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[Coursera] Neural Networks for Machine Learning by Geoffrey Hinton

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  • 文件大小:935MB
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文件列表

  1. 05_Lecture5/04_Convolutional_nets_for_object_recognition_17min.mp4 23MB
  2. 07_Lecture7/01_Modeling_sequences-_A_brief_overview.mp4 20MB
  3. 14_Lecture14/01_Learning_layers_of_features_by_stacking_RBMs_17_min.mp4 20MB
  4. 14_Lecture14/05_OPTIONAL_VIDEO-_RBMs_are_infinite_sigmoid_belief_nets_17_mins.mp4 19MB
  5. 05_Lecture5/03_Convolutional_nets_for_digit_recognition_16_min.mp4 18MB
  6. 12_Lecture12/02_OPTIONAL_VIDEO-_More_efficient_ways_to_get_the_statistics_15_mins.mp4 17MB
  7. 02_Lecture2/05_What_perceptrons_cant_do_15_min.mp4 17MB
  8. 08_Lecture8/02_Modeling_character_strings_with_multiplicative_connections_14_mins.mp4 17MB
  9. 08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.mp4 16MB
  10. 16_Lecture16/03_OPTIONAL-_Bayesian_optimization_of_hyper-parameters_13_min.mp4 16MB
  11. 13_Lecture13/04_The_wake-sleep_algorithm_13_min.mp4 16MB
  12. 10_Lecture10/01_Why_it_helps_to_combine_models_13_min.mp4 15MB
  13. 06_Lecture6/05_Rmsprop-_Divide_the_gradient_by_a_running_average_of_its_recent_magnitude.mp4 15MB
  14. 01_Lecture1/01_Why_do_we_need_machine_learning_13_min.mp4 15MB
  15. 10_Lecture10/02_Mixtures_of_Experts_13_min.mp4 15MB
  16. 06_Lecture6/02_A_bag_of_tricks_for_mini-batch_gradient_descent.mp4 15MB
  17. 13_Lecture13/02_Belief_Nets_13_min.mp4 15MB
  18. 11_Lecture11/01_Hopfield_Nets_13_min.mp4 15MB
  19. 04_Lecture4/01_Learning_to_predict_the_next_word_13_min.mp4 14MB
  20. 04_Lecture4/05_Ways_to_deal_with_the_large_number_of_possible_outputs_15_min.mp4 14MB
  21. 12_Lecture12/01_Boltzmann_machine_learning_12_min.mp4 14MB
  22. 08_Lecture8/03_Learning_to_predict_the_next_character_using_HF_12__mins.mp4 14MB
  23. 16_Lecture16/01_OPTIONAL-_Learning_a_joint_model_of_images_and_captions_10_min.mp4 14MB
  24. 13_Lecture13/03_Learning_sigmoid_belief_nets_12_min.mp4 14MB
  25. 09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.mp4 14MB
  26. 03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.mp4 14MB
  27. 03_Lecture3/04_The_backpropagation_algorithm_12_min.mp4 13MB
  28. 11_Lecture11/05_How_a_Boltzmann_machine_models_data_12_min.mp4 13MB
  29. 11_Lecture11/02_Dealing_with_spurious_minima_11_min.mp4 13MB
  30. 12_Lecture12/03_Restricted_Boltzmann_Machines_11_min.mp4 13MB
  31. 09_Lecture9/05_The_Bayesian_interpretation_of_weight_decay_11_min.mp4 12MB
  32. 09_Lecture9/04_Introduction_to_the_full_Bayesian_approach_12_min.mp4 12MB
  33. 13_Lecture13/01_The_ups_and_downs_of_back_propagation_10_min.mp4 12MB
  34. 11_Lecture11/04_Using_stochastic_units_to_improv_search_11_min.mp4 12MB
  35. 15_Lecture15/05_Learning_binary_codes_for_image_retrieval_9_mins.mp4 12MB
  36. 11_Lecture11/03_Hopfield_nets_with_hidden_units_10_min.mp4 11MB
