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The Bayesian interpretation of weight decay [11 min]
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      [3] Some simple model...
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      [4] A simple example ...
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      [5] Three types of le...
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      [6] Types of neural n...
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      [7] Perceptrons The f...
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      [8] A geometrical vie...
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      [9] Why the learning ...
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      [10] What perceptrons ...
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      [11] Learning the weig...
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      [12] The error surface...
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      [13] Learning the weig...
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      [14] The backpropagati...
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      [15] Using the derivat...
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      [16] Learning to predi...
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      [17] A brief diversion...
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      [18] Another diversion...
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      [19] Neuro-probabilist...
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      [20] Ways to deal with...
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      [21] Why object recogn...
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      [22] Achieving viewpoi...
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      [23] Convolutional net...
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      [24] Convolutional net...
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      [25] Overview of mini-...
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      [26] A bag of tricks f...
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      [27] The momentum meth...
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      [28] Adaptive learning...
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      [30] Modeling sequence...
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      [31] Training RNNs wit...
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      [32] A toy example of ...
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      [33] Why it is difficu...
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      [34] Long-term Short-t...
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      [35] A brief overview ...
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      [37] Learning to predi...
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      [38] Echo State Networ...
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      [39] Overview of ways ...
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      [40] Limiting the size...
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      [41] Using noise as a ...
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      [42] Introduction to t...
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      [43] The Bayesian inte...
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      [45] Why it helps to c...
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      [46] Mixtures of Exper...
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      [47] The idea of full ...
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      [48] Making full Bayes...
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      [49] Dropout [9 min]
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      [50] Hopfield Nets [13...
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      [51] Dealing with spur...
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      [52] Hopfield nets wit...
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      [53] Using stochastic ...
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      [54] How a Boltzmann m...
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      [55] Boltzmann machine...
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      [57] Restricted Boltzm...
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      [58] An example of RBM...
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      [59] RBMs for collabor...
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