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Neural Networks for Machine Learning
magnet:?xt=urn:btih:C44EA3E669C895B7EF510B09CA044C03B500DC8F&dn=Neural Networks for Machine Learning
磁力链接详情
Hash值:
C44EA3E669C895B7EF510B09CA044C03B500DC8F
点击数:
112
文件大小:
884.54 MB
文件数量:
78
创建日期:
2013-1-31 18:52
最后访问:
2024-6-10 22:59
访问标签:
Neural
Networks
for
Machine
Learning
文件列表详情
5 - 4 - Convolutional nets for object recognition [17min].mp4 23.03 MB
7 - 1 - Modeling sequences A brief overview.mp4 20.13 MB
14 - 1 - Learning layers of features by stacking RBMs [17 min].mp4 20.07 MB
14 - 5 - OPTIONAL VIDEO RBMs are infinite sigmoid belief nets [17 mins].mp4 19.44 MB
5 - 3 - Convolutional nets for digit recognition [16 min].mp4 18.46 MB
12 - 2 - OPTIONAL VIDEO More efficient ways to get the statistics [15 mins].mp4 16.93 MB
2 - 5 - What perceptrons cant do [15 min].mp4 16.57 MB
8 - 2 - Modeling character strings with multiplicative connections [14 mins].mp4 16.56 MB
8 - 1 - A brief overview of Hessian Free optimization.mp4 16.24 MB
16 - 3 - OPTIONAL Bayesian optimization of hyper-parameters [13 min].mp4 15.8 MB
13 - 4 - The wake-sleep algorithm [13 min].mp4 15.68 MB
10 - 1 - Why it helps to combine models [13 min].mp4 15.12 MB
6 - 5 - Rmsprop Divide the gradient by a running average of its recent magnitude.mp4 15.12 MB
1 - 1 - Why do we need machine learning [13 min].mp4 15.05 MB
10 - 2 - Mixtures of Experts [13 min].mp4 14.98 MB
6 - 2 - A bag of tricks for mini-batch gradient descent.mp4 14.9 MB
13 - 2 - Belief Nets [13 min].mp4 14.86 MB
11 - 1 - Hopfield Nets [13 min].mp4 14.65 MB
4 - 1 - Learning to predict the next word [13 min].mp4 14.28 MB
4 - 5 - Ways to deal with the large number of possible outputs [15 min].mp4 14.26 MB
12 - 1 - Boltzmann machine learning [12 min].mp4 14.03 MB
8 - 3 - Learning to predict the next character using HF [12 mins].mp4 13.92 MB
16 - 1 - OPTIONAL Learning a joint model of images and captions [10 min].mp4 13.83 MB
13 - 3 - Learning sigmoid belief nets [12 min].mp4 13.59 MB
9 - 1 - Overview of ways to improve generalization [12 min].mp4 13.57 MB
3 - 1 - Learning the weights of a linear neuron [12 min].mp4 13.52 MB
3 - 4 - The backpropagation algorithm [12 min].mp4 13.35 MB
11 - 5 - How a Boltzmann machine models data [12 min].mp4 13.28 MB
11 - 2 - Dealing with spurious minima [11 min].mp4 12.77 MB
12 - 3 - Restricted Boltzmann Machines [11 min].mp4 12.68 MB
9 - 5 - The Bayesian interpretation of weight decay [11 min].mp4 12.27 MB
9 - 4 - Introduction to the full Bayesian approach [12 min].mp4 12 MB
13 - 1 - The ups and downs of back propagation [10 min].mp4 11.83 MB
11 - 4 - Using stochastic units to improv search [11 min].mp4 11.76 MB
15 - 5 - Learning binary codes for image retrieval [9 mins].mp4 11.51 MB
11 - 3 - Hopfield nets with hidden units [10 min].mp4 11.31 MB
14 - 2 - Discriminative learning for DBNs [9 mins].mp4 11.29 MB
8 - 4 - Echo State Networks [9 min].mp4 11.28 MB
14 - 4 - Modeling real-valued data with an RBM [10 mins].mp4 11.2 MB
16 - 2 - OPTIONAL Hierarchical Coordinate Frames [10 mins].mp4 11.16 MB
3 - 5 - Using the derivatives computed by backpropagation [10 min].mp4 11.15 MB
15 - 3 - Deep auto encoders for document retrieval [8 mins].mp4 10.25 MB
7 - 5 - Long-term Short-term-memory.mp4 10.23 MB
14 - 3 - What happens during discriminative fine-tuning [8 mins].mp4 10.17 MB
15 - 4 - Semantic Hashing [9 mins].mp4 9.99 MB
1 - 2 - What are neural networks [8 min].mp4 9.76 MB
6 - 3 - The momentum method.mp4 9.74 MB
10 - 5 - Dropout [9 min].mp4 9.69 MB
15 - 1 - From PCA to autoencoders [5 mins].mp4 9.68 MB
6 - 1 - Overview of mini-batch gradient descent.mp4 9.6 MB
12 - 5 - RBMs for collaborative filtering [8 mins].mp4 9.53 MB
2 - 2 - Perceptrons The first generation of neural networks [8 min].mp4 9.39 MB
1 - 3 - Some simple models of neurons [8 min].mp4 9.26 MB
1 - 5 - Three types of learning [8 min].mp4 8.96 MB
4 - 4 - Neuro-probabilistic language models [8 min].mp4 8.93 MB
7 - 4 - Why it is difficult to train an RNN.mp4 8.89 MB
2 - 1 - Types of neural network architectures [7 min].mp4 8.78 MB
12 - 4 - An example of RBM learning [7 mins].mp4 8.71 MB
9 - 3 - Using noise as a regularizer [7 min].mp4 8.48 MB
10 - 3 - The idea of full Bayesian learning [7 min].mp4 8.39 MB
15 - 6 - Shallow autoencoders for pre-training [7 mins].mp4 8.25 MB
10 - 4 - Making full Bayesian learning practical [7 min].mp4 8.13 MB
4 - 3 - Another diversion The softmax output function [7 min].mp4 8.03 MB
9 - 2 - Limiting the size of the weights [6 min].mp4 7.36 MB
7 - 2 - Training RNNs with back propagation.mp4 7.33 MB
2 - 3 - A geometrical view of perceptrons [6 min].mp4 7.32 MB
7 - 3 - A toy example of training an RNN.mp4 7.24 MB
5 - 2 - Achieving viewpoint invariance [6 min].mp4 6.89 MB
6 - 4 - Adaptive learning rates for each connection.mp4 6.63 MB
1 - 4 - A simple example of learning [6 min].mp4 6.57 MB
2 - 4 - Why the learning works [5 min].mp4 5.9 MB
3 - 2 - The error surface for a linear neuron [5 min].mp4 5.89 MB
5 - 1 - Why object recognition is difficult [5 min].mp4 5.37 MB
4 - 2 - A brief diversion into cognitive science [4 min].mp4 5.31 MB
15 - 2 - Deep auto encoders [4 mins].mp4 4.92 MB
9 - 6 - MacKays quick and dirty method of setting weight costs [4 min].mp4 4.37 MB
3 - 3 - Learning the weights of a logistic output neuron [4 min].mp4 4.37 MB
16 - 4 - OPTIONAL The fog of progress [3 min].mp4 2.78 MB
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