PushBT Magnet/Torrent

Categories

Recommend Tools

  • Convert torrent to magnet
  • Convert torrent to magnet

filehinton-18eeQ

hinton
  • PPTXslides\\/lec7.pptx222.68KB
  • PPTXslides\\/lec16.pptx336.23KB
  • PPTXslides\\/lec2.pptx399.62KB
  • PPTXslides\\/lec13.pptx414.79KB
  • PPTXslides\\/lec8.pptx554.87KB
  • PPTXslides\\/lec6.pptx656.85KB
  • PPTXslides\\/lec11.pptx726.40KB
  • PPTXslides\\/lec10.pptx880.45KB
  • PPTXslides\\/lec4.pptx1.09MB
  • PPTXslides\\/lec3.pptx1.14MB
  • PPTXslides\\/lec14.pptx1.20MB
  • PPTXslides\\/lec9.pptx1.48MB
  • PPTXslides\\/lec5.pptx1.65MB
  • PPTXslides\\/lec15.pptx1.80MB
  • PPTXslides\\/lec12.pptx1.88MB
  • MP4videos\\/Neural Networks for chine Learning 15.3 OPTIONAL The fog of progress.mp42.78MB
  • PPTXslides\\/lec1.pptx3.62MB
  • MP4videos\\/Neural Networks for chine Learning 2.2 Learning the weights of a logistic output neuron.mp44.37MB
  • MP4videos\\/Neural Networks for chine Learning 8.5 MacKay\s quick and dirty method of setting weight costs.mp44.37MB
  • MP4videos\\/Neural Networks for chine Learning 14.1 Deep auto encoders.mp44.92MB
  • MP4videos\\/Neural Networks for chine Learning 3.1 A brief diversion into cognitive science.mp45.31MB
  • MP4videos\\/Neural Networks for chine Learning 4.0 Why ob<x>ject recognition is difficult.mp45.37MB
  • MP4videos\\/Neural Networks for chine Learning 2.1 The error surface for a linear neuron.mp45.89MB
  • MP4videos\\/Neural Networks for chine Learning 1.3 Why the learning works.mp45.90MB
  • MP4videos\\/Neural Networks for chine Learning 0.3 A simple example of learning.mp46.57MB
  • MP4videos\\/Neural Networks for chine Learning 5.3 Adaptive learning rates for each connection.mp46.63MB
  • MP4videos\\/Neural Networks for chine Learning 4.1 Achieving viewpoint invariance.mp46.89MB
  • MP4videos\\/Neural Networks for chine Learning 6.2 A toy example of training an RNN.mp47.24MB
  • MP4videos\\/Neural Networks for chine Learning 1.2 A geometrical view of perceptrons.mp47.32MB
  • MP4videos\\/Neural Networks for chine Learning 6.1 Training RNNs with back propagation.mp47.33MB
  • MP4videos\\/Neural Networks for chine Learning 8.1 Limiting the size of the weights.mp47.36MB
  • MP4videos\\/Neural Networks for chine Learning 3.2 Another diversion The softmax output function.mp48.03MB
  • MP4videos\\/Neural Networks for chine Learning 9.3 Making full Bayesian learning practical.mp48.13MB
  • MP4videos\\/Neural Networks for chine Learning 14.5 Shallow autoencoders for pre-training.mp48.25MB
  • MP4videos\\/Neural Networks for chine Learning 9.2 The idea of full Bayesian learning.mp48.39MB
  • MP4videos\\/Neural Networks for chine Learning 8.2 Using se as a regularizer.mp48.48MB
  • MP4videos\\/Neural Networks for chine Learning 11.3 An example of RBM learning.mp48.71MB
  • MP4videos\\/Neural Networks for chine Learning 1.0 Types of neural network architectures.mp48.78MB
  • MP4videos\\/Neural Networks for chine Learning 6.3 Why it is difficult to train an RNN.mp48.89MB
  • MP4videos\\/Neural Networks for chine Learning 3.3 Neuro-probabilistic language models.mp48.93MB
  • MP4videos\\/Neural Networks for chine Learning 0.4 Three types of learning.mp48.96MB
  • MP4videos\\/Neural Networks for chine Learning 0.2 Some simple models of neurons.mp49.26MB
  • MP4videos\\/Neural Networks for chine Learning 1.1 Perceptrons The first generation of neural networks.mp49.39MB
