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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： 884.54 MB
收录时间： 2013-1-31 18:52
已经下载： 3次
资源热度： 130
最近下载： 2024-11-30 05:17

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Neural Networks for Machine Learning.torrent

5 - 4 - Convolutional nets for object recognition [17min].mp423.03MB

7 - 1 - Modeling sequences A brief overview.mp420.13MB

14 - 1 - Learning layers of features by stacking RBMs [17 min].mp420.07MB

14 - 5 - OPTIONAL VIDEO RBMs are infinite sigmoid belief nets [17 mins].mp419.44MB

5 - 3 - Convolutional nets for digit recognition [16 min].mp418.46MB

12 - 2 - OPTIONAL VIDEO More efficient ways to get the statistics [15 mins].mp416.93MB

2 - 5 - What perceptrons cant do [15 min].mp416.57MB

8 - 2 - Modeling character strings with multiplicative connections [14 mins].mp416.56MB

8 - 1 - A brief overview of Hessian Free optimization.mp416.24MB

16 - 3 - OPTIONAL Bayesian optimization of hyper-parameters [13 min].mp415.8MB

13 - 4 - The wake-sleep algorithm [13 min].mp415.68MB

10 - 1 - Why it helps to combine models [13 min].mp415.12MB

6 - 5 - Rmsprop Divide the gradient by a running average of its recent magnitude.mp415.12MB

1 - 1 - Why do we need machine learning [13 min].mp415.05MB

10 - 2 - Mixtures of Experts [13 min].mp414.98MB

6 - 2 - A bag of tricks for mini-batch gradient descent.mp414.9MB

13 - 2 - Belief Nets [13 min].mp414.86MB

11 - 1 - Hopfield Nets [13 min].mp414.65MB

4 - 1 - Learning to predict the next word [13 min].mp414.28MB

4 - 5 - Ways to deal with the large number of possible outputs [15 min].mp414.26MB

12 - 1 - Boltzmann machine learning [12 min].mp414.03MB

8 - 3 - Learning to predict the next character using HF [12 mins].mp413.92MB

16 - 1 - OPTIONAL Learning a joint model of images and captions [10 min].mp413.83MB

13 - 3 - Learning sigmoid belief nets [12 min].mp413.59MB

9 - 1 - Overview of ways to improve generalization [12 min].mp413.57MB

3 - 1 - Learning the weights of a linear neuron [12 min].mp413.52MB

3 - 4 - The backpropagation algorithm [12 min].mp413.35MB

11 - 5 - How a Boltzmann machine models data [12 min].mp413.28MB

11 - 2 - Dealing with spurious minima [11 min].mp412.77MB

12 - 3 - Restricted Boltzmann Machines [11 min].mp412.68MB

9 - 5 - The Bayesian interpretation of weight decay [11 min].mp412.27MB

9 - 4 - Introduction to the full Bayesian approach [12 min].mp412MB

13 - 1 - The ups and downs of back propagation [10 min].mp411.83MB

11 - 4 - Using stochastic units to improv search [11 min].mp411.76MB

15 - 5 - Learning binary codes for image retrieval [9 mins].mp411.51MB

11 - 3 - Hopfield nets with hidden units [10 min].mp411.31MB

14 - 2 - Discriminative learning for DBNs [9 mins].mp411.29MB

8 - 4 - Echo State Networks [9 min].mp411.28MB

14 - 4 - Modeling real-valued data with an RBM [10 mins].mp411.2MB

16 - 2 - OPTIONAL Hierarchical Coordinate Frames [10 mins].mp411.16MB

3 - 5 - Using the derivatives computed by backpropagation [10 min].mp411.15MB

15 - 3 - Deep auto encoders for document retrieval [8 mins].mp410.25MB

7 - 5 - Long-term Short-term-memory.mp410.23MB

14 - 3 - What happens during discriminative fine-tuning [8 mins].mp410.17MB

15 - 4 - Semantic Hashing [9 mins].mp49.99MB

1 - 2 - What are neural networks [8 min].mp49.76MB

6 - 3 - The momentum method.mp49.74MB

10 - 5 - Dropout [9 min].mp49.69MB

15 - 1 - From PCA to autoencoders [5 mins].mp49.68MB

6 - 1 - Overview of mini-batch gradient descent.mp49.6MB

12 - 5 - RBMs for collaborative filtering [8 mins].mp49.53MB

2 - 2 - Perceptrons The first generation of neural networks [8 min].mp49.39MB

1 - 3 - Some simple models of neurons [8 min].mp49.26MB

1 - 5 - Three types of learning [8 min].mp48.96MB

4 - 4 - Neuro-probabilistic language models [8 min].mp48.93MB

7 - 4 - Why it is difficult to train an RNN.mp48.89MB

2 - 1 - Types of neural network architectures [7 min].mp48.78MB

12 - 4 - An example of RBM learning [7 mins].mp48.71MB

9 - 3 - Using noise as a regularizer [7 min].mp48.48MB

10 - 3 - The idea of full Bayesian learning [7 min].mp48.39MB

15 - 6 - Shallow autoencoders for pre-training [7 mins].mp48.25MB

10 - 4 - Making full Bayesian learning practical [7 min].mp48.13MB

4 - 3 - Another diversion The softmax output function [7 min].mp48.03MB

9 - 2 - Limiting the size of the weights [6 min].mp47.36MB

7 - 2 - Training RNNs with back propagation.mp47.33MB

2 - 3 - A geometrical view of perceptrons [6 min].mp47.32MB

7 - 3 - A toy example of training an RNN.mp47.24MB

5 - 2 - Achieving viewpoint invariance [6 min].mp46.89MB

6 - 4 - Adaptive learning rates for each connection.mp46.63MB

1 - 4 - A simple example of learning [6 min].mp46.57MB

2 - 4 - Why the learning works [5 min].mp45.9MB

3 - 2 - The error surface for a linear neuron [5 min].mp45.89MB

5 - 1 - Why object recognition is difficult [5 min].mp45.37MB

4 - 2 - A brief diversion into cognitive science [4 min].mp45.31MB

15 - 2 - Deep auto encoders [4 mins].mp44.92MB

9 - 6 - MacKays quick and dirty method of setting weight costs [4 min].mp44.37MB

3 - 3 - Learning the weights of a logistic output neuron [4 min].mp44.37MB

16 - 4 - OPTIONAL The fog of progress [3 min].mp42.78MB