Podcast #128: We chat with Kent C Dodds about why he loves React and discuss what life was like in the dark days before Git. Listen now.

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You can look at the paper Gradient-Based Learning Applied to Document Recognition (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.


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Is the image taken from a constant distance? If yes, you'd need to scale the images to the same dimensions first of all. For few images say 100-500 images (more the better) you'd need to label the dataset by proper scaling. Once labeled, use it to train a CNN (Although best would be training a ResNet). Once trained with decent accuracy, test it for the ...


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If you have stero pairs, and you can identify the objects in the scene, you do not need a neural network, you can just use triangulation. If you need to identify which objects in the scene are the same, you have an image segmentation problem. Depending on your problem and the amount of data you have access to, you may be able to use simple techniques like ...


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Chapter 9 of the book Deep Learning (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, which first introduces the mathematical operation convolution and then describes ...


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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 ...


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Chris Olah's work is always inspired, and not too technical as one would expect. He has several papers on CNNs on his website. In particular, check the series titled "Convolutional Neural Networks" with four papers on the topic.


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Here are two review articles: Elsken, Metzen, Hutter: Neural Architecture Search: A Survey (2019), Journal of Machine Learning Research 20, 1-21 He, Zhao, Chu: AutoML: A Survey of the State-of-the-Art (2019)


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MOEAs sounds very cool, but I feel that you can't really talk about conflict in AI without discussing generative adversarial networks (GANs), which have been shown to have amazing performance by training a model to say detect in-between pictures of cats and dogs and an adversarial network being trained to create pictures to attempt to trick the training ...


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The following articles Ising models for networks of real neurons (2006) by Gasper Tkacik et al. Deep neural networks for direct, featureless learning through observation: The case of two-dimensional spin models (2018) by Kyle Mills et al. Inverse Ising inference by combining Ornstein-Zernike theory with deep learning (2017) by Soma Turi, Alpha A. Lee et al. ...


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There are multi-objective optimization problems, where the objective functions may be in conflict with each other, which can potentially have multiple Pareto-optimal solutions. The paper Multi-objective optimization using genetic algorithms: A tutorial (2006) gives a good overview of the multi-objective optimization problem with genetic algorithms, which can ...


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I'll recommend two sources: The venerable Russell & Norvig book, which is a common text in AI courses. Russell & Norvig end each chapter with a summary of the history of the developments of the techniques they have just discussed. These sections are often skipped by novice readers, but are almost exactly what you are looking for. The ones in the ...


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