Answers.Body: <p><a href="https://www.deeplearningbook.org/contents/convnets.html" rel="nofollow noreferrer">Chapter 9</a> of the book <a href="https://www.deeplearningbook.org" rel="nofollow noreferrer">Deep Learning</a> (2016), by Goodfellow et al., describes the convolutional (neural) network (CNN), its main operations (namely, convolution and pooling) and properties (such as parameter sharing).</p>
  
  <p>There's also the article <a href="http://gregorygundersen.com/blog/2017/02/24/cnns/" rel="nofollow noreferrer">From Convolution to Neural Network</a>, which first introduces the mathematical operation <em>convolution</em> and then describes its connection with signal processing (where images can be viewed as 2D signals) and, finally, describes the CNN.</p>
  
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Answers.Body: <p>Chris Olah's work is always inspired, and not too technical as one would expect. He has several papers on CNNs on his <a href="https://colah.github.io/" rel="nofollow noreferrer">website</a>. In particular, check the series titled "Convolutional Neural Networks" with four papers on the topic.</p>
  
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Answers.Body: <p>You can look at the paper <a href="http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf" rel="nofollow noreferrer">Gradient-Based Learning Applied to Document
  Recognition</a> (1998) by Yann LeCun et al., which reviews and compares various methods applied to handwritten character recognition and shows that CNNs outperform all other methods.</p>
  
  <p>Also, I suggest Andrew Ng's CNN videos.</p>
  
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Answers.Body: <p>I'm not sure if this is what you are looking for but I find Goodfellow's book a pretty good resource:</p>
  
  <p>Goodfellow, specifically Section 2, Chapter 9 deals with convolutional neural networks: <a href="https://www.deeplearningbook.org/" rel="nofollow noreferrer">https://www.deeplearningbook.org/</a></p>
  
  <p>'Pattern Recognition and Machine Learning' by Bishop Might contains a section (5.5.5, pg 267 onwards) as well as an exercise, and a general discussion about neural networks in image recognition.</p>
  
  <p>If you edit your question to post a bit more detail, we can offer better answers, for example, what is about the convolutional layer?  How it's implemented?</p>
  
  <p>If you are looking for a more basic introduction to convolutional layers I would also suggest:</p>
  
  <p><a href="https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53" rel="nofollow noreferrer">A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way</a> gives a pretty general overview, starting at the difference between CNNs and ANNs and explains why CNNs are superior to ANNs (for certain problems).  It also gives some details about how the convolution actually works.</p>
  
  <p><a href="https://towardsdatascience.com/transpose-convolution-77818e55a123" rel="nofollow noreferrer">Demystifying the transpose convolution</a> explains the transpose convolution operation in the context of how a traditional convolution; this may not be relevant if you are strictly using CNNs and not transpose-CNNs.</p>
  
  <p><a href="https://medium.com/@RaghavPrabhu/understanding-of-convolutional-neural-network-cnn-deep-learning-99760835f148" rel="nofollow noreferrer">Understanding of Convolutional Neural Network (CNN) — Deep Learning</a> is quite similar to "A Comprehensive..." link above, but it also includes information about filtering and shows the effect that different filters have on an image, which is certainly very import to an understanding of why we use CNNs.</p>
  
  <p><a href="https://towardsdatascience.com/building-a-convolutional-neural-network-cnn-in-keras-329fbbadc5f5" rel="nofollow noreferrer">Building a Convolutional Neural Network (CNN) in Keras</a> (or one of the other thousand similar pages) are pretty good for just starting out and building your own CNN classifier.  You can also check out examples from Keras, e.g. <a href="https://keras.io/examples/cifar10_cnn/" rel="nofollow noreferrer">CIFAR10 CNN</a>, but these tend to give you a very little information about <em>why</em> they designed the network the way that they did.</p>
  
  <p>If, on the other hand, you are looking for some more advanced resources, here are is one that springs to mind:</p>
  
  <p><a href="https://arxiv.org/abs/1512.03385" rel="nofollow noreferrer">Deep Residual Learning for Image Recognition</a> by He et al., deals with a major advance in image recognition, using Residual Networks (ResNet).  This type of network has become pretty popular, so I highly recommend giving it a read.</p>
  
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Answers.Text: Thanks, I thought I could only ask one question. You can add all of the details that you want to your answer without asking it explicitly.
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Answers.Text: I meant, expand on your existing question, what about convolutional layers are you interested in?
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Answers.Text: I'm interested in everything related to CNN.
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Body: <p>I'm studying a master's degree and my final work is going to be about the convolutional neural network.</p>
  
  <p>I read a lot of books and I did Convolutional Network Standford's course, but I need more.</p>
  
  <p>Are there books or papers on the details of convolutional neural networks (in particular, convolutional layer)? </p>
  
Tags: <convolutional-neural-networks><reference-request><papers><books>
Title: What are examples of books or papers on the details of convolutional neural networks?
UserDisplayName: VansFannel
ThreadText: # What are examples of books or papers on the details of convolutional neural networks?
  
