Difference between revisions of "Neural Turing Machine"
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| − | [http://www.youtube.com/results?search_query= | + | |title=PRIMO.ai |
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| + | |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS | ||
| + | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
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| + | [http://www.youtube.com/results?search_query=Neural+Turing+Machine+Artificial+Intelligence+tutorial Youtube search...] | ||
| + | [http://www.google.com/search?q=Neural+Turing+machine+learning+ML+artificial+intelligence ...Google search] | ||
* [http://www.asimovinstitute.org/author/fjodorvanveen/ Neural Network Zoo | Fjodor Van Veen] | * [http://www.asimovinstitute.org/author/fjodorvanveen/ Neural Network Zoo | Fjodor Van Veen] | ||
| − | An abstraction of LSTMs and an attempt to un-black-box neural networks (and give us some insight in what is going on in there). Instead of coding a memory cell directly into a neuron, the memory is separated. It’s an attempt to combine the efficiency and permanency of regular digital storage and the efficiency and expressive power of neural networks. The idea is to have a content-addressable memory bank and a neural network that can read and write from it. The “Turing” in Neural Turing Machines comes from them being Turing complete: the ability to read and write and change state based on what it reads means it can represent anything a Universal Turing Machine can represent. Graves, Alex, Greg Wayne, and Ivo Danihelka. “Neural turing machines.” arXiv preprint arXiv:1410.5401 (2014). | + | An abstraction of LSTMs and an attempt to un-black-box neural networks (and give us some insight in what is going on in there). Instead of coding a [[memory]] cell directly into a neuron, the [[memory]] is separated. It’s an attempt to combine the efficiency and permanency of regular digital storage and the efficiency and expressive power of neural networks. The idea is to have a content-addressable [[memory]] bank and a neural network that can read and write from it. The “Turing” in Neural Turing Machines comes from them being Turing complete: the ability to read and write and change state based on what it reads means it can represent anything a Universal Turing Machine can represent. Graves, Alex, Greg Wayne, and Ivo Danihelka. “Neural turing machines.” arXiv preprint arXiv:1410.5401 (2014). |
http://www.asimovinstitute.org/wp-content/uploads/2016/09/ntm.png | http://www.asimovinstitute.org/wp-content/uploads/2016/09/ntm.png | ||
Latest revision as of 22:40, 1 March 2024
Youtube search... ...Google search
An abstraction of LSTMs and an attempt to un-black-box neural networks (and give us some insight in what is going on in there). Instead of coding a memory cell directly into a neuron, the memory is separated. It’s an attempt to combine the efficiency and permanency of regular digital storage and the efficiency and expressive power of neural networks. The idea is to have a content-addressable memory bank and a neural network that can read and write from it. The “Turing” in Neural Turing Machines comes from them being Turing complete: the ability to read and write and change state based on what it reads means it can represent anything a Universal Turing Machine can represent. Graves, Alex, Greg Wayne, and Ivo Danihelka. “Neural turing machines.” arXiv preprint arXiv:1410.5401 (2014).
