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coen.needell.org | ||
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www.kdnuggets.com
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| | | | | This blog post provides a tutorial on constructing a convolutional neural network for image classification in PyTorch, leveraging convolutional and pooling layers for feature extraction as well as fully-connected layers for prediction. | |
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www.paepper.com
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| | | | | Recent advances in training deep neural networks have led to a whole bunch of impressive machine learning models which are able to tackle a very diverse range of tasks. When you are developing such a model, one of the notable downsides is that it is considered a "black-box" approach in the sense that your model learns from data you feed it, but you don't really know what is going on inside the model. | |
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swethatanamala.github.io
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| | | | | In this paper, authors proposed a new simple network architecture, the Transformer, based solely on attention mechanisms, removing convolutions and recurrences entirely. Transformer is the first transduction model relying entirely... | |
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blog.fastforwardlabs.com
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| | | This article is available as a notebook on Github. Please refer to that notebook for a more detailed discussion and code fixes and updates. Despite all the recent excitement around deep learning, neural networks have a reputation among non-specialists as complicated to build and difficult to interpret. And while interpretability remains an issue, there are now high-level neural network libraries that enable developers to quickly build neural network models without worrying about the numerical details of floating point operations and linear algebra. | ||
