Coursera Neural Networks for Machine Learning by Geoffrey Hinton

1. 01_Lecture1/01_Why_do_we_need_machine_learning_13_min.mp415.05MB
2. 01_Lecture1/02_What_are_neural_networks_8_min.mp49.76MB
3. 01_Lecture1/03_Some_simple_models_of_neurons_8_min.mp49.26MB
4. 01_Lecture1/04_A_simple_example_of_learning_6_min.mp46.57MB
5. 01_Lecture1/05_Three_types_of_learning_8_min.mp48.96MB
6. 02_Lecture2/01_Types_of_neural_network_architectures_7_min.mp48.78MB
7. 02_Lecture2/02_Perceptrons-_The_first_generation_of_neural_networks_8_min.mp49.39MB
8. 02_Lecture2/03_A_geometrical_view_of_perceptrons_6_min.mp47.32MB
9. 02_Lecture2/04_Why_the_learning_works_5_min.mp45.9MB
10. 02_Lecture2/05_What_perceptrons_cant_do_15_min.mp416.57MB
11. 03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.mp413.52MB
12. 03_Lecture3/02_The_error_surface_for_a_linear_neuron_5_min.mp45.89MB
13. 03_Lecture3/03_Learning_the_weights_of_a_logistic_output_neuron_4_min.mp44.37MB
14. 03_Lecture3/04_The_backpropagation_algorithm_12_min.mp413.35MB
15. 03_Lecture3/05_Using_the_derivatives_computed_by_backpropagation_10_min.mp411.15MB
16. 04_Lecture4/01_Learning_to_predict_the_next_word_13_min.mp414.28MB
17. 04_Lecture4/02_A_brief_diversion_into_cognitive_science_4_min.mp45.31MB
18. 04_Lecture4/03_Another_diversion-_The_softmax_output_function_7_min.mp48.03MB
19. 04_Lecture4/04_Neuro-probabilistic_language_models_8_min.mp48.93MB
20. 04_Lecture4/05_Ways_to_deal_with_the_large_number_of_possible_outputs_15_min.mp414.26MB
21. 05_Lecture5/01_Why_object_recognition_is_difficult_5_min.mp45.37MB
22. 05_Lecture5/02_Achieving_viewpoint_invariance_6_min.mp46.89MB
23. 05_Lecture5/03_Convolutional_nets_for_digit_recognition_16_min.mp418.46MB
24. 05_Lecture5/04_Convolutional_nets_for_object_recognition_17min.mp423.03MB
25. 06_Lecture6/01_Overview_of_mini-batch_gradient_descent.mp49.6MB
26. 06_Lecture6/02_A_bag_of_tricks_for_mini-batch_gradient_descent.mp414.9MB
27. 06_Lecture6/03_The_momentum_method.mp49.74MB
28. 06_Lecture6/04_Adaptive_learning_rates_for_each_connection.mp46.63MB
29. 06_Lecture6/05_Rmsprop-_Divide_the_gradient_by_a_running_average_of_its_recent_magnitude.mp415.12MB
30. 07_Lecture7/01_Modeling_sequences-_A_brief_overview.mp420.13MB
31. 07_Lecture7/02_Training_RNNs_with_back_propagation.mp47.33MB
32. 07_Lecture7/03_A_toy_example_of_training_an_RNN.mp47.24MB
33. 07_Lecture7/04_Why_it_is_difficult_to_train_an_RNN.mp48.89MB
34. 07_Lecture7/05_Long-term_Short-term-memory.mp410.23MB
35. 08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.mp416.24MB
36. 08_Lecture8/02_Modeling_character_strings_with_multiplicative_connections_14_mins.mp416.56MB
37. 08_Lecture8/03_Learning_to_predict_the_next_character_using_HF_12__mins.mp413.92MB
38. 08_Lecture8/04_Echo_State_Networks_9_min.mp411.28MB
39. 09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.mp413.57MB
