Neural Networks for Machine Learning

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  3. Creat Time:2014-11-21
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  5. Last Active:2025-07-18
  6. File Tags:Neural  Networks  for  Machine  Learning  
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File List:

    Neural Networks for Machine Learning

  1. 0504 Convolutional nets for object recognition.mp4 23.03 MB
  2. 0701 Modeling sequences_ A brief overview.mp4 20.13 MB
  3. 1401 Learning layers of features by stacking RBMs.mp4 20.07 MB
  4. 1405 OPTIONAL VIDEO_ RBMs are infinite sigmoid belief nets.mp4 19.44 MB
  5. 0503 Convolutional nets for digit recognition.mp4 18.46 MB
  6. 1202 OPTIONAL VIDEO_ More efficient ways to get the statistics.mp4 16.93 MB
  7. 0205 What perceptrons can_t do.mp4 16.58 MB
  8. 0802 Modeling character strings with multiplicative connections.mp4 16.56 MB
  9. 0801 A brief overview of Hessian Free optimization.mp4 16.24 MB
  10. 1603 OPTIONAL_ Bayesian optimization of hyper-parameters.mp4 15.80 MB
  11. 1304 The wake-sleep algorithm.mp4 15.68 MB
  12. 1001 Why it helps to combine models.mp4 15.12 MB
  13. 0605 Rmsprop_ Divide the gradient by a running average of its recent magnitude.mp4 15.12 MB
  14. 0101 Why do we need machine learning_.mp4 15.05 MB
  15. 1002 Mixtures of Experts.mp4 14.98 MB
  16. 0602 A bag of tricks for mini-batch gradient descent.mp4 14.90 MB
  17. 1302 Belief Nets.mp4 14.86 MB
  18. 1101 Hopfield Nets.mp4 14.65 MB
  19. 0401 Learning to predict the next word.mp4 14.28 MB
  20. 0405 Ways to deal with the large number of possible outputs.mp4 14.26 MB
  21. 1303 Learning sigmoid belief nets.mp4 14.19 MB
  22. 1201 Boltzmann machine learning.mp4 14.03 MB
  23. 0803 Learning to predict the next character using HF.mp4 13.92 MB
  24. 1601 OPTIONAL_ Learning a joint model of images and captions.mp4 13.83 MB
  25. 0901 Overview of ways to improve generalization.mp4 13.57 MB
  26. 0301 Learning the weights of a linear neuron.mp4 13.52 MB
  27. 0304 The backpropagation algorithm.mp4 13.35 MB
  28. 1105 How a Boltzmann machine models data.mp4 13.29 MB
  29. 1102 Dealing with spurious minima.mp4 12.77 MB
  30. 1203 Restricted Boltzmann Machines.mp4 12.68 MB
  31. 0905 The Bayesian interpretation of weight decay.mp4 12.27 MB
  32. 0904 Introduction to the full Bayesian approach.mp4 12.00 MB
  33. 1301 The ups and downs of back propagation.mp4 11.83 MB
  34. 1104 Using stochastic units to improv search.mp4 11.77 MB
  35. 1505 Learning binary codes for image retrieval.mp4 11.51 MB
  36. 1103 Hopfield nets with hidden units.mp4 11.31 MB
  37. 1402 Discriminative learning for DBNs.mp4 11.29 MB
  38. 0804 Echo State Networks.mp4 11.28 MB
  39. 1404 Modeling real-valued data with an RBM.mp4 11.20 MB
  40. 1602 OPTIONAL_ Hierarchical Coordinate Frames.mp4 11.16 MB
  41. 0305 Using the derivatives computed by backpropagation.mp4 11.15 MB
  42. 1504 Semantic Hashing.mp4 10.97 MB
  43. 1503 Deep auto encoders for document retrieval.mp4 10.25 MB
  44. 0705 Long-term Short-term-memory.mp4 10.23 MB
  45. 1403 What happens during discriminative fine-tuning_.mp4 10.18 MB
  46. 0202 Perceptrons_ The first generation of neural networks.mp4 9.78 MB
  47. 0102 What are neural networks_.mp4 9.76 MB
  48. 0603 The momentum method.mp4 9.74 MB
  49. 1005 Dropout.mp4 9.69 MB
  50. 1501 From PCA to autoencoders.mp4 9.68 MB
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