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Neural Networks for Machine Learning [2012, ENG]

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Neural Networks for Machine Learning [2012, ENG]

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Neural Networks for Machine Learning [2012, ENG]
▼ show more 228 files
 0101 Why do we need machine learning_.mp4
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15.05 MB
0101 Why do we need machine learning_.srt
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18.34 KB
0102 What are neural networks_.mp4
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9.76 MB
0102 What are neural networks_.srt
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11.51 KB
0103 Some simple models of neurons.mp4
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9.26 MB
0103 Some simple models of neurons.srt
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10.7 KB
0104 A simple example of learning.mp4
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6.57 MB
0104 A simple example of learning.srt
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7.02 KB
0105 Three types of learning.mp4
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8.96 MB
0105 Three types of learning.srt
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10.39 KB
0201 Types of neural network architectures.mp4
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8.78 MB
0201 Types of neural network architectures.srt
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9.85 KB
0202 Perceptrons_ The first generation of neural networks.mp4
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9.78 MB
0202 Perceptrons_ The first generation of neural networks.srt
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10.86 KB
0203 A geometrical view of perceptrons.mp4
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7.32 MB
0203 A geometrical view of perceptrons.srt
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8.29 KB
0204 Why the learning works.mp4
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5.9 MB
0204 Why the learning works.srt
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6.4 KB
0205 What perceptrons can_t do.mp4
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16.57 MB
0205 What perceptrons can_t do.srt
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18.5 KB
0301 Learning the weights of a linear neuron.mp4
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13.52 MB
0301 Learning the weights of a linear neuron.srt
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15.09 KB
0302 The error surface for a linear neuron.mp4
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5.89 MB
0302 The error surface for a linear neuron.srt
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6.3 KB
0303 Learning the weights of a logistic output neuron.mp4
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4.37 MB
0303 Learning the weights of a logistic output neuron.srt
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4.46 KB
0304 The backpropagation algorithm.mp4
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13.35 MB
0304 The backpropagation algorithm.srt
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14.87 KB
0305 Using the derivatives computed by backpropagation.mp4
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11.15 MB
0305 Using the derivatives computed by backpropagation.srt
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13.58 KB
0401 Learning to predict the next word.mp4
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14.28 MB
0401 Learning to predict the next word.srt
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16.48 KB
0402 A brief diversion into cognitive science.mp4
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5.31 MB
0402 A brief diversion into cognitive science.srt
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5.76 KB
0403 Another diversion_ The softmax output function.mp4
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8.03 MB
0403 Another diversion_ The softmax output function.srt
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9.07 KB
0404 Neuro-probabilistic language models.mp4
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8.93 MB
0404 Neuro-probabilistic language models.srt
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10.71 KB
0405 Ways to deal with the large number of possible outputs.mp4
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14.26 MB
0405 Ways to deal with the large number of possible outputs.srt
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18.12 KB
0501 Why object recognition is difficult.mp4
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5.37 MB
0501 Why object recognition is difficult.srt
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6.16 KB
0502 Achieving viewpoint invariance.mp4
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6.89 MB
0502 Achieving viewpoint invariance.srt
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8.11 KB
0503 Convolutional nets for digit recognition.mp4
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18.46 MB
0503 Convolutional nets for digit recognition.srt
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21.54 KB
0504 Convolutional nets for object recognition.mp4
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23.03 MB
0504 Convolutional nets for object recognition.srt
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25.63 KB
0601 Overview of mini-batch gradient descent.mp4
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9.6 MB
0601 Overview of mini-batch gradient descent.srt
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11.95 KB
0602 A bag of tricks for mini-batch gradient descent.mp4
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14.9 MB
0602 A bag of tricks for mini-batch gradient descent.srt
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18.77 KB
0603 The momentum method.mp4
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9.74 MB
0603 The momentum method.srt
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11.14 KB
0604 Adaptive learning rates for each connection.mp4
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6.63 MB
0604 Adaptive learning rates for each connection.srt
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0605 Rmsprop_ Divide the gradient by a running average of its recent magnitude.mp4
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15.12 MB
0605 Rmsprop_ Divide the gradient by a running average of its recent magnitude.srt
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0701 Modeling sequences_ A brief overview.mp4
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20.13 MB
0701 Modeling sequences_ A brief overview.srt
