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313.08KB
Info/0803 reading_list-Generating Text with Recurrent Neural Networks.pdf
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Info/1505 reading_list-Using Very Deep Autoencoders for Content-Based Image Retrieval.pdf
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0101 Why do we need machine learning_.mp4
15.05MB
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0101 Why do we need machine learning_.srt
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0102 What are neural networks_.mp4
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0103 Some simple models of neurons.mp4
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0104 A simple example of learning.mp4
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0301 Learning the weights of a linear neuron.mp4
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0302 The error surface for a linear neuron.mp4
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0304 The backpropagation algorithm.mp4
13.35MB
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0304 The backpropagation algorithm.srt
14.87KB
0305 Using the derivatives computed by backpropagation.mp4
11.15MB
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0305 Using the derivatives computed by backpropagation.srt
13.58KB
0401 Learning to predict the next word.mp4
14.28MB
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0401 Learning to predict the next word.srt
16.48KB
0402 A brief diversion into cognitive science.mp4
5.31MB
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0402 A brief diversion into cognitive science.srt
5.76KB
0403 Another diversion_ The softmax output function.mp4
8.03MB
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0403 Another diversion_ The softmax output function.srt
9.07KB
0404 Neuro-probabilistic language models.mp4
8.93MB
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0404 Neuro-probabilistic language models.srt
10.71KB
0405 Ways to deal with the large number of possible outputs.mp4
14.26MB
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0405 Ways to deal with the large number of possible outputs.srt
18.12KB
0501 Why object recognition is difficult.mp4
5.37MB
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0501 Why object recognition is difficult.srt
6.16KB
0502 Achieving viewpoint invariance.mp4
6.89MB
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0502 Achieving viewpoint invariance.srt
8.11KB
0503 Convolutional nets for digit recognition.mp4
18.46MB
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0503 Convolutional nets for digit recognition.srt
21.54KB
0504 Convolutional nets for object recognition.mp4
23.03MB
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0504 Convolutional nets for object recognition.srt
25.63KB
0601 Overview of mini-batch gradient descent.mp4
9.6MB
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0601 Overview of mini-batch gradient descent.srt
11.95KB
0602 A bag of tricks for mini-batch gradient descent.mp4
14.9MB
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0602 A bag of tricks for mini-batch gradient descent.srt
18.77KB
0603 The momentum method.mp4
9.74MB
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0603 The momentum method.srt
11.14KB
0604 Adaptive learning rates for each connection.mp4
6.63MB
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0604 Adaptive learning rates for each connection.srt
7.73KB
0605 Rmsprop_ Divide the gradient by a running average of its recent magnitude.mp4
15.12MB
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0605 Rmsprop_ Divide the gradient by a running average of its recent magnitude.srt
15.69KB
0701 Modeling sequences_ A brief overview.mp4
20.13MB
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0701 Modeling sequences_ A brief overview.srt
22.65KB
0702 Training RNNs with back propagation.mp4
7.33MB
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0702 Training RNNs with back propagation.srt
8.37KB
0703 A toy example of training an RNN.mp4
7.24MB
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0703 A toy example of training an RNN.srt
7.52KB
0704 Why it is difficult to train an RNN.mp4
8.89MB
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0704 Why it is difficult to train an RNN.srt
9.79KB
0705 Long-term Short-term-memory.mp4
10.23MB
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0705 Long-term Short-term-memory.srt
11.62KB
0801 A brief overview of Hessian Free optimization.mp4
16.24MB
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0801 A brief overview of Hessian Free optimization.srt
17.95KB
0802 Modeling character strings with multiplicative connections.mp4
16.56MB
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0802 Modeling character strings with multiplicative connections.srt
17.49KB
0803 Learning to predict the next character using HF.mp4
13.92MB
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0803 Learning to predict the next character using HF.srt
15.74KB
0804 Echo State Networks.mp4
11.28MB
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0804 Echo State Networks.srt
11.98KB
0901 Overview of ways to improve generalization.mp4
13.57MB
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0901 Overview of ways to improve generalization.srt
15.8KB
0902 Limiting the size of the weights.mp4
7.36MB
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0902 Limiting the size of the weights.srt
8.41KB
0903 Using noise as a regularizer.mp4
8.48MB
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0903 Using noise as a regularizer.srt
8.87KB
0904 Introduction to the full Bayesian approach.mp4
