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Tag: CNN

#024 CNN Convolutional Operation of Sliding Windows

23.11.2018

Convolutional operation of sliding windows In the previous post we learned about the sliding windows object detection algorithm using a \(convnet \), but we saw that it was too slow. In this post we will see how to implement that algorithm convolutionaly. Let’s see what that means. To build up the convolutional implementation of sliding windows, let’s first see how we can turn \(Fully \enspace connected \) layers in our neural network into \(Convolutional\) layes.…
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017 CNN Inception Network

#017 CNN Inception Network

14.11.2018

\(Inception\enspace network \)  Motivation for the \(Inception\enspace network \): In the last post we talked about why \(1\times 1 \) convolutional layer can be useful and now we will use it for building the \(Inception\enspace network \). When designing a layer for a \(convnet \) we might have to choose between a \(1 \times 3 \) filter, a \(3 \times 3\) or \(5\times 5\) or maybe a pooling layer. An \(Inception\enspace network \) solves this…
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#010 CNN An Example of a Neural Network

08.11.2018

Convolutional Neural Network – An Example In previous posts (CNN 004, CNN 005 and CNN 009) we have defined all building blocks for building a full convolutional neural network. Let’s now look at an example of a convolutional neural network (CNN). Let’s say that we have a \(32 \times 32 \times 3 \) dimensional image as an input to the CNN. So it’s an RGB image and supoose we want to try to do a handwritten…
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001-CNN-Convolutional-Neural-Networks

#001 CNN Convolutional Neural Networks

30.10.2018

Source: Stanford CS 231n Convolutional Neural Networks What is Computer Vision?      Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do.                               Computer Vision is one of the fields of artificial intelligence that is rapidly progressing thanks to Deep…
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