  37. 14_Lecture14/02_Discriminative_learning_for_DBNs_9_mins.mp4 11MB
  38. 08_Lecture8/04_Echo_State_Networks_9_min.mp4 11MB
  39. 14_Lecture14/04_Modeling_real-valued_data_with_an_RBM_10_mins.mp4 11MB
  40. 16_Lecture16/02_OPTIONAL-_Hierarchical_Coordinate_Frames_10_mins.mp4 11MB
  41. 03_Lecture3/05_Using_the_derivatives_computed_by_backpropagation_10_min.mp4 11MB
  42. 15_Lecture15/03_Deep_auto_encoders_for_document_retrieval_8_mins.mp4 10MB
  43. 07_Lecture7/05_Long-term_Short-term-memory.mp4 10MB
  44. 14_Lecture14/03_What_happens_during_discriminative_fine-tuning_8_mins.mp4 10MB
  45. 15_Lecture15/04_Semantic_Hashing_9_mins.mp4 10MB
  46. 01_Lecture1/02_What_are_neural_networks_8_min.mp4 10MB
  47. 06_Lecture6/03_The_momentum_method.mp4 10MB
  48. 10_Lecture10/05_Dropout_9_min.mp4 10MB
  49. 15_Lecture15/01_From_PCA_to_autoencoders_5_mins.mp4 10MB
  50. 06_Lecture6/01_Overview_of_mini-batch_gradient_descent.mp4 10MB
  51. 12_Lecture12/05_RBMs_for_collaborative_filtering_8_mins.mp4 10MB
  52. 02_Lecture2/02_Perceptrons-_The_first_generation_of_neural_networks_8_min.mp4 9MB
  53. 01_Lecture1/03_Some_simple_models_of_neurons_8_min.mp4 9MB
  54. 01_Lecture1/05_Three_types_of_learning_8_min.mp4 9MB
  55. 04_Lecture4/04_Neuro-probabilistic_language_models_8_min.mp4 9MB
  56. 07_Lecture7/04_Why_it_is_difficult_to_train_an_RNN.mp4 9MB
  57. 02_Lecture2/01_Types_of_neural_network_architectures_7_min.mp4 9MB
  58. 12_Lecture12/04_An_example_of_RBM_learning_7_mins.mp4 9MB
  59. 09_Lecture9/03_Using_noise_as_a_regularizer_7_min.mp4 8MB
  60. 10_Lecture10/03_The_idea_of_full_Bayesian_learning_7_min.mp4 8MB
  61. 15_Lecture15/06_Shallow_autoencoders_for_pre-training_7_mins.mp4 8MB
  62. 10_Lecture10/04_Making_full_Bayesian_learning_practical_7_min.mp4 8MB
  63. 04_Lecture4/03_Another_diversion-_The_softmax_output_function_7_min.mp4 8MB
  64. 09_Lecture9/02_Limiting_the_size_of_the_weights_6_min.mp4 7MB
  65. 07_Lecture7/02_Training_RNNs_with_back_propagation.mp4 7MB
  66. 02_Lecture2/03_A_geometrical_view_of_perceptrons_6_min.mp4 7MB
  67. 07_Lecture7/03_A_toy_example_of_training_an_RNN.mp4 7MB
  68. 05_Lecture5/02_Achieving_viewpoint_invariance_6_min.mp4 7MB
  69. 06_Lecture6/04_Adaptive_learning_rates_for_each_connection.mp4 7MB
  70. 01_Lecture1/04_A_simple_example_of_learning_6_min.mp4 7MB
  71. 02_Lecture2/04_Why_the_learning_works_5_min.mp4 6MB
  72. 03_Lecture3/02_The_error_surface_for_a_linear_neuron_5_min.mp4 6MB
  73. 05_Lecture5/01_Why_object_recognition_is_difficult_5_min.mp4 5MB
  74. 04_Lecture4/02_A_brief_diversion_into_cognitive_science_4_min.mp4 5MB
  75. 15_Lecture15/02_Deep_auto_encoders_4_mins.mp4 5MB
  76. 09_Lecture9/06_MacKays_quick_and_dirty_method_of_setting_weight_costs_4_min.mp4 4MB
  77. 03_Lecture3/03_Learning_the_weights_of_a_logistic_output_neuron_4_min.mp4 4MB
  78. 01_Lecture1/01_Why_do_we_need_machine_learning_13_min.pdf 4MB
  79. 01_Lecture1/01_Why_do_we_need_machine_learning_13_min.pptx 4MB
  80. 03_Lecture3/04_The_backpropagation_algorithm_12_min.pdf 3MB
