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Long-term Short-term-memory
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      Overview of mini-batch gradient descent
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      A bag of tricks for mini-batch gradient descent
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      The momentum method
      [28] Adaptive learning...
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      Adaptive learning rates for each connection
      [30] Modeling sequence...
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      Modeling sequences A brief overview
      [31] Training RNNs wit...
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      Training RNNs with back propagation
      [32] A toy example of ...
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      A toy example of training an RNN
      [33] Why it is difficu...
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      Why it is difficult to train an RNN
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      [35] A brief overview ...
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      A brief overview of Hessian Free optimization
      [37] Learning to predi...
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      12:25
      Learning to predict the next character using HF [12 mins]
      [38] Echo State Networ...
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      09:38
      Echo State Networks [9 min]
      [39] Overview of ways ...
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      11:45
      Overview of ways to improve generalization [12 min]
      [40] Limiting the size...
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      06:23
      Limiting the size of the weights [6 min]
      [41] Using noise as a ...
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      07:32
      Using noise as a regularizer [7 min]
      [42] Introduction to t...
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      10:50
      Introduction to the full Bayesian approach [12 min]
      [43] The Bayesian inte...
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      10:53
      The Bayesian interpretation of weight decay [11 min]
      [44] MacKays quick and...
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      MacKays quick and dirty method of setting weight costs [4 min]
      [45] Why it helps to c...
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      Why it helps to combine models [13 min]
      [46] Mixtures of Exper...
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      Mixtures of Experts [13 min]
      [47] The idea of full ...
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      The idea of full Bayesian learning [7 min]
      [48] Making full Bayes...
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      Making full Bayesian learning practical [7 min]
      [49] Dropout [9 min]
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      Dropout [9 min]
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      Hopfield Nets [13 min]
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      [52] Hopfield nets wit...
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      Hopfield nets with hidden units [10 min]
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      Restricted Boltzmann Machines [11 min]
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      An example of RBM learning [7 mins]
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      RBMs for collaborative filtering [8 mins]
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