[Coursera] Neural Networks for Machine Learning by Geoffrey Hinton

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[Coursera] Neural Networks for Machine Learning by Geoffrey Hinton的二维码

文档列表

05_Lecture5/04_Convolutional_nets_for_object_recognition_17min.mp4 24.1 MB
07_Lecture7/01_Modeling_sequences-_A_brief_overview.mp4 21.1 MB
14_Lecture14/01_Learning_layers_of_features_by_stacking_RBMs_17_min.mp4 21.0 MB
14_Lecture14/05_OPTIONAL_VIDEO-_RBMs_are_infinite_sigmoid_belief_nets_17_mins.mp4 20.4 MB
05_Lecture5/03_Convolutional_nets_for_digit_recognition_16_min.mp4 19.4 MB
12_Lecture12/02_OPTIONAL_VIDEO-_More_efficient_ways_to_get_the_statistics_15_mins.mp4 17.8 MB
02_Lecture2/05_What_perceptrons_cant_do_15_min.mp4 17.4 MB
08_Lecture8/02_Modeling_character_strings_with_multiplicative_connections_14_mins.mp4 17.4 MB
08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.mp4 17.0 MB
16_Lecture16/03_OPTIONAL-_Bayesian_optimization_of_hyper-parameters_13_min.mp4 16.6 MB
13_Lecture13/04_The_wake-sleep_algorithm_13_min.mp4 16.4 MB
10_Lecture10/01_Why_it_helps_to_combine_models_13_min.mp4 15.9 MB
06_Lecture6/05_Rmsprop-_Divide_the_gradient_by_a_running_average_of_its_recent_magnitude.mp4 15.9 MB
01_Lecture1/01_Why_do_we_need_machine_learning_13_min.mp4 15.8 MB
10_Lecture10/02_Mixtures_of_Experts_13_min.mp4 15.7 MB
06_Lecture6/02_A_bag_of_tricks_for_mini-batch_gradient_descent.mp4 15.6 MB
13_Lecture13/02_Belief_Nets_13_min.mp4 15.6 MB
11_Lecture11/01_Hopfield_Nets_13_min.mp4 15.4 MB
04_Lecture4/01_Learning_to_predict_the_next_word_13_min.mp4 15.0 MB
04_Lecture4/05_Ways_to_deal_with_the_large_number_of_possible_outputs_15_min.mp4 14.9 MB
12_Lecture12/01_Boltzmann_machine_learning_12_min.mp4 14.7 MB
08_Lecture8/03_Learning_to_predict_the_next_character_using_HF_12__mins.mp4 14.6 MB
16_Lecture16/01_OPTIONAL-_Learning_a_joint_model_of_images_and_captions_10_min.mp4 14.5 MB
13_Lecture13/03_Learning_sigmoid_belief_nets_12_min.mp4 14.3 MB
09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.mp4 14.2 MB
03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.mp4 14.2 MB
03_Lecture3/04_The_backpropagation_algorithm_12_min.mp4 14.0 MB
11_Lecture11/05_How_a_Boltzmann_machine_models_data_12_min.mp4 13.9 MB
11_Lecture11/02_Dealing_with_spurious_minima_11_min.mp4 13.4 MB
12_Lecture12/03_Restricted_Boltzmann_Machines_11_min.mp4 13.3 MB
09_Lecture9/05_The_Bayesian_interpretation_of_weight_decay_11_min.mp4 12.9 MB
09_Lecture9/04_Introduction_to_the_full_Bayesian_approach_12_min.mp4 12.6 MB
13_Lecture13/01_The_ups_and_downs_of_back_propagation_10_min.mp4 12.4 MB
11_Lecture11/04_Using_stochastic_units_to_improv_search_11_min.mp4 12.3 MB
15_Lecture15/05_Learning_binary_codes_for_image_retrieval_9_mins.mp4 12.1 MB
11_Lecture11/03_Hopfield_nets_with_hidden_units_10_min.mp4 11.9 MB
14_Lecture14/02_Discriminative_learning_for_DBNs_9_mins.mp4 11.8 MB
08_Lecture8/04_Echo_State_Networks_9_min.mp4 11.8 MB
14_Lecture14/04_Modeling_real-valued_data_with_an_RBM_10_mins.mp4 11.7 MB
16_Lecture16/02_OPTIONAL-_Hierarchical_Coordinate_Frames_10_mins.mp4 11.7 MB
03_Lecture3/05_Using_the_derivatives_computed_by_backpropagation_10_min.mp4 11.7 MB
