Create Tool:
Create Time:1970-01-01 08:00:00
File Size:902.51 MB
File Count:95
File Hash:44bb6cc23b321554a0ac13d0dc7228b28846b129
| 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 | 726.40 KB |
| slides/lec10.pptx | 880.45 KB |
| slides/lec4.pptx | 1.09 MB |
| slides/lec3.pptx | 1.14 MB |
| slides/lec14.pptx | 1.20 MB |
| slides/lec9.pptx | 1.48 MB |
| slides/lec5.pptx | 1.65 MB |
| slides/lec15.pptx | 1.80 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 | 5.90 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 | 9.60 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 | 11.20 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 | 12.00 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 | 14.90 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 | 15.80 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 |
| udp://tracker.leechers-paradise.org:6969 |
| udp://zer0day.ch:1337 |
| udp://tracker.coppersurfer.tk:6969 |
| udp://public.popcorn-tracker.org:6969 |
TorrentBank
Copyright © 2024