What is a Recurrent Neural Network?What is an artificial neural network?What can be considered a deep...
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What is a Recurrent Neural Network?
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$begingroup$
Surprisingly this wasn't asked before - at least I didn't find anything besides some vaguely related questions.
So, what is a recurrent neural network, and what are their advantages over regular NNs?
recurrent-neural-networks
asked 3 hours ago
NetHackerNetHacker
1677
$endgroup$
add a comment |
$begingroup$
Surprisingly this wasn't asked before - at least I didn't find anything besides some vaguely related questions.
So, what is a recurrent neural network, and what are their advantages over regular NNs?
recurrent-neural-networks
asked 3 hours ago
NetHackerNetHacker
1677
$endgroup$
$begingroup$
In the 1990s Mark W. Tilden has introduced the first BEAM robotics walker. The system is based on the nv-neuron which is an oscillating neural network. Tilden has called the concept bicores, but it's the same like a recurrent neural network. Explaining the inner working in a few sentences is a bit complicated. The more easier way to introduce the technology is an autonomous boolean network. This logic gate network contains of a feedback loop which means the system is oscillating. In contrast to a boolean logic gate, a recurrent neural network has more features and can be trained by algorithms.
$endgroup$
– Manuel Rodriguez
1 hour ago
add a comment |
$begingroup$
Surprisingly this wasn't asked before - at least I didn't find anything besides some vaguely related questions.
So, what is a recurrent neural network, and what are their advantages over regular NNs?
recurrent-neural-networks
asked 3 hours ago
NetHackerNetHacker
1677
$endgroup$
Surprisingly this wasn't asked before - at least I didn't find anything besides some vaguely related questions.
So, what is a recurrent neural network, and what are their advantages over regular NNs?
recurrent-neural-networks
recurrent-neural-networks
asked 3 hours ago
NetHackerNetHacker
1677
asked 3 hours ago
NetHackerNetHacker
1677
asked 3 hours ago
NetHackerNetHacker
1677
asked 3 hours ago
NetHackerNetHacker
1677
asked 3 hours ago
NetHackerNetHacker
1677
1677
$begingroup$
In the 1990s Mark W. Tilden has introduced the first BEAM robotics walker. The system is based on the nv-neuron which is an oscillating neural network. Tilden has called the concept bicores, but it's the same like a recurrent neural network. Explaining the inner working in a few sentences is a bit complicated. The more easier way to introduce the technology is an autonomous boolean network. This logic gate network contains of a feedback loop which means the system is oscillating. In contrast to a boolean logic gate, a recurrent neural network has more features and can be trained by algorithms.
$endgroup$
– Manuel Rodriguez
1 hour ago
add a comment |
$begingroup$
In the 1990s Mark W. Tilden has introduced the first BEAM robotics walker. The system is based on the nv-neuron which is an oscillating neural network. Tilden has called the concept bicores, but it's the same like a recurrent neural network. Explaining the inner working in a few sentences is a bit complicated. The more easier way to introduce the technology is an autonomous boolean network. This logic gate network contains of a feedback loop which means the system is oscillating. In contrast to a boolean logic gate, a recurrent neural network has more features and can be trained by algorithms.
$endgroup$
– Manuel Rodriguez
1 hour ago
$begingroup$
In the 1990s Mark W. Tilden has introduced the first BEAM robotics walker. The system is based on the nv-neuron which is an oscillating neural network. Tilden has called the concept bicores, but it's the same like a recurrent neural network. Explaining the inner working in a few sentences is a bit complicated. The more easier way to introduce the technology is an autonomous boolean network. This logic gate network contains of a feedback loop which means the system is oscillating. In contrast to a boolean logic gate, a recurrent neural network has more features and can be trained by algorithms.
$endgroup$
– Manuel Rodriguez
1 hour ago
$begingroup$
In the 1990s Mark W. Tilden has introduced the first BEAM robotics walker. The system is based on the nv-neuron which is an oscillating neural network. Tilden has called the concept bicores, but it's the same like a recurrent neural network. Explaining the inner working in a few sentences is a bit complicated. The more easier way to introduce the technology is an autonomous boolean network. This logic gate network contains of a feedback loop which means the system is oscillating. In contrast to a boolean logic gate, a recurrent neural network has more features and can be trained by algorithms.
$endgroup$
– Manuel Rodriguez
1 hour ago
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
A recurrent neural network (RNN) is an artificial neural network that contains backward or self-connections, as opposed to just having forward connections, like in a feed-forward neural network (FFNN). The adjective "recurrent" thus refers to this backward or self-connections, which create loops in these networks.
An RNN can be trained using back-propagation through time (BBTT), such that these backward or self-connections "memorise" previously seen inputs. Hence, these connections are mainly used to track temporal relations between elements of a sequence of inputs, which makes RNNs well suited to sequence prediction and similar tasks.