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0702 Training RNNs with back propagation.mp4
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7.33 MB
0702 Training RNNs with back propagation.srt
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0703 A toy example of training an RNN.mp4
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7.24 MB
0703 A toy example of training an RNN.srt
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0704 Why it is difficult to train an RNN.mp4
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8.89 MB
0704 Why it is difficult to train an RNN.srt
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9.79 KB
0705 Long-term Short-term-memory.mp4
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10.23 MB
0705 Long-term Short-term-memory.srt
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11.62 KB
0801 A brief overview of Hessian Free optimization.mp4
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16.24 MB
0801 A brief overview of Hessian Free optimization.srt
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17.95 KB
0802 Modeling character strings with multiplicative connections.mp4
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16.56 MB
0802 Modeling character strings with multiplicative connections.srt
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17.49 KB
0803 Learning to predict the next character using HF.mp4
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13.92 MB
0803 Learning to predict the next character using HF.srt
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0804 Echo State Networks.mp4
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11.28 MB
0804 Echo State Networks.srt
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0901 Overview of ways to improve generalization.mp4
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13.57 MB
0901 Overview of ways to improve generalization.srt
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0902 Limiting the size of the weights.mp4
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7.36 MB
0902 Limiting the size of the weights.srt
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0903 Using noise as a regularizer.mp4
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8.48 MB
0903 Using noise as a regularizer.srt
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0904 Introduction to the full Bayesian approach.mp4
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12 MB
0904 Introduction to the full Bayesian approach.srt
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0905 The Bayesian interpretation of weight decay.mp4
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12.27 MB
0905 The Bayesian interpretation of weight decay.srt
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0906 MacKay_s quick and dirty method of setting weight costs.mp4
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0906 MacKay_s quick and dirty method of setting weight costs.srt
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1001 Why it helps to combine models.mp4
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1001 Why it helps to combine models.srt
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1002 Mixtures of Experts.mp4
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1002 Mixtures of Experts.srt
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1003 The idea of full Bayesian learning.mp4
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1003 The idea of full Bayesian learning.srt
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1004 Making full Bayesian learning practical.mp4
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1004 Making full Bayesian learning practical.srt
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1005 Dropout.mp4
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1005 Dropout.srt
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1101 Hopfield Nets.mp4
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1101 Hopfield Nets.srt
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1102 Dealing with spurious minima.mp4
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1102 Dealing with spurious minima.srt
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1103 Hopfield nets with hidden units.mp4
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1103 Hopfield nets with hidden units.srt
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1104 Using stochastic units to improv search.mp4
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11.76 MB
1104 Using stochastic units to improv search.srt
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1105 How a Boltzmann machine models data.mp4
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1105 How a Boltzmann machine models data.srt
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1201 Boltzmann machine learning.mp4
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1201 Boltzmann machine learning.srt
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1202 OPTIONAL VIDEO_ More efficient ways to get the statistics.mp4
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1202 OPTIONAL VIDEO_ More efficient ways to get the statistics.srt
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1203 Restricted Boltzmann Machines.mp4
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12.68 MB
1203 Restricted Boltzmann Machines.srt
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1204 An example of RBM learning.mp4
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8.71 MB
1204 An example of RBM learning.srt
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1205 RBMs for collaborative filtering.mp4
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9.53 MB
1205 RBMs for collaborative filtering.srt
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1301 The ups and downs of back propagation.mp4
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11.83 MB
1301 The ups and downs of back propagation.srt
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1302 Belief Nets.mp4
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14.86 MB
1302 Belief Nets.srt
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1303 Learning sigmoid belief nets.mp4
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1303 Learning sigmoid belief nets.srt
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1304 The wake-sleep algorithm.mp4
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15.68 MB
1304 The wake-sleep algorithm.srt
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1401 Learning layers of features by stacking RBMs.mp4
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20.07 MB
1401 Learning layers of features by stacking RBMs.srt