  81. 16_Lecture16/04_OPTIONAL-_The_fog_of_progress_3_min.mp4 3MB
  82. 15_Lecture15/01_From_PCA_to_autoencoders_5_mins.pdf 2MB
  83. 13_Lecture13/03_Learning_sigmoid_belief_nets_12_min.pdf 2MB
  84. 12_Lecture12/01_Boltzmann_machine_learning_12_min.pptx 2MB
  85. 15_Lecture15/01_From_PCA_to_autoencoders_5_mins.pptx 2MB
  86. 12_Lecture12/01_Boltzmann_machine_learning_12_min.pdf 2MB
  87. 05_Lecture5/01_Why_object_recognition_is_difficult_5_min.pptx 2MB
  88. 10_Lecture10/05_Dropout_9_min.pdf 2MB
  89. 05_Lecture5/01_Why_object_recognition_is_difficult_5_min.pdf 2MB
  90. 09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.pptx 1MB
  91. 14_Lecture14/01_Learning_layers_of_features_by_stacking_RBMs_17_min.pptx 1MB
  92. 03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.pptx 1MB
  93. 14_Lecture14/01_Learning_layers_of_features_by_stacking_RBMs_17_min.pdf 1MB
  94. 04_Lecture4/01_Learning_to_predict_the_next_word_13_min.pptx 1MB
  95. 07_Lecture7/01_Modeling_sequences-_A_brief_overview.pdf 953KB
  96. 04_Lecture4/01_Learning_to_predict_the_next_word_13_min.pdf 941KB
  97. 05_Lecture5/04_Convolutional_nets_for_object_recognition_17min_0_.pdf 933KB
  98. 10_Lecture10/01_Why_it_helps_to_combine_models_13_min.pptx 880KB
  99. 10_Lecture10/01_Why_it_helps_to_combine_models_13_min.pdf 827KB
  100. 14_Lecture14/01_Learning_layers_of_features_by_stacking_RBMs_17_min_2_.pdf 769KB
  101. 15_Lecture15/05_Learning_binary_codes_for_image_retrieval_9_mins.pdf 741KB
  102. 11_Lecture11/01_Hopfield_Nets_13_min.pptx 726KB
  103. 09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.pdf 702KB
  104. 11_Lecture11/01_Hopfield_Nets_13_min.pdf 695KB
  105. 06_Lecture6/01_Overview_of_mini-batch_gradient_descent.pptx 657KB
  106. 08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.pdf 643KB
  107. 15_Lecture15/04_Semantic_Hashing_9_mins.pdf 627KB
  108. 08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.pptx 555KB
  109. 03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.pdf 535KB
  110. 06_Lecture6/01_Overview_of_mini-batch_gradient_descent.pdf 534KB
  111. 14_Lecture14/01_Learning_layers_of_features_by_stacking_RBMs_17_min_1_.pdf 502KB
  112. 02_Lecture2/01_Types_of_neural_network_architectures_7_min.pdf 493KB
  113. 14_Lecture14/01_Learning_layers_of_features_by_stacking_RBMs_17_min_0_Self-taught_learning-_transfer_learning_from_unlabeled_data.pdf 474KB
  114. 13_Lecture13/01_The_ups_and_downs_of_back_propagation_10_min.pptx 415KB
  115. 02_Lecture2/01_Types_of_neural_network_architectures_7_min.pptx 400KB
  116. 16_Lecture16/01_OPTIONAL-_Learning_a_joint_model_of_images_and_captions_10_min.pdf 339KB
  117. 16_Lecture16/04_OPTIONAL-_The_fog_of_progress_3_min.pdf 339KB
  118. 16_Lecture16/01_OPTIONAL-_Learning_a_joint_model_of_images_and_captions_10_min.pptx 336KB
  119. 16_Lecture16/04_OPTIONAL-_The_fog_of_progress_3_min.pptx 336KB
  120. 07_Lecture7/05_Long-term_Short-term-memory.pdf 313KB
