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种子名称： Neural Networks for Machine Learning
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文件数目： 78个文件
文件大小： 919.67 MB
收录时间： 2013-12-8 21:13
已经下载： 3次
资源热度： 213
最近下载： 2024-11-20 00:50

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Neural Networks for Machine Learning.torrent

0101 Why do we need machine learning_.mp415.05MB

0102 What are neural networks_.mp49.76MB

0103 Some simple models of neurons.mp49.26MB

0104 A simple example of learning.mp46.57MB

0105 Three types of learning.mp48.96MB

0201 Types of neural network architectures.mp48.78MB

0202 Perceptrons_ The first generation of neural networks.mp49.78MB

0203 A geometrical view of perceptrons.mp47.32MB

0204 Why the learning works.mp45.9MB

0205 What perceptrons can_t do.mp416.57MB

0301 Learning the weights of a linear neuron.mp413.52MB

0302 The error surface for a linear neuron.mp45.89MB

0303 Learning the weights of a logistic output neuron.mp44.37MB

0304 The backpropagation algorithm.mp413.35MB

0305 Using the derivatives computed by backpropagation.mp411.15MB

0401 Learning to predict the next word.mp414.28MB

0402 A brief diversion into cognitive science.mp45.31MB

0403 Another diversion_ The softmax output function.mp48.03MB

0404 Neuro-probabilistic language models.mp48.93MB

0405 Ways to deal with the large number of possible outputs.mp414.26MB

0501 Why object recognition is difficult.mp45.37MB

0502 Achieving viewpoint invariance.mp46.89MB

0503 Convolutional nets for digit recognition.mp418.46MB

0504 Convolutional nets for object recognition.mp423.03MB

0601 Overview of mini-batch gradient descent.mp49.6MB

0602 A bag of tricks for mini-batch gradient descent.mp414.9MB

0603 The momentum method.mp49.74MB

0604 Adaptive learning rates for each connection.mp46.63MB

0605 Rmsprop_ Divide the gradient by a running average of its recent magnitude.mp415.12MB

0701 Modeling sequences_ A brief overview.mp420.13MB

0702 Training RNNs with back propagation.mp47.33MB

0703 A toy example of training an RNN.mp47.24MB

0704 Why it is difficult to train an RNN.mp48.89MB

0705 Long-term Short-term-memory.mp410.23MB

0801 A brief overview of Hessian Free optimization.mp416.24MB

0802 Modeling character strings with multiplicative connections.mp416.56MB

0803 Learning to predict the next character using HF.mp413.92MB

0804 Echo State Networks.mp411.28MB

0901 Overview of ways to improve generalization.mp413.57MB

0902 Limiting the size of the weights.mp47.36MB

0903 Using noise as a regularizer.mp48.48MB

0904 Introduction to the full Bayesian approach.mp412MB

0905 The Bayesian interpretation of weight decay.mp412.27MB

0906 MacKay_s quick and dirty method of setting weight costs.mp44.37MB

1001 Why it helps to combine models.mp415.12MB

1002 Mixtures of Experts.mp414.98MB

1003 The idea of full Bayesian learning.mp48.39MB

1004 Making full Bayesian learning practical.mp48.13MB

1005 Dropout.mp49.69MB

1101 Hopfield Nets.mp414.65MB

1102 Dealing with spurious minima.mp412.77MB

1103 Hopfield nets with hidden units.mp411.31MB

1104 Using stochastic units to improv search.mp411.76MB

1105 How a Boltzmann machine models data.mp413.28MB

1201 Boltzmann machine learning.mp414.03MB

1202 OPTIONAL VIDEO_ More efficient ways to get the statistics.mp416.93MB

1203 Restricted Boltzmann Machines.mp412.68MB

1204 An example of RBM learning.mp48.71MB

1205 RBMs for collaborative filtering.mp49.53MB

1301 The ups and downs of back propagation.mp411.83MB

1302 Belief Nets.mp414.86MB

1303 Learning sigmoid belief nets.mp414.19MB

1304 The wake-sleep algorithm.mp415.68MB

1401 Learning layers of features by stacking RBMs.mp420.07MB

1402 Discriminative learning for DBNs.mp411.29MB

1403 What happens during discriminative fine-tuning_.mp410.17MB

1404 Modeling real-valued data with an RBM.mp411.2MB

1405 OPTIONAL VIDEO_ RBMs are infinite sigmoid belief nets.mp419.44MB

1501 From PCA to autoencoders.mp49.68MB

1502 Deep auto encoders.mp44.92MB

1503 Deep auto encoders for document retrieval.mp410.25MB

1504 Semantic Hashing.mp410.97MB

1505 Learning binary codes for image retrieval.mp411.51MB

1506 Shallow autoencoders for pre-training.mp48.25MB

1601 OPTIONAL_ Learning a joint model of images and captions.mp413.83MB

1602 OPTIONAL_ Hierarchical Coordinate Frames.mp411.16MB

1603 OPTIONAL_ Bayesian optimization of hyper-parameters.mp415.8MB

1604 OPTIONAL_ The fog of progress.mp42.78MB