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  • Whats the difference between feed-forward and recurrent neural networks?
    What is the difference between a feed-forward and recurrent neural network? Why would you use one over the other? Do other network topologies exist?
  • Recurrent vs Recursive Neural Networks: Which is better for NLP?
    Recurrent Neural networks are recurring over time For example if you have a sequence x = ['h', 'e', 'l', 'l'] This sequence is fed to a single neuron which has a single connection to itself At time step 0, the letter 'h' is given as input At time step 1, 'e' is given as input The network when unfolded over time will look like this A recursive network is just a generalization of a recurrent
  • recurrent neural network - RNN vs Kalman filter : learning the . . .
    Being recently interested in Kalman filters and Recurrent neural networks, it appears to me that the two are closely related, yet I can't find relevant enough litterature : In a Kalman filter, the
  • What is the difference between a neural network and a deep neural . . .
    Let's start with a triviliaty: Deep neural network is simply a feedforward network with many hidden layers This is more or less all there is to say about the definition Neural networks can be recurrent or feedforward; feedforward ones do not have any loops in their graph and can be organized in layers If there are "many" layers, then we say that the network is deep How many layers does a
  • Hidden Markov Model vs Recurrent Neural Network
    Hidden Markov Models (HMMs) are much simpler than Recurrent Neural Networks (RNNs), and rely on strong assumptions which may not always be true If the assumptions are true then you may see better performance from an HMM since it is less finicky to get working
  • Recurrent neural networks vs. State space models
    Start asking to get answers Find the answer to your question by asking Ask question reinforcement-learning recurrent-neural-network state-space-models
  • time series - Recurrent neural networks in R - Cross Validated
    I've heard a bit about using neural networks to forecast time series, specifically recurrent neural networks I was wondering, is there a recurrent neural network package for R? I can't seem to f
  • machine learning - Recurrent Neural Network (RNN) topology: why always . . .
    EDIT: 3 years after this question was posted, NVIDIA released this paper, arXiv:1905 12340: "Rethinking Full Connectivity in Recurrent Neural Networks", showing that sparser connections are usually just as accurate and much faster than fully-connected networks The second diagram above corresponds to the "Diagonal RNN" in the arXiv paper
  • machine learning - Why do RNNs have a tendency to . . . - Cross Validated
    21 Why do recurrent neural networks (RNNs) have a tendency to suffer from vanishing exploding gradient? For what a vanishing exploding gradient is, see Pascanu, et al (2013) On the difficulty of training recurrent neural networks, section 2 (pdf)
  • machine learning - Proper way of using recurrent neural network for . . .
    The joint modeling of hidden and observed variables in large recurrent neural networks provides new prospects for planning and risk management The ensemble approach based on HCNN offers an alternative approach to forecasting of future probability distributions HCNNs give a perfect description of the dynamic of the observables in the past





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