  121. 13_Lecture13/01_The_ups_and_downs_of_back_propagation_10_min.pdf 307KB
  122. 08_Lecture8/03_Learning_to_predict_the_next_character_using_HF_12__mins.pdf 267KB
  123. 10_Lecture10/02_Mixtures_of_Experts_13_min.pdf 265KB
  124. 13_Lecture13/04_The_wake-sleep_algorithm_13_min.pdf 255KB
  125. 07_Lecture7/01_Modeling_sequences-_A_brief_overview.pptx 223KB
  126. 04_Lecture4/05_Ways_to_deal_with_the_large_number_of_possible_outputs_15_min.png 151KB
  127. 04_Lecture4/04_Neuro-probabilistic_language_models_8_min.pdf 137KB
  128. 05_Lecture5/04_Convolutional_nets_for_object_recognition_17min.pdf 122KB
  129. 05_Lecture5/04_Convolutional_nets_for_object_recognition_17min.srt 26KB
  130. 14_Lecture14/01_Learning_layers_of_features_by_stacking_RBMs_17_min.srt 23KB
  131. 07_Lecture7/01_Modeling_sequences-_A_brief_overview.srt 23KB
  132. 14_Lecture14/05_OPTIONAL_VIDEO-_RBMs_are_infinite_sigmoid_belief_nets_17_mins.srt 22KB
  133. 05_Lecture5/03_Convolutional_nets_for_digit_recognition_16_min.srt 22KB
  134. 06_Lecture6/02_A_bag_of_tricks_for_mini-batch_gradient_descent.srt 19KB
  135. 16_Lecture16/03_OPTIONAL-_Bayesian_optimization_of_hyper-parameters_13_min.srt 19KB
  136. 02_Lecture2/05_What_perceptrons_cant_do_15_min.srt 19KB
  137. 01_Lecture1/01_Why_do_we_need_machine_learning_13_min.srt 18KB
  138. 12_Lecture12/02_OPTIONAL_VIDEO-_More_efficient_ways_to_get_the_statistics_15_mins.srt 18KB
  139. 04_Lecture4/05_Ways_to_deal_with_the_large_number_of_possible_outputs_15_min.srt 18KB
  140. 08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.srt 18KB
  141. 10_Lecture10/01_Why_it_helps_to_combine_models_13_min.srt 18KB
  142. 08_Lecture8/02_Modeling_character_strings_with_multiplicative_connections_14_mins.srt 17KB
  143. 13_Lecture13/04_The_wake-sleep_algorithm_13_min.srt 17KB
  144. 13_Lecture13/02_Belief_Nets_13_min.srt 17KB
  145. 10_Lecture10/02_Mixtures_of_Experts_13_min.srt 17KB
  146. 05_Lecture5/04_Convolutional_nets_for_object_recognition_17min.txt 17KB
  147. 04_Lecture4/01_Learning_to_predict_the_next_word_13_min.srt 16KB
  148. 11_Lecture11/01_Hopfield_Nets_13_min.srt 16KB
  149. 12_Lecture12/01_Boltzmann_machine_learning_12_min.srt 16KB
  150. 11_Lecture11/05_How_a_Boltzmann_machine_models_data_12_min.srt 16KB
  151. 09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.srt 16KB
  152. 08_Lecture8/03_Learning_to_predict_the_next_character_using_HF_12__mins.srt 16KB
  153. 06_Lecture6/05_Rmsprop-_Divide_the_gradient_by_a_running_average_of_its_recent_magnitude.srt 16KB
  154. 14_Lecture14/01_Learning_layers_of_features_by_stacking_RBMs_17_min.txt 15KB
  155. 03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.srt 15KB
  156. 03_Lecture3/04_The_backpropagation_algorithm_12_min.srt 15KB
  157. 11_Lecture11/02_Dealing_with_spurious_minima_11_min.srt 15KB
  158. 07_Lecture7/01_Modeling_sequences-_A_brief_overview.txt 15KB
  159. 13_Lecture13/03_Learning_sigmoid_belief_nets_12_min.srt 15KB
  160. 14_Lecture14/05_OPTIONAL_VIDEO-_RBMs_are_infinite_sigmoid_belief_nets_17_mins.txt 14KB
