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Neural Networks for Machine Learning
magnet:?xt=urn:btih:65EC8A88B2C48329BD11761A87F0EEF0BFCA60ED&dn=Neural Networks for Machine Learning
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文件列表详情
65EC8A88B2C48329BD11761A87F0EEF0BFCA60ED
infohash:
78
文件数量
919.67 MB
文件大小
2013-12-8 21:13
创建日期
2024-6-30 15:55
最后访问
相关分词
Neural
Networks
for
Machine
Learning
0101 Why do we need machine learning_.mp4 15.05 MB
0102 What are neural networks_.mp4 9.76 MB
0103 Some simple models of neurons.mp4 9.26 MB
0104 A simple example of learning.mp4 6.57 MB
0105 Three types of learning.mp4 8.96 MB
0201 Types of neural network architectures.mp4 8.78 MB
0202 Perceptrons_ The first generation of neural networks.mp4 9.78 MB
0203 A geometrical view of perceptrons.mp4 7.32 MB
0204 Why the learning works.mp4 5.9 MB
0205 What perceptrons can_t do.mp4 16.57 MB
0301 Learning the weights of a linear neuron.mp4 13.52 MB
0302 The error surface for a linear neuron.mp4 5.89 MB
0303 Learning the weights of a logistic output neuron.mp4 4.37 MB
0304 The backpropagation algorithm.mp4 13.35 MB
0305 Using the derivatives computed by backpropagation.mp4 11.15 MB
0401 Learning to predict the next word.mp4 14.28 MB
0402 A brief diversion into cognitive science.mp4 5.31 MB
0403 Another diversion_ The softmax output function.mp4 8.03 MB
0404 Neuro-probabilistic language models.mp4 8.93 MB
0405 Ways to deal with the large number of possible outputs.mp4 14.26 MB
0501 Why object recognition is difficult.mp4 5.37 MB
0502 Achieving viewpoint invariance.mp4 6.89 MB
0503 Convolutional nets for digit recognition.mp4 18.46 MB
0504 Convolutional nets for object recognition.mp4 23.03 MB
0601 Overview of mini-batch gradient descent.mp4 9.6 MB
0602 A bag of tricks for mini-batch gradient descent.mp4 14.9 MB
0603 The momentum method.mp4 9.74 MB
0604 Adaptive learning rates for each connection.mp4 6.63 MB
0605 Rmsprop_ Divide the gradient by a running average of its recent magnitude.mp4 15.12 MB
0701 Modeling sequences_ A brief overview.mp4 20.13 MB
0702 Training RNNs with back propagation.mp4 7.33 MB
0703 A toy example of training an RNN.mp4 7.24 MB
0704 Why it is difficult to train an RNN.mp4 8.89 MB
0705 Long-term Short-term-memory.mp4 10.23 MB
0801 A brief overview of Hessian Free optimization.mp4 16.24 MB
0802 Modeling character strings with multiplicative connections.mp4 16.56 MB
0803 Learning to predict the next character using HF.mp4 13.92 MB
0804 Echo State Networks.mp4 11.28 MB
0901 Overview of ways to improve generalization.mp4 13.57 MB
0902 Limiting the size of the weights.mp4 7.36 MB
0903 Using noise as a regularizer.mp4 8.48 MB
0904 Introduction to the full Bayesian approach.mp4 12 MB
0905 The Bayesian interpretation of weight decay.mp4 12.27 MB
0906 MacKay_s quick and dirty method of setting weight costs.mp4 4.37 MB
1001 Why it helps to combine models.mp4 15.12 MB
1002 Mixtures of Experts.mp4 14.98 MB
1003 The idea of full Bayesian learning.mp4 8.39 MB
1004 Making full Bayesian learning practical.mp4 8.13 MB
1005 Dropout.mp4 9.69 MB
1101 Hopfield Nets.mp4 14.65 MB
1102 Dealing with spurious minima.mp4 12.77 MB
1103 Hopfield nets with hidden units.mp4 11.31 MB
1104 Using stochastic units to improv search.mp4 11.76 MB
1105 How a Boltzmann machine models data.mp4 13.28 MB
1201 Boltzmann machine learning.mp4 14.03 MB
1202 OPTIONAL VIDEO_ More efficient ways to get the statistics.mp4 16.93 MB
1203 Restricted Boltzmann Machines.mp4 12.68 MB
1204 An example of RBM learning.mp4 8.71 MB
1205 RBMs for collaborative filtering.mp4 9.53 MB
1301 The ups and downs of back propagation.mp4 11.83 MB
1302 Belief Nets.mp4 14.86 MB
1303 Learning sigmoid belief nets.mp4 14.19 MB
1304 The wake-sleep algorithm.mp4 15.68 MB
1401 Learning layers of features by stacking RBMs.mp4 20.07 MB
1402 Discriminative learning for DBNs.mp4 11.29 MB
1403 What happens during discriminative fine-tuning_.mp4 10.17 MB
1404 Modeling real-valued data with an RBM.mp4 11.2 MB
1405 OPTIONAL VIDEO_ RBMs are infinite sigmoid belief nets.mp4 19.44 MB
1501 From PCA to autoencoders.mp4 9.68 MB
1502 Deep auto encoders.mp4 4.92 MB
1503 Deep auto encoders for document retrieval.mp4 10.25 MB
1504 Semantic Hashing.mp4 10.97 MB
1505 Learning binary codes for image retrieval.mp4 11.51 MB
1506 Shallow autoencoders for pre-training.mp4 8.25 MB
1601 OPTIONAL_ Learning a joint model of images and captions.mp4 13.83 MB
1602 OPTIONAL_ Hierarchical Coordinate Frames.mp4 11.16 MB
1603 OPTIONAL_ Bayesian optimization of hyper-parameters.mp4 15.8 MB
1604 OPTIONAL_ The fog of progress.mp4 2.78 MB
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