  (Asked by: username_0 on 2019-11-17)
  
  I'm studying a master's degree and my final work is going to be about the convolutional neural network.
  
  I read a lot of books and I did Convolutional Network Standford's course, but I need more.
  
  Are there books or papers on the details of convolutional neural networks (in particular, convolutional layer)?
  
  **Tags:** convolutional-neural-networks, reference-request, papers, books
  
  
  ---
  
  ## 4 answers
  
  ### Answer by username_1 on 2019-11-17. Score: 0 | Accepted answer
  
  [Chapter 9](https://www.deeplearningbook.org/contents/convnets.html) of the book [Deep Learning](https://www.deeplearningbook.org) (2016), by Goodfellow et al., describes the convolutional (neural) network (CNN), its main operations (namely, convolution and pooling) and properties (such as parameter sharing).
  
  There's also the article [From Convolution to Neural Network](http://gregorygundersen.com/blog/2017/02/24/cnns/), which first introduces the mathematical operation *convolution* and then describes its connection with signal processing (where images can be viewed as 2D signals) and, finally, describes the CNN.
  
  
  ---
  
  ### Answer by username_2 on 2019-11-17. Score: 1
  
  Chris Olah's work is always inspired, and not too technical as one would expect. He has several papers on CNNs on his [website](https://colah.github.io/). In particular, check the series titled "Convolutional Neural Networks" with four papers on the topic.
  
  
  ---
  
  ### Answer by username_3 on 2019-11-19. Score: 0
  
  You can look at the paper [Gradient-Based Learning Applied to Document
  Recognition](http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf) (1998) by Yann LeCun et al., which reviews and compares various methods applied to handwritten character recognition and shows that CNNs outperform all other methods.
  
  Also, I suggest Andrew Ng's CNN videos.
  
  
  ---
  
  ### Answer by username_4 on 2019-11-17. Score: 0
  
  I'm not sure if this is what you are looking for but I find Goodfellow's book a pretty good resource:
  
  Goodfellow, specifically Section 2, Chapter 9 deals with convolutional neural networks: <https://www.deeplearningbook.org/>
  
  'Pattern Recognition and Machine Learning' by Bishop Might contains a section (5.5.5, pg 267 onwards) as well as an exercise, and a general discussion about neural networks in image recognition.
  
  If you edit your question to post a bit more detail, we can offer better answers, for example, what is about the convolutional layer? How it's implemented?
  
  If you are looking for a more basic introduction to convolutional layers I would also suggest:
  
  [A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way](https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53) gives a pretty general overview, starting at the difference between CNNs and ANNs and explains why CNNs are superior to ANNs (for certain problems). It also gives some details about how the convolution actually works.
  
  [Demystifying the transpose convolution](https://towardsdatascience.com/transpose-convolution-77818e55a123) explains the transpose convolution operation in the context of how a traditional convolution; this may not be relevant if you are strictly using CNNs and not transpose-CNNs.
  
  [Understanding of Convolutional Neural Network (CNN) — Deep Learning](https://medium.com/@RaghavPrabhu/understanding-of-convolutional-neural-network-cnn-deep-learning-99760835f148) is quite similar to "A Comprehensive..." link above, but it also includes information about filtering and shows the effect that different filters have on an image, which is certainly very import to an understanding of why we use CNNs.
  
  [Building a Convolutional Neural Network (CNN) in Keras](https://towardsdatascience.com/building-a-convolutional-neural-network-cnn-in-keras-329fbbadc5f5) (or one of the other thousand similar pages) are pretty good for just starting out and building your own CNN classifier. You can also check out examples from Keras, e.g. [CIFAR10 CNN](https://keras.io/examples/cifar10_cnn/), but these tend to give you a very little information about *why* they designed the network the way that they did.
  
  If, on the other hand, you are looking for some more advanced resources, here are is one that springs to mind:
  
  [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al., deals with a major advance in image recognition, using Residual Networks (ResNet). This type of network has become pretty popular, so I highly recommend giving it a read.
  
  > **Comments:**
  > **username_0**: Thanks, I thought I could only ask one question. You can add all of the details that you want to your answer without asking it explicitly.  
  > **username_4**: I meant, expand on your existing question, what about convolutional layers are you interested in?  
  > **username_0**: I'm interested in everything related to CNN.  
  
  ---
  
  