hinton - 鹿鹿堂

hinton_videos_slides.torrent

27.77 KB

slides/lec7.pptx

222.68 KB

slides/lec16.pptx

336.23 KB

slides/lec2.pptx

399.62 KB

slides/lec13.pptx

414.79 KB

slides/lec8.pptx

554.87 KB

slides/lec6.pptx

656.85 KB

slides/lec11.pptx

726.4 KB

slides/lec10.pptx

880.45 KB

slides/lec4.pptx

1.09 MB

slides/lec3.pptx

1.14 MB

slides/lec14.pptx

1.2 MB

slides/lec9.pptx

1.48 MB

slides/lec5.pptx

1.65 MB

slides/lec15.pptx

1.8 MB

slides/lec12.pptx

1.88 MB

videos/Neural Networks for Machine Learning 15.3 OPTIONAL The fog of progress.mp4

2.78 MB

slides/lec1.pptx

3.62 MB

videos/Neural Networks for Machine Learning 2.2 Learning the weights of a logistic output neuron.mp4

4.37 MB

videos/Neural Networks for Machine Learning 8.5 MacKay's quick and dirty method of setting weight costs.mp4

4.37 MB

videos/Neural Networks for Machine Learning 14.1 Deep auto encoders.mp4

4.92 MB

videos/Neural Networks for Machine Learning 3.1 A brief diversion into cognitive science.mp4

5.31 MB

videos/Neural Networks for Machine Learning 4.0 Why object recognition is difficult.mp4

5.37 MB

videos/Neural Networks for Machine Learning 2.1 The error surface for a linear neuron.mp4

5.89 MB

videos/Neural Networks for Machine Learning 1.3 Why the learning works.mp4

5.9 MB

videos/Neural Networks for Machine Learning 0.3 A simple example of learning.mp4

6.57 MB

videos/Neural Networks for Machine Learning 5.3 Adaptive learning rates for each connection.mp4

6.63 MB

videos/Neural Networks for Machine Learning 4.1 Achieving viewpoint invariance.mp4

6.89 MB

videos/Neural Networks for Machine Learning 6.2 A toy example of training an RNN.mp4

7.24 MB

videos/Neural Networks for Machine Learning 1.2 A geometrical view of perceptrons.mp4

7.32 MB

videos/Neural Networks for Machine Learning 6.1 Training RNNs with back propagation.mp4

7.33 MB

videos/Neural Networks for Machine Learning 8.1 Limiting the size of the weights.mp4

7.36 MB

videos/Neural Networks for Machine Learning 3.2 Another diversion The softmax output function.mp4

8.03 MB

videos/Neural Networks for Machine Learning 9.3 Making full Bayesian learning practical.mp4

8.13 MB

videos/Neural Networks for Machine Learning 14.5 Shallow autoencoders for pre-training.mp4

8.25 MB

videos/Neural Networks for Machine Learning 9.2 The idea of full Bayesian learning.mp4

8.39 MB

videos/Neural Networks for Machine Learning 8.2 Using noise as a regularizer.mp4

8.48 MB

videos/Neural Networks for Machine Learning 11.3 An example of RBM learning.mp4

8.71 MB

videos/Neural Networks for Machine Learning 1.0 Types of neural network architectures.mp4

8.78 MB

videos/Neural Networks for Machine Learning 6.3 Why it is difficult to train an RNN.mp4

8.89 MB

videos/Neural Networks for Machine Learning 3.3 Neuro-probabilistic language models.mp4

8.93 MB

videos/Neural Networks for Machine Learning 0.4 Three types of learning.mp4

8.96 MB

videos/Neural Networks for Machine Learning 0.2 Some simple models of neurons.mp4

9.26 MB

videos/Neural Networks for Machine Learning 1.1 Perceptrons The first generation of neural networks.mp4

9.39 MB

videos/Neural Networks for Machine Learning 11.4 RBMs for collaborative filtering.mp4

9.53 MB

videos/Neural Networks for Machine Learning 5.0 Overview of mini-batch gradient descent.mp4

9.6 MB

videos/Neural Networks for Machine Learning 14.0 From PCA to autoencoders.mp4

9.68 MB

videos/Neural Networks for Machine Learning 9.4 Dropout.mp4

9.69 MB

videos/Neural Networks for Machine Learning 5.2 The momentum method.mp4

9.74 MB

videos/Neural Networks for Machine Learning 0.1 What are neural networks.mp4

9.76 MB

videos/Neural Networks for Machine Learning 14.3 Semantic Hashing.mp4

9.99 MB

videos/Neural Networks for Machine Learning 13.2 What happens during discriminative fine-tuning.mp4

10.17 MB

videos/Neural Networks for Machine Learning 6.4 Long-term Short-term-memory.mp4

10.23 MB

videos/Neural Networks for Machine Learning 14.2 Deep auto encoders for document retrieval.mp4

