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Neural Networks for Machine Learning
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2013-12-8 21:13
2024-12-18 22:57
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919.67 MB
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磁力链接
magnet:?xt=urn:btih:65EC8A88B2C48329BD11761A87F0EEF0BFCA60ED
迅雷链接
thunder://QUFtYWduZXQ6P3h0PXVybjpidGloOjY1RUM4QTg4QjJDNDgzMjlCRDExNzYxQTg3RjBFRUYwQkZDQTYwRURaWg==
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相关链接
Neural
Networks
for
Machine
Learning
文件列表
0101 Why do we need machine learning_.mp4
15.05MB
0102 What are neural networks_.mp4
9.76MB
0103 Some simple models of neurons.mp4
9.26MB
0104 A simple example of learning.mp4
6.57MB
0105 Three types of learning.mp4
8.96MB
0201 Types of neural network architectures.mp4
8.78MB
0202 Perceptrons_ The first generation of neural networks.mp4
9.78MB
0203 A geometrical view of perceptrons.mp4
7.32MB
0204 Why the learning works.mp4
5.9MB
0205 What perceptrons can_t do.mp4
16.57MB
0301 Learning the weights of a linear neuron.mp4
13.52MB
0302 The error surface for a linear neuron.mp4
5.89MB
0303 Learning the weights of a logistic output neuron.mp4
4.37MB
0304 The backpropagation algorithm.mp4
13.35MB
0305 Using the derivatives computed by backpropagation.mp4
11.15MB
0401 Learning to predict the next word.mp4
14.28MB
0402 A brief diversion into cognitive science.mp4
5.31MB
0403 Another diversion_ The softmax output function.mp4
8.03MB
0404 Neuro-probabilistic language models.mp4
8.93MB
0405 Ways to deal with the large number of possible outputs.mp4
14.26MB
0501 Why object recognition is difficult.mp4
5.37MB
0502 Achieving viewpoint invariance.mp4
6.89MB
0503 Convolutional nets for digit recognition.mp4
18.46MB
0504 Convolutional nets for object recognition.mp4
23.03MB
0601 Overview of mini-batch gradient descent.mp4
9.6MB
0602 A bag of tricks for mini-batch gradient descent.mp4
14.9MB
0603 The momentum method.mp4
9.74MB
0604 Adaptive learning rates for each connection.mp4
6.63MB
0605 Rmsprop_ Divide the gradient by a running average of its recent magnitude.mp4
15.12MB
0701 Modeling sequences_ A brief overview.mp4
20.13MB
0702 Training RNNs with back propagation.mp4
7.33MB
0703 A toy example of training an RNN.mp4
7.24MB
0704 Why it is difficult to train an RNN.mp4
8.89MB
0705 Long-term Short-term-memory.mp4
10.23MB
0801 A brief overview of Hessian Free optimization.mp4
16.24MB
0802 Modeling character strings with multiplicative connections.mp4
16.56MB
0803 Learning to predict the next character using HF.mp4
13.92MB
0804 Echo State Networks.mp4
11.28MB
0901 Overview of ways to improve generalization.mp4
13.57MB
0902 Limiting the size of the weights.mp4
7.36MB
0903 Using noise as a regularizer.mp4
8.48MB
0904 Introduction to the full Bayesian approach.mp4
12MB
0905 The Bayesian interpretation of weight decay.mp4
12.27MB
0906 MacKay_s quick and dirty method of setting weight costs.mp4
4.37MB
1001 Why it helps to combine models.mp4
15.12MB
1002 Mixtures of Experts.mp4
14.98MB
1003 The idea of full Bayesian learning.mp4
8.39MB
1004 Making full Bayesian learning practical.mp4
8.13MB
1005 Dropout.mp4
9.69MB
1101 Hopfield Nets.mp4
14.65MB
1102 Dealing with spurious minima.mp4
12.77MB
1103 Hopfield nets with hidden units.mp4
11.31MB
1104 Using stochastic units to improv search.mp4
11.76MB
1105 How a Boltzmann machine models data.mp4
13.28MB
1201 Boltzmann machine learning.mp4
14.03MB
1202 OPTIONAL VIDEO_ More efficient ways to get the statistics.mp4
16.93MB
1203 Restricted Boltzmann Machines.mp4
12.68MB
1204 An example of RBM learning.mp4
8.71MB
1205 RBMs for collaborative filtering.mp4
9.53MB
1301 The ups and downs of back propagation.mp4
11.83MB
1302 Belief Nets.mp4
14.86MB
1303 Learning sigmoid belief nets.mp4
14.19MB
1304 The wake-sleep algorithm.mp4
15.68MB
1401 Learning layers of features by stacking RBMs.mp4
20.07MB
1402 Discriminative learning for DBNs.mp4
11.29MB
1403 What happens during discriminative fine-tuning_.mp4
10.17MB
1404 Modeling real-valued data with an RBM.mp4
11.2MB
1405 OPTIONAL VIDEO_ RBMs are infinite sigmoid belief nets.mp4
19.44MB
1501 From PCA to autoencoders.mp4
9.68MB
1502 Deep auto encoders.mp4
4.92MB
1503 Deep auto encoders for document retrieval.mp4
10.25MB
1504 Semantic Hashing.mp4
10.97MB
1505 Learning binary codes for image retrieval.mp4
11.51MB
1506 Shallow autoencoders for pre-training.mp4
8.25MB
1601 OPTIONAL_ Learning a joint model of images and captions.mp4
13.83MB
1602 OPTIONAL_ Hierarchical Coordinate Frames.mp4
11.16MB
1603 OPTIONAL_ Bayesian optimization of hyper-parameters.mp4
15.8MB
1604 OPTIONAL_ The fog of progress.mp4
2.78MB
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