  161. 11_Lecture11/04_Using_stochastic_units_to_improv_search_11_min.srt 14KB
  162. 05_Lecture5/03_Convolutional_nets_for_digit_recognition_16_min.txt 14KB
  163. 13_Lecture13/01_The_ups_and_downs_of_back_propagation_10_min.srt 14KB
  164. 12_Lecture12/03_Restricted_Boltzmann_Machines_11_min.srt 14KB
  165. 03_Lecture3/05_Using_the_derivatives_computed_by_backpropagation_10_min.srt 14KB
  166. 16_Lecture16/02_OPTIONAL-_Hierarchical_Coordinate_Frames_10_mins.srt 13KB
  167. 09_Lecture9/04_Introduction_to_the_full_Bayesian_approach_12_min.srt 13KB
  168. 09_Lecture9/05_The_Bayesian_interpretation_of_weight_decay_11_min.srt 13KB
  169. 15_Lecture15/05_Learning_binary_codes_for_image_retrieval_9_mins.srt 13KB
  170. 14_Lecture14/02_Discriminative_learning_for_DBNs_9_mins.srt 13KB
  171. 16_Lecture16/03_OPTIONAL-_Bayesian_optimization_of_hyper-parameters_13_min.txt 12KB
  172. 11_Lecture11/03_Hopfield_nets_with_hidden_units_10_min.srt 12KB
  173. 06_Lecture6/02_A_bag_of_tricks_for_mini-batch_gradient_descent.txt 12KB
  174. 14_Lecture14/04_Modeling_real-valued_data_with_an_RBM_10_mins.srt 12KB
  175. 02_Lecture2/05_What_perceptrons_cant_do_15_min.txt 12KB
  176. 08_Lecture8/04_Echo_State_Networks_9_min.srt 12KB
  177. 06_Lecture6/01_Overview_of_mini-batch_gradient_descent.srt 12KB
  178. 12_Lecture12/02_OPTIONAL_VIDEO-_More_efficient_ways_to_get_the_statistics_15_mins.txt 12KB
  179. 01_Lecture1/01_Why_do_we_need_machine_learning_13_min.txt 12KB
  180. 04_Lecture4/05_Ways_to_deal_with_the_large_number_of_possible_outputs_15_min.txt 12KB
  181. 10_Lecture10/05_Dropout_9_min.srt 12KB
  182. 08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.txt 12KB
  183. 07_Lecture7/05_Long-term_Short-term-memory.srt 12KB
  184. 08_Lecture8/02_Modeling_character_strings_with_multiplicative_connections_14_mins.txt 12KB
  185. 01_Lecture1/02_What_are_neural_networks_8_min.srt 12KB
  186. 10_Lecture10/01_Why_it_helps_to_combine_models_13_min.txt 11KB
  187. 15_Lecture15/04_Semantic_Hashing_9_mins.srt 11KB
  188. 13_Lecture13/02_Belief_Nets_13_min.txt 11KB
  189. 13_Lecture13/04_The_wake-sleep_algorithm_13_min.txt 11KB
  190. 10_Lecture10/02_Mixtures_of_Experts_13_min.txt 11KB
  191. 06_Lecture6/03_The_momentum_method.srt 11KB
  192. 02_Lecture2/02_Perceptrons-_The_first_generation_of_neural_networks_8_min.srt 11KB
  193. 04_Lecture4/01_Learning_to_predict_the_next_word_13_min.txt 11KB
  194. 04_Lecture4/04_Neuro-probabilistic_language_models_8_min.srt 11KB
  195. 01_Lecture1/03_Some_simple_models_of_neurons_8_min.srt 11KB
  196. 12_Lecture12/05_RBMs_for_collaborative_filtering_8_mins.srt 11KB
  197. 14_Lecture14/03_What_happens_during_discriminative_fine-tuning_8_mins.srt 11KB
  198. 11_Lecture11/01_Hopfield_Nets_13_min.txt 11KB
  199. 15_Lecture15/03_Deep_auto_encoders_for_document_retrieval_8_mins.srt 11KB
  200. 12_Lecture12/01_Boltzmann_machine_learning_12_min.txt 10KB
  201. 01_Lecture1/05_Three_types_of_learning_8_min.srt 10KB
