digit Recognition
LeNet for Digit Recognition

Introduction

LeNet is one of the most fundamental deep learning models that is primarily used to classify handwritten digits. Proposed by Yann LeCun[1] in 1989, LeNet is one of the…

mrgrhn

Boğaziçi Üniversitesi ’20 Electrical & Electronics Engineering — Physics | Articles on various Deep Learning topics

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