PushBT Magnet/Torrent

Categories

Recommend Tools

  • Convert torrent to magnet
  • Convert torrent to magnet

fileneural-nets-hinton-15ZLo

neural hinton
  • MP41 - 1 - Why do we need chine learning- [13 min].mp415.05MB
  • MP41 - 2 - What are neural networks- [8 min].mp49.76MB
  • MP41 - 3 - Some simple models of neurons [8 min].mp49.26MB
  • MP41 - 4 - A simple example of learning [6 min].mp46.57MB
  • MP41 - 5 - Three types of learning [8 min].mp48.96MB
  • MP410 - 1 - Why it helps to combine models [13 min].mp415.12MB
  • MP410 - 2 - Mixtures of Experts [13 min].mp414.98MB
  • MP410 - 3 - The idea of full Bayesian learning [7 min].mp48.39MB
  • MP410 - 4 - king full Bayesian learning practical [7 min].mp48.13MB
  • MP410 - 5 - Dropout [9 min].mp49.69MB
  • MP411 - 1 - Hopfield Nets [13 min].mp414.65MB
  • MP411 - 2 - Dealing with spurious mini [11 min].mp412.77MB
  • MP411 - 3 - Hopfield nets with hidden units [10 min].mp411.31MB
  • MP411 - 4 - Using stochastic units to improv search [11 min].mp411.76MB
  • MP411 - 5 - How a Boltznn machine models data [12 min].mp413.28MB
  • MP412 - 1 - Boltznn machine learning [12 min].mp414.03MB
  • MP412 - 2 - OPTIONAL VIDEO- More efficient ways to get the statistics [15 mins].mp416.93MB
  • MP412 - 3 - Restricted Boltznn Machines [11 min].mp412.68MB
  • MP412 - 4 - An example of RBM learning [7 mins].mp48.71MB
  • MP412 - 5 - RBMs for collaborative filtering [8 mins].mp49.53MB
  • MP413 - 1 - The ups and downs of back propagation [10 min].mp411.83MB
  • MP413 - 2 - Belief Nets [13 min].mp414.86MB
  • MP413 - 3 - Learning sigmoid belief nets [12 min].mp413.59MB
  • MP413 - 4 - The wake-sleep algorithm [13 min].mp415.68MB
  • MP414 - 1 - Learning la<x>yers of features by stacking RBMs [17 min].mp420.07MB
  • MP414 - 2 - Discriminative learning for DBNs [9 mins].mp411.29MB
  • MP414 - 3 - What happens during discriminative fine-tuning- [8 mins].mp410.17MB
  • MP414 - 4 - Modeling real-valued data with an RBM [10 mins].mp411.20MB
  • MP414 - 5 - OPTIONAL VIDEO- RBMs are infinite sigmoid belief nets [17 mins].mp419.44MB
  • MP415 - 1 - From PCA to autoencoders [5 mins].mp49.68MB
  • MP415 - 2 - Deep auto encoders [4 mins].mp44.92MB
  • MP415 - 3 - Deep auto encoders for doent retrieval [8 mins].mp410.25MB
  • MP415 - 4 - Sentic Hashing [9 mins].mp49.99MB
  • MP415 - 5 - Learning binary codes for ige retrieval [9 mins].mp411.51MB
  • MP415 - 6 - Shallow autoencoders for pre-training [7 mins].mp48.25MB
  • MP416 - 1 - OPTIONAL- Learning a joint model of iges and captions [10 min].mp413.83MB
  • MP416 - 2 - OPTIONAL- Hierarchical Coordinate fr<x>ames [10 mins].mp411.16MB
  • MP416 - 3 - OPTIONAL- Bayesian optimization of hyper-parameters [13 min].mp415.80MB
  • MP416 - 4 - OPTIONAL- The fog of progress [3 min].mp42.78MB
  • MP42 - 1 - Types of neural network architectures [7 min].mp48.78MB
  • MP42 - 2 - Perceptrons- The first generation of neural networks [8 min].mp49.39MB
  • MP42 - 3 - A geometrical view of perceptrons [6 min].mp47.32MB
  • MP42 - 4 - Why the learning works [5 min].mp45.90MB
  • MP42 - 5 - What perceptrons can-\t do [15 min].mp416.57MB
  • MP43 - 1 - Learning the weights of a linear neuron [12 min].mp413.52MB
  • MP43 - 2 - The error surface for a linear neuron [5 min].mp45.89MB