  202. 09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.txt 10KB
  203. 16_Lecture16/01_OPTIONAL-_Learning_a_joint_model_of_images_and_captions_10_min.srt 10KB
  204. 11_Lecture11/05_How_a_Boltzmann_machine_models_data_12_min.txt 10KB
  205. 10_Lecture10/03_The_idea_of_full_Bayesian_learning_7_min.srt 10KB
  206. 15_Lecture15/01_From_PCA_to_autoencoders_5_mins.srt 10KB
  207. 06_Lecture6/05_Rmsprop-_Divide_the_gradient_by_a_running_average_of_its_recent_magnitude.txt 10KB
  208. 08_Lecture8/03_Learning_to_predict_the_next_character_using_HF_12__mins.txt 10KB
  209. 15_Lecture15/06_Shallow_autoencoders_for_pre-training_7_mins.srt 10KB
  210. 03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.txt 10KB
  211. 12_Lecture12/04_An_example_of_RBM_learning_7_mins.srt 10KB
  212. 02_Lecture2/01_Types_of_neural_network_architectures_7_min.srt 10KB
  213. 07_Lecture7/04_Why_it_is_difficult_to_train_an_RNN.srt 10KB
  214. 11_Lecture11/02_Dealing_with_spurious_minima_11_min.txt 10KB
  215. 03_Lecture3/04_The_backpropagation_algorithm_12_min.txt 10KB
  216. 13_Lecture13/03_Learning_sigmoid_belief_nets_12_min.txt 10KB
  217. 11_Lecture11/04_Using_stochastic_units_to_improv_search_11_min.txt 9KB
  218. 04_Lecture4/03_Another_diversion-_The_softmax_output_function_7_min.srt 9KB
  219. 16_Lecture16/02_OPTIONAL-_Hierarchical_Coordinate_Frames_10_mins.txt 9KB
  220. 03_Lecture3/05_Using_the_derivatives_computed_by_backpropagation_10_min.txt 9KB
  221. 12_Lecture12/03_Restricted_Boltzmann_Machines_11_min.txt 9KB
  222. 09_Lecture9/03_Using_noise_as_a_regularizer_7_min.srt 9KB
  223. 13_Lecture13/01_The_ups_and_downs_of_back_propagation_10_min.txt 9KB
  224. 15_Lecture15/05_Learning_binary_codes_for_image_retrieval_9_mins.txt 9KB
  225. 09_Lecture9/05_The_Bayesian_interpretation_of_weight_decay_11_min.txt 9KB
  226. 09_Lecture9/04_Introduction_to_the_full_Bayesian_approach_12_min.txt 9KB
  227. 14_Lecture14/02_Discriminative_learning_for_DBNs_9_mins.txt 8KB
  228. 10_Lecture10/04_Making_full_Bayesian_learning_practical_7_min.srt 8KB
  229. 09_Lecture9/02_Limiting_the_size_of_the_weights_6_min.srt 8KB
  230. 07_Lecture7/02_Training_RNNs_with_back_propagation.srt 8KB
  231. 02_Lecture2/03_A_geometrical_view_of_perceptrons_6_min.srt 8KB
  232. 14_Lecture14/04_Modeling_real-valued_data_with_an_RBM_10_mins.txt 8KB
  233. 05_Lecture5/02_Achieving_viewpoint_invariance_6_min.srt 8KB
  234. 11_Lecture11/03_Hopfield_nets_with_hidden_units_10_min.txt 8KB
  235. 08_Lecture8/04_Echo_State_Networks_9_min.txt 8KB
  236. 06_Lecture6/01_Overview_of_mini-batch_gradient_descent.txt 8KB
  237. 06_Lecture6/04_Adaptive_learning_rates_for_each_connection.srt 8KB
  238. 07_Lecture7/05_Long-term_Short-term-memory.txt 8KB
  239. 15_Lecture15/04_Semantic_Hashing_9_mins.txt 8KB
  240. 10_Lecture10/05_Dropout_9_min.txt 8KB
  241. 07_Lecture7/03_A_toy_example_of_training_an_RNN.srt 8KB
  242. 01_Lecture1/02_What_are_neural_networks_8_min.txt 7KB