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1402 Discriminative learning for DBNs.mp4
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1402 Discriminative learning for DBNs.srt
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1403 What happens during discriminative fine-tuning_.mp4
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10.17 MB
1403 What happens during discriminative fine-tuning_.srt
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1404 Modeling real-valued data with an RBM.mp4
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11.2 MB
1404 Modeling real-valued data with an RBM.srt
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1405 OPTIONAL VIDEO_ RBMs are infinite sigmoid belief nets.mp4
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19.44 MB
1405 OPTIONAL VIDEO_ RBMs are infinite sigmoid belief nets.srt
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1501 From PCA to autoencoders.mp4
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9.68 MB
1501 From PCA to autoencoders.srt
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1502 Deep auto encoders.mp4
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4.92 MB
1502 Deep auto encoders.srt
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1503 Deep auto encoders for document retrieval.mp4
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10.25 MB
1503 Deep auto encoders for document retrieval.srt
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1504 Semantic Hashing.mp4
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10.97 MB
1504 Semantic Hashing.srt
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1505 Learning binary codes for image retrieval.mp4
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11.51 MB
1505 Learning binary codes for image retrieval.srt
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1506 Shallow autoencoders for pre-training.mp4
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8.25 MB
1506 Shallow autoencoders for pre-training.srt
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1601 OPTIONAL_ Learning a joint model of images and captions.mp4
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13.83 MB
1601 OPTIONAL_ Learning a joint model of images and captions.srt
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1602 OPTIONAL_ Hierarchical Coordinate Frames.mp4
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11.16 MB
1602 OPTIONAL_ Hierarchical Coordinate Frames.srt
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1603 OPTIONAL_ Bayesian optimization of hyper-parameters.mp4
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1603 OPTIONAL_ Bayesian optimization of hyper-parameters.srt
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1604 OPTIONAL_ The fog of progress.mp4
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1604 OPTIONAL_ The fog of progress.srt
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Info
0304 reading_list-Learning representations by back-propagating errors.pdf
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2.95 MB
0404 reading_list-Neural probabilisic language models.pdf
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136.81 KB
0405 images-Lecture4-turian.png
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150.8 KB
0504 reading_list-Convolutional networks for images, speech, and time series.pdf
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122.42 KB
0504 reading_list-Gradient-based learning applied to document recognition.pdf
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932.67 KB
0705 reading_list-A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks.pdf
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313.08 KB
0803 reading_list-Generating Text with Recurrent Neural Networks.pdf
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266.99 KB
0804 Echo state network - Scholarpedia.htm
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0804 Echo state network - Scholarpedia_files
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1002 reading_list-Adaptive mixtures of local experts.pdf
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264.76 KB
1005 reading_list-Improving neural networks by preventing co-adaptation of feature detectors.pdf
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1.59 MB
1105 Boltzmann machine - Scholarpedia.htm
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289 KB
1105 Boltzmann machine - Scholarpedia_files
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1303 reading_list-Connectionist learning of belief networks.pdf
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2.3 MB
1304 reading_list-- algorithm for unsupervised neural networks.pdf
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255.35 KB
1401 reading_list-A fast learning algorithm for deep belief nets.pdf
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769.31 KB
1401 reading_list-Self-taught learning- transfer learning from unlabeled data.pdf
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473.52 KB
1401 reading_list-To recognize shapes, first learn to generate images.pdf
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501.86 KB
1504 reading_list-Semantic Hashing.pdf
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626.56 KB
1505 reading_list-Using Very Deep Autoencoders for Content-Based Image Retrieval.pdf
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741.42 KB
Slides
lecture_slides-lec1.pdf
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3.88 MB
lecture_slides-lec10.pdf
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lecture_slides-lec11.pdf
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lecture_slides-lec12.pdf
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lecture_slides-lec13.pdf
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lecture_slides-lec14.pdf
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lecture_slides-lec15.pdf
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lecture_slides-lec16.pdf
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lecture_slides-lec2.pdf
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lecture_slides-lec3.pdf
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lecture_slides-lec4.pdf
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lecture_slides-lec5.pdf
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lecture_slides-lec6.pdf
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534.03 KB
lecture_slides-lec7.pdf
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lecture_slides-lec8.pdf
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lecture_slides-lec9.pdf
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702.13 KB

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