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Pursuant to the California Public Records Act (Government Code § 6250 et seq.) "Public records" include "any writing containing information relating to the conduct of the public’s business prepared, owned, used, or retained by any state or local agency regardless of physical form or characteristics." (Cal. Gov't.
The standard LSTM architecture was introduced in 2000 by Felix Gers, Schmidhuber, and Fred Cummins. [20] Today's "vanilla LSTM" using backpropagation through time was published with his student Alex Graves in 2005, [21] [22] and its connectionist temporal classification (CTC) training algorithm [23] in 2006. CTC was applied to end-to-end speech ...
The California Department of Motor Vehicles (DMV) is the state agency that registers motor vehicles and boats and issues driver licenses in the U.S. state of California. It regulates new car dealers (through the New Motor Vehicle Board), commercial cargo carriers, private driving schools, and private traffic schools.
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A California car license plate saying ANRCHST (a vanity plate–speak form of anarchist) from 2006. The use of year-of-manufacture (YOM) plates is authorized by Section 5004.1 of the California Motor Vehicle Code. It is a law that allows vintage cars to be registered to use vintage license plates.
Long short-term memory (LSTM) [1] is a type of recurrent neural network (RNN) aimed at mitigating the vanishing gradient problem [2] commonly encountered by traditional RNNs. Its relative insensitivity to gap length is its advantage over other RNNs, hidden Markov models , and other sequence learning methods.
Connectionist temporal classification (CTC) is a type of neural network output and associated scoring function, for training recurrent neural networks (RNNs) such as LSTM networks to tackle sequence problems where the timing is variable.
Hochreiter developed the long short-term memory (LSTM) neural network architecture in his diploma thesis in 1991 leading to the main publication in 1997. [3] [4] LSTM overcomes the problem of numerical instability in training recurrent neural networks (RNNs) that prevents them from learning from long sequences (vanishing or exploding gradient).