  243. 06_Lecture6/03_The_momentum_method.txt 7KB
  244. 14_Lecture14/03_What_happens_during_discriminative_fine-tuning_8_mins.txt 7KB
  245. 02_Lecture2/02_Perceptrons-_The_first_generation_of_neural_networks_8_min.txt 7KB
  246. 15_Lecture15/03_Deep_auto_encoders_for_document_retrieval_8_mins.txt 7KB
  247. 01_Lecture1/04_A_simple_example_of_learning_6_min.srt 7KB
  248. 12_Lecture12/05_RBMs_for_collaborative_filtering_8_mins.txt 7KB
  249. 04_Lecture4/04_Neuro-probabilistic_language_models_8_min.txt 7KB
  250. 16_Lecture16/01_OPTIONAL-_Learning_a_joint_model_of_images_and_captions_10_min.txt 7KB
  251. 01_Lecture1/03_Some_simple_models_of_neurons_8_min.txt 7KB
  252. 15_Lecture15/01_From_PCA_to_autoencoders_5_mins.txt 7KB
  253. 01_Lecture1/05_Three_types_of_learning_8_min.txt 7KB
  254. 15_Lecture15/06_Shallow_autoencoders_for_pre-training_7_mins.txt 7KB
  255. 10_Lecture10/03_The_idea_of_full_Bayesian_learning_7_min.txt 7KB
  256. 12_Lecture12/04_An_example_of_RBM_learning_7_mins.txt 6KB
  257. 02_Lecture2/01_Types_of_neural_network_architectures_7_min.txt 6KB
  258. 07_Lecture7/04_Why_it_is_difficult_to_train_an_RNN.txt 6KB
  259. 02_Lecture2/04_Why_the_learning_works_5_min.srt 6KB
  260. 03_Lecture3/02_The_error_surface_for_a_linear_neuron_5_min.srt 6KB
  261. 05_Lecture5/01_Why_object_recognition_is_difficult_5_min.srt 6KB
  262. 04_Lecture4/03_Another_diversion-_The_softmax_output_function_7_min.txt 6KB
  263. 09_Lecture9/03_Using_noise_as_a_regularizer_7_min.txt 6KB
  264. 04_Lecture4/02_A_brief_diversion_into_cognitive_science_4_min.srt 6KB
  265. 10_Lecture10/04_Making_full_Bayesian_learning_practical_7_min.txt 6KB
  266. 07_Lecture7/02_Training_RNNs_with_back_propagation.txt 6KB
  267. 09_Lecture9/02_Limiting_the_size_of_the_weights_6_min.txt 5KB
  268. 02_Lecture2/03_A_geometrical_view_of_perceptrons_6_min.txt 5KB
  269. 15_Lecture15/02_Deep_auto_encoders_4_mins.srt 5KB
  270. 05_Lecture5/02_Achieving_viewpoint_invariance_6_min.txt 5KB
  271. 06_Lecture6/04_Adaptive_learning_rates_for_each_connection.txt 5KB
  272. 07_Lecture7/03_A_toy_example_of_training_an_RNN.txt 5KB
  273. 01_Lecture1/04_A_simple_example_of_learning_6_min.txt 5KB
  274. 03_Lecture3/03_Learning_the_weights_of_a_logistic_output_neuron_4_min.srt 4KB
  275. 09_Lecture9/06_MacKays_quick_and_dirty_method_of_setting_weight_costs_4_min.srt 4KB
  276. 02_Lecture2/04_Why_the_learning_works_5_min.txt 4KB
  277. 03_Lecture3/02_The_error_surface_for_a_linear_neuron_5_min.txt 4KB
  278. 05_Lecture5/01_Why_object_recognition_is_difficult_5_min.txt 4KB
  279. 04_Lecture4/02_A_brief_diversion_into_cognitive_science_4_min.txt 4KB
  280. 15_Lecture15/02_Deep_auto_encoders_4_mins.txt 4KB
  281. 16_Lecture16/04_OPTIONAL-_The_fog_of_progress_3_min.srt 3KB
  282. 03_Lecture3/03_Learning_the_weights_of_a_logistic_output_neuron_4_min.txt 3KB
  283. 09_Lecture9/06_MacKays_quick_and_dirty_method_of_setting_weight_costs_4_min.txt 3KB
  284. 16_Lecture16/04_OPTIONAL-_The_fog_of_progress_3_min.txt 2KB