  • MP4videos\\/Neural Networks for chine Learning 11.4 RBMs for collaborative filtering.mp49.53MB
  • MP4videos\\/Neural Networks for chine Learning 5.0 Overview of mini-batch gradient descent.mp49.60MB
  • MP4videos\\/Neural Networks for chine Learning 14.0 From PCA to autoencoders.mp49.68MB
  • MP4videos\\/Neural Networks for chine Learning 9.4 Dropout.mp49.69MB
  • MP4videos\\/Neural Networks for chine Learning 5.2 The momentum method.mp49.74MB
  • MP4videos\\/Neural Networks for chine Learning 0.1 What are neural networks.mp49.76MB
  • MP4videos\\/Neural Networks for chine Learning 14.3 Semantic Hashing.mp49.99MB
  • MP4videos\\/Neural Networks for chine Learning 13.2 What happens during discriminative fine-tuning.mp410.17MB
  • MP4videos\\/Neural Networks for chine Learning 6.4 Long-term Short-term-memory.mp410.23MB
  • MP4videos\\/Neural Networks for chine Learning 14.2 Deep auto encoders for doent retrieval.mp410.25MB
  • MP4videos\\/Neural Networks for chine Learning 2.4 Using the derivatives computed by backpropagation.mp411.15MB
  • MP4videos\\/Neural Networks for chine Learning 15.1 OPTIONAL Hierarchical Coordinate fr<x>ames.mp411.16MB
  • MP4videos\\/Neural Networks for chine Learning 13.3 Modeling real-valued data with an RBM.mp411.20MB
  • MP4videos\\/Neural Networks for chine Learning 7.3 Echo State Networks.mp411.28MB
  • MP4videos\\/Neural Networks for chine Learning 13.1 Discriminative learning for DBNs.mp411.29MB
  • MP4videos\\/Neural Networks for chine Learning 10.2 Hopfield nets with hidden units.mp411.31MB
  • MP4videos\\/Neural Networks for chine Learning 14.4 Learning binary codes for image retrieval.mp411.51MB
  • MP4videos\\/Neural Networks for chine Learning 10.3 Using stochastic units to improv search.mp411.76MB
  • MP4videos\\/Neural Networks for chine Learning 12.0 The ups and downs of back propagation.mp411.83MB
  • MP4videos\\/Neural Networks for chine Learning 8.3 Introduction to the full Bayesian approach.mp412.00MB
  • MP4videos\\/Neural Networks for chine Learning 8.4 The Bayesian interpretation of weight decay.mp412.27MB
  • MP4videos\\/Neural Networks for chine Learning 11.2 Restricted Boltzmann Machines.mp412.68MB
  • MP4videos\\/Neural Networks for chine Learning 10.1 Dealing with spurious minima.mp412.77MB
  • MP4videos\\/Neural Networks for chine Learning 10.4 How a Boltzmann machine models data.mp413.28MB
  • MP4videos\\/Neural Networks for chine Learning 2.3 The backpropagation algorithm.mp413.35MB
  • MP4videos\\/Neural Networks for chine Learning 2.0 Learning the weights of a linear neuron.mp413.52MB
  • MP4videos\\/Neural Networks for chine Learning 8.0 Overview of ways to improve generalization.mp413.57MB
  • MP4videos\\/Neural Networks for chine Learning 12.2 Learning sigmoid belief nets.mp413.59MB
  • MP4videos\\/Neural Networks for chine Learning 15.0 OPTIONAL Learning a joint model of images and captions.mp413.83MB
  • MP4videos\\/Neural Networks for chine Learning 7.2 Learning to predict the next character using HF.mp413.92MB
  • MP4videos\\/Neural Networks for chine Learning 11.0 Boltzmann machine learning.mp414.03MB
  • MP4videos\\/Neural Networks for chine Learning 3.4 Ways to deal with the large number of possible outputs.mp414.26MB
  • MP4videos\\/Neural Networks for chine Learning 3.0 Learning to predict the next word.mp414.28MB
  • MP4videos\\/Neural Networks for chine Learning 10.0 Hopfield Nets.mp414.65MB