10.25 MB

videos/Neural Networks for Machine Learning 2.4 Using the derivatives computed by backpropagation.mp4

11.15 MB

videos/Neural Networks for Machine Learning 15.1 OPTIONAL Hierarchical Coordinate Frames.mp4

11.16 MB

videos/Neural Networks for Machine Learning 13.3 Modeling real-valued data with an RBM.mp4

11.2 MB

videos/Neural Networks for Machine Learning 7.3 Echo State Networks.mp4

11.28 MB

videos/Neural Networks for Machine Learning 13.1 Discriminative learning for DBNs.mp4

11.29 MB

videos/Neural Networks for Machine Learning 10.2 Hopfield nets with hidden units.mp4

11.31 MB

videos/Neural Networks for Machine Learning 14.4 Learning binary codes for image retrieval.mp4

11.51 MB

videos/Neural Networks for Machine Learning 10.3 Using stochastic units to improv search.mp4

11.76 MB

videos/Neural Networks for Machine Learning 12.0 The ups and downs of back propagation.mp4

11.83 MB

videos/Neural Networks for Machine Learning 8.3 Introduction to the full Bayesian approach.mp4

12 MB

videos/Neural Networks for Machine Learning 8.4 The Bayesian interpretation of weight decay.mp4

12.27 MB

videos/Neural Networks for Machine Learning 11.2 Restricted Boltzmann Machines.mp4

12.68 MB

videos/Neural Networks for Machine Learning 10.1 Dealing with spurious minima.mp4

12.77 MB

videos/Neural Networks for Machine Learning 10.4 How a Boltzmann machine models data.mp4

13.28 MB

videos/Neural Networks for Machine Learning 2.3 The backpropagation algorithm.mp4

13.35 MB

videos/Neural Networks for Machine Learning 2.0 Learning the weights of a linear neuron.mp4

13.52 MB

videos/Neural Networks for Machine Learning 8.0 Overview of ways to improve generalization.mp4

13.57 MB

videos/Neural Networks for Machine Learning 12.2 Learning sigmoid belief nets.mp4

13.59 MB

videos/Neural Networks for Machine Learning 15.0 OPTIONAL Learning a joint model of images and captions.mp4

13.83 MB

videos/Neural Networks for Machine Learning 7.2 Learning to predict the next character using HF.mp4

13.92 MB

videos/Neural Networks for Machine Learning 11.0 Boltzmann machine learning.mp4

14.03 MB

videos/Neural Networks for Machine Learning 3.4 Ways to deal with the large number of possible outputs.mp4

14.26 MB

videos/Neural Networks for Machine Learning 3.0 Learning to predict the next word.mp4

14.28 MB

videos/Neural Networks for Machine Learning 10.0 Hopfield Nets.mp4

14.65 MB

videos/Neural Networks for Machine Learning 12.1 Belief Nets.mp4

14.86 MB

videos/Neural Networks for Machine Learning 5.1 A bag of tricks for mini-batch gradient descent.mp4

14.9 MB

videos/Neural Networks for Machine Learning 9.1 Mixtures of Experts.mp4

14.98 MB

videos/Neural Networks for Machine Learning 0.0 Why do we need machine learning.mp4

15.05 MB

videos/Neural Networks for Machine Learning 5.4 Rmsprop Divide the gradient by a running average of its recent magnitude.mp4

15.12 MB

videos/Neural Networks for Machine Learning 9.0 Why it helps to combine models.mp4

15.12 MB

videos/Neural Networks for Machine Learning 12.3 The wake-sleep algorithm.mp4

15.68 MB

videos/Neural Networks for Machine Learning 15.2 OPTIONAL Bayesian optimization of hyper-parameters.mp4

15.8 MB

videos/Neural Networks for Machine Learning 7.0 A brief overview of Hessian Free optimization.mp4

16.24 MB

videos/Neural Networks for Machine Learning 7.1 Modeling character strings with multiplicative connections.mp4

16.56 MB

videos/Neural Networks for Machine Learning 1.4 What perceptrons can't do.mp4

16.57 MB

videos/Neural Networks for Machine Learning 11.1 OPTIONAL VIDEO More efficient ways to get the statistics.mp4

16.93 MB

videos/Neural Networks for Machine Learning 4.2 Convolutional nets for digit recognition.mp4

18.46 MB

videos/Neural Networks for Machine Learning 13.4 OPTIONAL VIDEO RBMs are infinite sigmoid belief nets.mp4

19.44 MB

videos/Neural Networks for Machine Learning 13.0 Learning layers of features by stacking RBMs.mp4

20.07 MB

videos/Neural Networks for Machine Learning 6.0 Modeling sequences A brief overview.mp4

20.13 MB

videos/Neural Networks for Machine Learning 4.3 Convolutional nets for object recognition.mp4

23.03 MB

资源详情

免责声明