  • MP43 - 3 - Learning the weights of a logistic output neuron [4 min].mp44.37MB
  • MP43 - 4 - The backpropagation algorithm [12 min].mp413.35MB
  • MP43 - 5 - Using the derivatives computed by backpropagation [10 min].mp411.15MB
  • MP44 - 1 - Learning to predict the next word [13 min].mp414.28MB
  • MP44 - 2 - A brief diversion into cognitive science [4 min].mp45.31MB
  • MP44 - 3 - Another diversion- The softx output function [7 min].mp48.03MB
  • MP44 - 4 - Neuro-probabilistic language models [8 min].mp48.93MB
  • MP44 - 5 - Ways to deal with the large number of possible outputs [15 min].mp414.26MB
  • MP45 - 1 - Why ob<x>ject recognition is difficult [5 min].mp45.37MB
  • MP45 - 2 - Achieving viewpoint invariance [6 min].mp46.89MB
  • MP45 - 3 - Convolutional nets for digit recognition [16 min].mp418.46MB
  • MP45 - 4 - Convolutional nets for ob<x>ject recognition [17min].mp423.03MB
  • MP46 - 1 - Overview of mini-batch gradient descent.mp49.60MB
  • MP46 - 2 - A bag of tricks for mini-batch gradient descent.mp414.90MB
  • MP46 - 3 - The momentum method.mp49.74MB
  • MP46 - 4 - Adaptive learning rates for each connection.mp46.63MB
  • MP46 - 5 - Rmsprop- Divide the gradient by a running erage of its recent gnitude.mp415.12MB
  • MP47 - 1 - Modeling sequences- A brief overview.mp420.13MB
  • MP47 - 2 - Training RNNs with back propagation.mp47.33MB
  • MP47 - 3 - A toy example of training an RNN.mp47.24MB
  • MP47 - 4 - Why it is difficult to train an RNN.mp48.89MB
  • MP47 - 5 - Long-term Short-term-memory.mp410.23MB
  • MP48 - 1 - A brief overview of Hessian Free optimization.mp416.24MB
  • MP48 - 2 - Modeling character strings with multiplicative connections [14 mins].mp416.56MB
  • MP48 - 3 - Learning to predict the next character using HF [12 mins].mp413.92MB
  • MP48 - 4 - Echo State Networks [9 min].mp411.28MB
  • MP49 - 1 - Overview of ways to improve generalization [12 min].mp413.57MB
  • MP49 - 2 - Limiting the size of the weights [6 min].mp47.36MB
  • MP49 - 3 - Using se as a regularizer [7 min].mp48.48MB
  • MP49 - 4 - Introduction to the full Bayesian approach [12 min].mp412.00MB
  • MP49 - 5 - The Bayesian interpretation of weight decay [11 min].mp412.27MB
  • MP49 - 6 - cKay-\s quick and dirty method of setting weight costs [4 min].mp44.37MB
Latest Search: 1.MIBD-735   2.SDMS-464   3.RKI-261   4.PBD-137   5.SWSM-001   6.NATR-261   7.SIDD-022   8.ALD-462   9.SMA-575   10.SWD-118   11.PDX-052   12.ID-021   13.KS-8633   14.BNDV-402   15.SAMA-695   16.BSJ-008   17.PKC-012   18.JPDRS-1657   19.MDB-592   20.XRW-074   21.HND-223   22.DFGR-001   23.BNSPS-425   24.YSN-458   25.DJSB-089   26.PBD-335   27.SDMU-375   28.CJOB-023   29.MDTM-204   30.DINM-363   31.MAGN-007   32.PARATHD-1922   33.EVIS-175   34.FABS-093   35.XC-1076   36.KBMS-039   37.HAHOB-011   38.KMVR-453   39.YRH-168   40.T28-537   41.AVOP-415   42.EMRD-122   43.HMD-035   44.735   45.4   46.261   47.137   48.001   49.261   50.022   51.462   52.575   53.118   54.052   55.021   56.8633   57.402   58.695   59.008   60.012   61.1657   62.592   63.074   64.223   65.001   66.425   67.458   68.089   69.335   70.375   71.023   72.204   73.363   74.007   75.1922   76.175   77.093   78.1076   79.039   80.011   81.453   82.168   83.537   84.415   85.122   86.035