40. 09_Lecture9/02_Limiting_the_size_of_the_weights_6_min.mp47.36MB
41. 09_Lecture9/03_Using_noise_as_a_regularizer_7_min.mp48.48MB
42. 09_Lecture9/04_Introduction_to_the_full_Bayesian_approach_12_min.mp412MB
43. 09_Lecture9/05_The_Bayesian_interpretation_of_weight_decay_11_min.mp412.27MB
44. 09_Lecture9/06_MacKays_quick_and_dirty_method_of_setting_weight_costs_4_min.mp44.37MB
45. 10_Lecture10/01_Why_it_helps_to_combine_models_13_min.mp415.12MB
46. 10_Lecture10/02_Mixtures_of_Experts_13_min.mp414.98MB
47. 10_Lecture10/03_The_idea_of_full_Bayesian_learning_7_min.mp48.39MB
48. 10_Lecture10/04_Making_full_Bayesian_learning_practical_7_min.mp48.13MB
49. 10_Lecture10/05_Dropout_9_min.mp49.69MB
50. 11_Lecture11/01_Hopfield_Nets_13_min.mp414.65MB
51. 11_Lecture11/02_Dealing_with_spurious_minima_11_min.mp412.77MB
52. 11_Lecture11/03_Hopfield_nets_with_hidden_units_10_min.mp411.31MB
53. 11_Lecture11/04_Using_stochastic_units_to_improv_search_11_min.mp411.76MB
54. 11_Lecture11/05_How_a_Boltzmann_machine_models_data_12_min.mp413.28MB
55. 12_Lecture12/01_Boltzmann_machine_learning_12_min.mp414.03MB
56. 12_Lecture12/02_OPTIONAL_VIDEO-_More_efficient_ways_to_get_the_statistics_15_mins.mp416.93MB
57. 12_Lecture12/03_Restricted_Boltzmann_Machines_11_min.mp412.68MB
58. 12_Lecture12/04_An_example_of_RBM_learning_7_mins.mp48.71MB
59. 12_Lecture12/05_RBMs_for_collaborative_filtering_8_mins.mp49.53MB
60. 13_Lecture13/01_The_ups_and_downs_of_back_propagation_10_min.mp411.83MB
61. 13_Lecture13/02_Belief_Nets_13_min.mp414.86MB
62. 13_Lecture13/03_Learning_sigmoid_belief_nets_12_min.mp413.59MB
63. 13_Lecture13/04_The_wake-sleep_algorithm_13_min.mp415.68MB
64. 14_Lecture14/01_Learning_layers_of_features_by_stacking_RBMs_17_min.mp420.07MB
65. 14_Lecture14/02_Discriminative_learning_for_DBNs_9_mins.mp411.29MB
66. 14_Lecture14/03_What_happens_during_discriminative_fine-tuning_8_mins.mp410.17MB
67. 14_Lecture14/04_Modeling_real-valued_data_with_an_RBM_10_mins.mp411.2MB
68. 14_Lecture14/05_OPTIONAL_VIDEO-_RBMs_are_infinite_sigmoid_belief_nets_17_mins.mp419.44MB
69. 15_Lecture15/01_From_PCA_to_autoencoders_5_mins.mp49.68MB
70. 15_Lecture15/02_Deep_auto_encoders_4_mins.mp44.92MB
71. 15_Lecture15/03_Deep_auto_encoders_for_document_retrieval_8_mins.mp410.25MB
72. 15_Lecture15/04_Semantic_Hashing_9_mins.mp49.99MB
73. 15_Lecture15/05_Learning_binary_codes_for_image_retrieval_9_mins.mp411.51MB
74. 15_Lecture15/06_Shallow_autoencoders_for_pre-training_7_mins.mp48.25MB
75. 16_Lecture16/01_OPTIONAL-_Learning_a_joint_model_of_images_and_captions_10_min.mp413.83MB
76. 16_Lecture16/02_OPTIONAL-_Hierarchical_Coordinate_Frames_10_mins.mp411.16MB
77. 16_Lecture16/03_OPTIONAL-_Bayesian_optimization_of_hyper-parameters_13_min.mp415.8MB
78. 16_Lecture16/04_OPTIONAL-_The_fog_of_progress_3_min.mp42.78MB