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Training RNNs with back propagation
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      Why do we need machine learning [Neural Networks for Machine Learn
      [2] What are neural n...
      1278播放
      08:30
      What are neural networks- [8 min]
      [3] Some simple model...
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      08:24
      Some simple models of neurons [8 min]
      [4] A simple example ...
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      A simple example of learning [6 min]
      [5] Three types of le...
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      Three types of learning [8 min]
      [6] Types of neural n...
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      Types of neural network architectures [7 min]
      [7] Perceptrons The f...
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      Perceptrons The first generation of neural networks [8 min]
      [8] A geometrical vie...
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      A geometrical view of perceptrons [6 min]
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      Why the learning works [5 min]
      [10] What perceptrons ...
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      What perceptrons cant do [15 min]
      [11] Learning the weig...
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      Learning the weights of a linear neuron [12 min]
      [12] The error surface...
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      05:03
      The error surface for a linear neuron [5 min]
      [13] Learning the weig...
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      03:57
      Learning the weights of a logistic output neuron [4 min]
      [14] The backpropagati...
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      The backpropagation algorithm [12 min]
      [15] Using the derivat...
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      Using the derivatives computed by backpropagation [10 min]
      [16] Learning to predi...
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      Learning to predict the next word [13 min]
      [17] A brief diversion...
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      04:27
      A brief diversion into cognitive science [4 min]
      [18] Another diversion...
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      Another diversion The softmax output function [7 min]
      [19] Neuro-probabilist...
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      Neuro-probabilistic language models [8 min]
      [20] Ways to deal with...
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      Ways to deal with the large number of possible outputs [15 min]
      [21] Why object recogn...
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      Why object recognition is difficult [5 min]
      [22] Achieving viewpoi...
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      Achieving viewpoint invariance [6 min]
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      16:02
      Convolutional nets for digit recognition [16 min]
      [24] Convolutional net...
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      Convolutional nets for object recognition [17min]
      [25] Overview of mini-...
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      08:23
      Overview of mini-batch gradient descent
      [26] A bag of tricks f...
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      13:16
      A bag of tricks for mini-batch gradient descent
      [27] The momentum meth...
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      08:43
      The momentum method
      [28] Adaptive learning...
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      05:44
      Adaptive learning rates for each connection
      [30] Modeling sequence...
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      17:24
      Modeling sequences A brief overview
      [31] Training RNNs wit...
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      待播放
      Training RNNs with back propagation
      [32] A toy example of ...
      1503播放
      06:15
      A toy example of training an RNN
      [33] Why it is difficu...
      1359播放
      07:44
      Why it is difficult to train an RNN
      [34] Long-term Short-t...
      1079播放
      09:16
      Long-term Short-term-memory
      [35] A brief overview ...
      1485播放
      14:26
      A brief overview of Hessian Free optimization
      [37] Learning to predi...
      670播放
      12:25
      Learning to predict the next character using HF [12 mins]
      [38] Echo State Networ...
      753播放
      09:38
      Echo State Networks [9 min]
      [39] Overview of ways ...
      940播放
      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 ...
      804播放
      07:32
      Using noise as a regularizer [7 min]
      [42] Introduction to t...
      626播放
      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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      03:32
      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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      13:16
      Mixtures of Experts [13 min]
      [47] The idea of full ...
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      07:28
      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]
      [50] Hopfield Nets [13...
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      Hopfield Nets [13 min]
      [51] Dealing with spur...
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      Dealing with spurious minima [11 min]
      [52] Hopfield nets wit...
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      Hopfield nets with hidden units [10 min]
      [53] Using stochastic ...
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      Using stochastic units to improv search [11 min]
      [54] How a Boltzmann m...
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      How a Boltzmann machine models data [12 min]
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      Boltzmann machine learning [12 min]
      [57] Restricted Boltzm...
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      Restricted Boltzmann Machines [11 min]
      [58] An example of RBM...
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      An example of RBM learning [7 mins]
      [59] RBMs for collabor...
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      RBMs for collaborative filtering [8 mins]
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