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neural_nets_hinton

  • 收录时间:2018-03-06 00:06:25
  • 文件大小:885MB
  • 下载次数:280
  • 最近下载:2021-01-12 13:34:46
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文件列表

  1. 5 - 4 - Convolutional nets for object recognition [17min].mp4 23MB
  2. 7 - 1 - Modeling sequences- A brief overview.mp4 20MB
  3. 14 - 1 - Learning layers of features by stacking RBMs [17 min].mp4 20MB
  4. 14 - 5 - OPTIONAL VIDEO- RBMs are infinite sigmoid belief nets [17 mins].mp4 19MB
  5. 5 - 3 - Convolutional nets for digit recognition [16 min].mp4 18MB
  6. 12 - 2 - OPTIONAL VIDEO- More efficient ways to get the statistics [15 mins].mp4 17MB
  7. 2 - 5 - What perceptrons can-'t do [15 min].mp4 17MB
  8. 8 - 2 - Modeling character strings with multiplicative connections [14 mins].mp4 17MB
  9. 8 - 1 - A brief overview of Hessian Free optimization.mp4 16MB
  10. 16 - 3 - OPTIONAL- Bayesian optimization of hyper-parameters [13 min].mp4 16MB
  11. 13 - 4 - The wake-sleep algorithm [13 min].mp4 16MB
  12. 10 - 1 - Why it helps to combine models [13 min].mp4 15MB
  13. 6 - 5 - Rmsprop- Divide the gradient by a running average of its recent magnitude.mp4 15MB
  14. 1 - 1 - Why do we need machine learning- [13 min].mp4 15MB
  15. 10 - 2 - Mixtures of Experts [13 min].mp4 15MB
  16. 6 - 2 - A bag of tricks for mini-batch gradient descent.mp4 15MB
  17. 13 - 2 - Belief Nets [13 min].mp4 15MB
  18. 11 - 1 - Hopfield Nets [13 min].mp4 15MB
  19. 4 - 1 - Learning to predict the next word [13 min].mp4 14MB
  20. 4 - 5 - Ways to deal with the large number of possible outputs [15 min].mp4 14MB
  21. 12 - 1 - Boltzmann machine learning [12 min].mp4 14MB
  22. 8 - 3 - Learning to predict the next character using HF [12 mins].mp4 14MB
  23. 16 - 1 - OPTIONAL- Learning a joint model of images and captions [10 min].mp4 14MB
  24. 13 - 3 - Learning sigmoid belief nets [12 min].mp4 14MB
  25. 9 - 1 - Overview of ways to improve generalization [12 min].mp4 14MB
  26. 3 - 1 - Learning the weights of a linear neuron [12 min].mp4 14MB
  27. 3 - 4 - The backpropagation algorithm [12 min].mp4 13MB
  28. 11 - 5 - How a Boltzmann machine models data [12 min].mp4 13MB
  29. 11 - 2 - Dealing with spurious minima [11 min].mp4 13MB
  30. 12 - 3 - Restricted Boltzmann Machines [11 min].mp4 13MB
  31. 9 - 5 - The Bayesian interpretation of weight decay [11 min].mp4 12MB
  32. 9 - 4 - Introduction to the full Bayesian approach [12 min].mp4 12MB
  33. 13 - 1 - The ups and downs of back propagation [10 min].mp4 12MB
  34. 11 - 4 - Using stochastic units to improv search [11 min].mp4 12MB
  35. 15 - 5 - Learning binary codes for image retrieval [9 mins].mp4 12MB
  36. 11 - 3 - Hopfield nets with hidden units [10 min].mp4 11MB
  37. 14 - 2 - Discriminative learning for DBNs [9 mins].mp4 11MB
  38. 8 - 4 - Echo State Networks [9 min].mp4 11MB
  39. 14 - 4 - Modeling real-valued data with an RBM [10 mins].mp4 11MB
  40. 16 - 2 - OPTIONAL- Hierarchical Coordinate Frames [10 mins].mp4 11MB
  41. 3 - 5 - Using the derivatives computed by backpropagation [10 min].mp4 11MB
  42. 15 - 3 - Deep auto encoders for document retrieval [8 mins].mp4 10MB
  43. 7 - 5 - Long-term Short-term-memory.mp4 10MB
  44. 14 - 3 - What happens during discriminative fine-tuning- [8 mins].mp4 10MB
  45. 15 - 4 - Semantic Hashing [9 mins].mp4 10MB
  46. 1 - 2 - What are neural networks- [8 min].mp4 10MB
  47. 6 - 3 - The momentum method.mp4 10MB
  48. 10 - 5 - Dropout [9 min].mp4 10MB
  49. 15 - 1 - From PCA to autoencoders [5 mins].mp4 10MB
  50. 6 - 1 - Overview of mini-batch gradient descent.mp4 10MB
  51. 12 - 5 - RBMs for collaborative filtering [8 mins].mp4 10MB
  52. 2 - 2 - Perceptrons- The first generation of neural networks [8 min].mp4 9MB
  53. 1 - 3 - Some simple models of neurons [8 min].mp4 9MB
  54. 1 - 5 - Three types of learning [8 min].mp4 9MB
  55. 4 - 4 - Neuro-probabilistic language models [8 min].mp4 9MB
  56. 7 - 4 - Why it is difficult to train an RNN.mp4 9MB
  57. 2 - 1 - Types of neural network architectures [7 min].mp4 9MB
  58. 12 - 4 - An example of RBM learning [7 mins].mp4 9MB
  59. 9 - 3 - Using noise as a regularizer [7 min].mp4 8MB
  60. 10 - 3 - The idea of full Bayesian learning [7 min].mp4 8MB
  61. 15 - 6 - Shallow autoencoders for pre-training [7 mins].mp4 8MB
  62. 10 - 4 - Making full Bayesian learning practical [7 min].mp4 8MB
  63. 4 - 3 - Another diversion- The softmax output function [7 min].mp4 8MB
  64. 9 - 2 - Limiting the size of the weights [6 min].mp4 7MB
  65. 7 - 2 - Training RNNs with back propagation.mp4 7MB
  66. 2 - 3 - A geometrical view of perceptrons [6 min].mp4 7MB
  67. 7 - 3 - A toy example of training an RNN.mp4 7MB
  68. 5 - 2 - Achieving viewpoint invariance [6 min].mp4 7MB
  69. 6 - 4 - Adaptive learning rates for each connection.mp4 7MB
  70. 1 - 4 - A simple example of learning [6 min].mp4 7MB
  71. 2 - 4 - Why the learning works [5 min].mp4 6MB
  72. 3 - 2 - The error surface for a linear neuron [5 min].mp4 6MB
  73. 5 - 1 - Why object recognition is difficult [5 min].mp4 5MB
  74. 4 - 2 - A brief diversion into cognitive science [4 min].mp4 5MB
  75. 15 - 2 - Deep auto encoders [4 mins].mp4 5MB
  76. 9 - 6 - MacKay-'s quick and dirty method of setting weight costs [4 min].mp4 4MB
  77. 3 - 3 - Learning the weights of a logistic output neuron [4 min].mp4 4MB
  78. 16 - 4 - OPTIONAL- The fog of progress [3 min].mp4 3MB