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cprimozic.net | ||
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kavita-ganesan.com
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| | | | | This article examines the parts that make up neural networks and deep neural networks, as well as the fundamental different types of models (e.g. regression), their constituent parts (and how they contribute to model accuracy), and which tasks they are designed to learn. | |
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www.asimovinstitute.org
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| | | | | With new neural networkarchitectures popping up every now and then, its hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first. So I decided to compose a cheat sheet containingmany of thosearchitectures. Most of theseare neural networks, some are completely [] | |
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ben.bolte.cc
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| | | | | An in-depth introduction to using Keras for language modeling; word embedding, recurrent and convolutional neural networks, attentional RNNs, and similarity metrics for vector embeddings. | |
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dennybritz.com
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| | | This the thirdpart of the Recurrent Neural Network Tutorial. | ||