There are several RNN models: for example, RNNs with LSTM or GRU units. LSTM (or GRU) is an RNN whose single units perform a more complex transformation than a unit in a "plain RNN", which performs a linear transformation of the input followed by the application of a non-linear function (e.g. ReLU) to this linear transformation. In theory, "plain RNN" are as powerful as RNNs with LSTM units. In practice, they suffer from the "vanishing and exploding gradients" problem. Hence, in practice, LSTMs (or similar sophisticated recurrent units) are used.
answered 2 hours ago
nbronbro
2,5781726
$endgroup$
add a comment |
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1 Answer
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$begingroup$
A recurrent neural network (RNN) is an artificial neural network that contains backward or self-connections, as opposed to just having forward connections, like in a feed-forward neural network (FFNN). The adjective "recurrent" thus refers to this backward or self-connections, which create loops in these networks.
An RNN can be trained using back-propagation through time (BBTT), such that these backward or self-connections "memorise" previously seen inputs. Hence, these connections are mainly used to track temporal relations between elements of a sequence of inputs, which makes RNNs well suited to sequence prediction and similar tasks.
There are several RNN models: for example, RNNs with LSTM or GRU units. LSTM (or GRU) is an RNN whose single units perform a more complex transformation than a unit in a "plain RNN", which performs a linear transformation of the input followed by the application of a non-linear function (e.g. ReLU) to this linear transformation. In theory, "plain RNN" are as powerful as RNNs with LSTM units. In practice, they suffer from the "vanishing and exploding gradients" problem. Hence, in practice, LSTMs (or similar sophisticated recurrent units) are used.
answered 2 hours ago
nbronbro
2,5781726
$endgroup$
add a comment |
$begingroup$
A recurrent neural network (RNN) is an artificial neural network that contains backward or self-connections, as opposed to just having forward connections, like in a feed-forward neural network (FFNN). The adjective "recurrent" thus refers to this backward or self-connections, which create loops in these networks.
An RNN can be trained using back-propagation through time (BBTT), such that these backward or self-connections "memorise" previously seen inputs. Hence, these connections are mainly used to track temporal relations between elements of a sequence of inputs, which makes RNNs well suited to sequence prediction and similar tasks.
There are several RNN models: for example, RNNs with LSTM or GRU units. LSTM (or GRU) is an RNN whose single units perform a more complex transformation than a unit in a "plain RNN", which performs a linear transformation of the input followed by the application of a non-linear function (e.g. ReLU) to this linear transformation. In theory, "plain RNN" are as powerful as RNNs with LSTM units. In practice, they suffer from the "vanishing and exploding gradients" problem. Hence, in practice, LSTMs (or similar sophisticated recurrent units) are used.
answered 2 hours ago
nbronbro
2,5781726
$endgroup$
add a comment |
$begingroup$
A recurrent neural network (RNN) is an artificial neural network that contains backward or self-connections, as opposed to just having forward connections, like in a feed-forward neural network (FFNN). The adjective "recurrent" thus refers to this backward or self-connections, which create loops in these networks.
An RNN can be trained using back-propagation through time (BBTT), such that these backward or self-connections "memorise" previously seen inputs. Hence, these connections are mainly used to track temporal relations between elements of a sequence of inputs, which makes RNNs well suited to sequence prediction and similar tasks.
There are several RNN models: for example, RNNs with LSTM or GRU units. LSTM (or GRU) is an RNN whose single units perform a more complex transformation than a unit in a "plain RNN", which performs a linear transformation of the input followed by the application of a non-linear function (e.g. ReLU) to this linear transformation. In theory, "plain RNN" are as powerful as RNNs with LSTM units. In practice, they suffer from the "vanishing and exploding gradients" problem. Hence, in practice, LSTMs (or similar sophisticated recurrent units) are used.
answered 2 hours ago
nbronbro
2,5781726
$endgroup$
A recurrent neural network (RNN) is an artificial neural network that contains backward or self-connections, as opposed to just having forward connections, like in a feed-forward neural network (FFNN). The adjective "recurrent" thus refers to this backward or self-connections, which create loops in these networks.
An RNN can be trained using back-propagation through time (BBTT), such that these backward or self-connections "memorise" previously seen inputs. Hence, these connections are mainly used to track temporal relations between elements of a sequence of inputs, which makes RNNs well suited to sequence prediction and similar tasks.
There are several RNN models: for example, RNNs with LSTM or GRU units. LSTM (or GRU) is an RNN whose single units perform a more complex transformation than a unit in a "plain RNN", which performs a linear transformation of the input followed by the application of a non-linear function (e.g. ReLU) to this linear transformation. In theory, "plain RNN" are as powerful as RNNs with LSTM units. In practice, they suffer from the "vanishing and exploding gradients" problem. Hence, in practice, LSTMs (or similar sophisticated recurrent units) are used.
answered 2 hours ago
nbronbro
2,5781726
answered 2 hours ago
nbronbro
2,5781726
answered 2 hours ago
nbronbro
2,5781726
answered 2 hours ago
nbronbro
2,5781726
2,5781726
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$begingroup$
In the 1990s Mark W. Tilden has introduced the first BEAM robotics walker. The system is based on the nv-neuron which is an oscillating neural network. Tilden has called the concept bicores, but it's the same like a recurrent neural network. Explaining the inner working in a few sentences is a bit complicated. The more easier way to introduce the technology is an autonomous boolean network. This logic gate network contains of a feedback loop which means the system is oscillating. In contrast to a boolean logic gate, a recurrent neural network has more features and can be trained by algorithms.
$endgroup$
– Manuel Rodriguez
1 hour ago