  • MP4videos\\/Neural Networks for chine Learning 12.1 Belief Nets.mp414.86MB
  • MP4videos\\/Neural Networks for chine Learning 5.1 A bag of tricks for mini-batch gradient descent.mp414.90MB
  • MP4videos\\/Neural Networks for chine Learning 9.1 Mixtures of Experts.mp414.98MB
  • MP4videos\\/Neural Networks for chine Learning 0.0 Why do we need machine learning.mp415.05MB
  • MP4videos\\/Neural Networks for chine Learning 5.4 Rmsprop Divide the gradient by a running erage of its recent magnitude.mp415.12MB
  • MP4videos\\/Neural Networks for chine Learning 9.0 Why it helps to combine models.mp415.12MB
  • MP4videos\\/Neural Networks for chine Learning 12.3 The wake-sleep algorithm.mp415.68MB
  • MP4videos\\/Neural Networks for chine Learning 15.2 OPTIONAL Bayesian optimization of hyper-parameters.mp415.80MB
  • MP4videos\\/Neural Networks for chine Learning 7.0 A brief overview of Hessian Free optimization.mp416.24MB
  • MP4videos\\/Neural Networks for chine Learning 7.1 Modeling character strings with multiplicative connections.mp416.56MB
  • MP4videos\\/Neural Networks for chine Learning 1.4 What perceptrons can\t do.mp416.57MB
  • MP4videos\\/Neural Networks for chine Learning 11.1 OPTIONAL VIDEO More efficient ways to get the statistics.mp416.93MB
  • MP4videos\\/Neural Networks for chine Learning 4.2 Convolutional nets for digit recognition.mp418.46MB
  • MP4videos\\/Neural Networks for chine Learning 13.4 OPTIONAL VIDEO RBMs are infinite sigmoid belief nets.mp419.44MB
  • MP4videos\\/Neural Networks for chine Learning 13.0 Learning la<x>yers of features by stacking RBMs.mp420.07MB
  • MP4videos\\/Neural Networks for chine Learning 6.0 Modeling sequences A brief overview.mp420.13MB
  • MP4videos\\/Neural Networks for chine Learning 4.3 Convolutional nets for ob<x>ject recognition.mp423.03MB
Latest Search: 1.KBKD-929   2.IDBD-400   3.MXSPS-290   4.MGDV-044   5.LPT-010   6.JUSD-302   7.DVMO-007   8.TMGM-002   9.GUR-003   10.MBW-09   11.ARBX-015   12.DKYE-19   13.DJSI-039   14.BNDV-00601   15.KNCS-038   16.YKL-007   17.IDBD-309   18.TYWD-017   19.MIBD-652   20.ONSD-440   21.ONSD-610   22.AGEMIX-073   23.NFXV-027   24.JUKD-117   25.DVDPS-845   26.FUL-013   27.IDBD-155   28.ONSD-503   29.NTRD-026   30.PXV-014   31.ISD-113   32.YTR-044   33.NHDTA-271   34.DWD-093   35.BMW-003   36.VSPDS-381   37.CADR-195   38.MIBD-558   39.SEED-83   40.HKM-055   41.DWD-053   42.KIBD-086   43.MXD-013   44.HBAD-093   45.OBSE-002   46.JUC-945   47.NFDM-289   48.GYAZ-089   49.UK-25   50.DOM-036   51.VNDS-7008   52.YTR-044   53.ABP-026   54.TJT-001   55.KTDVR-142   56.OKSN-161   57.FAX-097   58.FE-051   59.NADE-916   60.AEIL-284   61.JUMP-1047   62.TDMJ-078   63.EMAF-038   64.DVPO-004   65.VO-018   66.DOGU-011   67.KJV-008   68.AYA-006   69.VNDS-2452   70.FN-045D   71.929   72.400   73.290   74.044   75.010   76.302   77.007   78.002   79.003   80.09   81.015   82.19   83.039   84.00601   85.038   86.007   87.309   88.017   89.652   90.440   91.610   92.073   93.027   94.117   95.845   96.013   97.155   98.503   99.026   100.014   101.113   102.044   103.271   104.093   105.003   106.381   107.195   108.558   109.83   110.055   111.053   112.086   113.013   114.093   115.002   116.945   117.289   118.089   119.25   120.036   121.7008   122.044   123.026   124.001   125.142   126.161   127.097   128.051   129.916   130.284   131.1047   132.078   133.038   134.004   135.018   136.011   137.008   138.006   139.2452   140.045D