12MB
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0904 Introduction to the full Bayesian approach.srt
13.18KB
0905 The Bayesian interpretation of weight decay.mp4
12.27MB
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0905 The Bayesian interpretation of weight decay.srt
13.02KB
0906 MacKay_s quick and dirty method of setting weight costs.mp4
4.37MB
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0906 MacKay_s quick and dirty method of setting weight costs.srt
4.41KB
1001 Why it helps to combine models.mp4
15.12MB
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1001 Why it helps to combine models.srt
17.68KB
1002 Mixtures of Experts.mp4
14.98MB
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1002 Mixtures of Experts.srt
17.06KB
1003 The idea of full Bayesian learning.mp4
8.39MB
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1003 The idea of full Bayesian learning.srt
10.28KB
1004 Making full Bayesian learning practical.mp4
8.13MB
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1004 Making full Bayesian learning practical.srt
8.46KB
1005 Dropout.mp4
9.69MB
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1101 Hopfield Nets.mp4
14.65MB
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1101 Hopfield Nets.srt
16.36KB
1102 Dealing with spurious minima.mp4
12.77MB
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1102 Dealing with spurious minima.srt
14.85KB
1103 Hopfield nets with hidden units.mp4
11.31MB
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1103 Hopfield nets with hidden units.srt
12.29KB
1104 Using stochastic units to improv search.mp4
11.76MB
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1104 Using stochastic units to improv search.srt
13.99KB
1105 How a Boltzmann machine models data.mp4
13.28MB
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1105 How a Boltzmann machine models data.srt
15.89KB
1201 Boltzmann machine learning.mp4
14.03MB
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1201 Boltzmann machine learning.srt
16.02KB
1202 OPTIONAL VIDEO_ More efficient ways to get the statistics.mp4
16.93MB
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1202 OPTIONAL VIDEO_ More efficient ways to get the statistics.srt
18.21KB
1203 Restricted Boltzmann Machines.mp4
12.68MB
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1203 Restricted Boltzmann Machines.srt
13.59KB
1204 An example of RBM learning.mp4
8.71MB
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1204 An example of RBM learning.srt
9.87KB
1205 RBMs for collaborative filtering.mp4
9.53MB
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1205 RBMs for collaborative filtering.srt
10.67KB
1301 The ups and downs of back propagation.mp4
11.83MB
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1301 The ups and downs of back propagation.srt
13.64KB
1302 Belief Nets.mp4
14.86MB
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1302 Belief Nets.srt
17.35KB
1303 Learning sigmoid belief nets.mp4
14.19MB
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1303 Learning sigmoid belief nets.srt
14.61KB
1304 The wake-sleep algorithm.mp4
15.68MB
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1304 The wake-sleep algorithm.srt
17.4KB
1401 Learning layers of features by stacking RBMs.mp4
20.07MB
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1401 Learning layers of features by stacking RBMs.srt
22.81KB
1402 Discriminative learning for DBNs.mp4
11.29MB
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1402 Discriminative learning for DBNs.srt
12.74KB
1403 What happens during discriminative fine-tuning_.mp4
10.17MB
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1403 What happens during discriminative fine-tuning_.srt
10.66KB
1404 Modeling real-valued data with an RBM.mp4
11.2MB
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1404 Modeling real-valued data with an RBM.srt
12.15KB
1405 OPTIONAL VIDEO_ RBMs are infinite sigmoid belief nets.mp4
19.44MB
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1405 OPTIONAL VIDEO_ RBMs are infinite sigmoid belief nets.srt
21.63KB
1501 From PCA to autoencoders.mp4
9.68MB
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1501 From PCA to autoencoders.srt
10.26KB
1502 Deep auto encoders.mp4
4.92MB
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1502 Deep auto encoders.srt
5.35KB
1503 Deep auto encoders for document retrieval.mp4
10.25MB
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1503 Deep auto encoders for document retrieval.srt
10.52KB
1504 Semantic Hashing.mp4
10.97MB
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1504 Semantic Hashing.srt
11.32KB
1505 Learning binary codes for image retrieval.mp4
11.51MB
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1505 Learning binary codes for image retrieval.srt
12.89KB
1506 Shallow autoencoders for pre-training.mp4
8.25MB
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1506 Shallow autoencoders for pre-training.srt
10.05KB
1601 OPTIONAL_ Learning a joint model of images and captions.mp4
13.83MB
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1601 OPTIONAL_ Learning a joint model of images and captions.srt
10.31KB
1602 OPTIONAL_ Hierarchical Coordinate Frames.mp4
11.16MB
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1602 OPTIONAL_ Hierarchical Coordinate Frames.srt
13.34KB
1603 OPTIONAL_ Bayesian optimization of hyper-parameters.mp4
15.8MB
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1603 OPTIONAL_ Bayesian optimization of hyper-parameters.srt
18.54KB
1604 OPTIONAL_ The fog of progress.mp4
2.78MB
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1604 OPTIONAL_ The fog of progress.srt
3.49KB