15_Lecture15/03_Deep_auto_encoders_for_document_retrieval_8_mins.mp4 10.7 MB
07_Lecture7/05_Long-term_Short-term-memory.mp4 10.7 MB
14_Lecture14/03_What_happens_during_discriminative_fine-tuning_8_mins.mp4 10.7 MB
15_Lecture15/04_Semantic_Hashing_9_mins.mp4 10.5 MB
01_Lecture1/02_What_are_neural_networks_8_min.mp4 10.2 MB
06_Lecture6/03_The_momentum_method.mp4 10.2 MB
10_Lecture10/05_Dropout_9_min.mp4 10.2 MB
15_Lecture15/01_From_PCA_to_autoencoders_5_mins.mp4 10.2 MB
06_Lecture6/01_Overview_of_mini-batch_gradient_descent.mp4 10.1 MB
12_Lecture12/05_RBMs_for_collaborative_filtering_8_mins.mp4 10.0 MB
02_Lecture2/02_Perceptrons-_The_first_generation_of_neural_networks_8_min.mp4 9.8 MB
01_Lecture1/03_Some_simple_models_of_neurons_8_min.mp4 9.7 MB
01_Lecture1/05_Three_types_of_learning_8_min.mp4 9.4 MB
04_Lecture4/04_Neuro-probabilistic_language_models_8_min.mp4 9.4 MB
07_Lecture7/04_Why_it_is_difficult_to_train_an_RNN.mp4 9.3 MB
02_Lecture2/01_Types_of_neural_network_architectures_7_min.mp4 9.2 MB
12_Lecture12/04_An_example_of_RBM_learning_7_mins.mp4 9.1 MB
09_Lecture9/03_Using_noise_as_a_regularizer_7_min.mp4 8.9 MB
10_Lecture10/03_The_idea_of_full_Bayesian_learning_7_min.mp4 8.8 MB
15_Lecture15/06_Shallow_autoencoders_for_pre-training_7_mins.mp4 8.7 MB
10_Lecture10/04_Making_full_Bayesian_learning_practical_7_min.mp4 8.5 MB
04_Lecture4/03_Another_diversion-_The_softmax_output_function_7_min.mp4 8.4 MB
09_Lecture9/02_Limiting_the_size_of_the_weights_6_min.mp4 7.7 MB
07_Lecture7/02_Training_RNNs_with_back_propagation.mp4 7.7 MB
02_Lecture2/03_A_geometrical_view_of_perceptrons_6_min.mp4 7.7 MB
07_Lecture7/03_A_toy_example_of_training_an_RNN.mp4 7.6 MB
05_Lecture5/02_Achieving_viewpoint_invariance_6_min.mp4 7.2 MB
06_Lecture6/04_Adaptive_learning_rates_for_each_connection.mp4 7.0 MB
01_Lecture1/04_A_simple_example_of_learning_6_min.mp4 6.9 MB
02_Lecture2/04_Why_the_learning_works_5_min.mp4 6.2 MB
03_Lecture3/02_The_error_surface_for_a_linear_neuron_5_min.mp4 6.2 MB
05_Lecture5/01_Why_object_recognition_is_difficult_5_min.mp4 5.6 MB
04_Lecture4/02_A_brief_diversion_into_cognitive_science_4_min.mp4 5.6 MB
15_Lecture15/02_Deep_auto_encoders_4_mins.mp4 5.2 MB
09_Lecture9/06_MacKays_quick_and_dirty_method_of_setting_weight_costs_4_min.mp4 4.6 MB
03_Lecture3/03_Learning_the_weights_of_a_logistic_output_neuron_4_min.mp4 4.6 MB
01_Lecture1/01_Why_do_we_need_machine_learning_13_min.pdf 4.1 MB
01_Lecture1/01_Why_do_we_need_machine_learning_13_min.pptx 3.8 MB
03_Lecture3/04_The_backpropagation_algorithm_12_min.pdf 3.1 MB
16_Lecture16/04_OPTIONAL-_The_fog_of_progress_3_min.mp4 2.9 MB
15_Lecture15/01_From_PCA_to_autoencoders_5_mins.pdf 2.6 MB
13_Lecture13/03_Learning_sigmoid_belief_nets_12_min.pdf 2.4 MB
12_Lecture12/01_Boltzmann_machine_learning_12_min.pptx 2.0 MB
15_Lecture15/01_From_PCA_to_autoencoders_5_mins.pptx 1.9 MB
12_Lecture12/01_Boltzmann_machine_learning_12_min.pdf 1.8 MB
05_Lecture5/01_Why_object_recognition_is_difficult_5_min.pptx 1.7 MB
10_Lecture10/05_Dropout_9_min.pdf 1.7 MB
05_Lecture5/01_Why_object_recognition_is_difficult_5_min.pdf 1.6 MB
09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.pptx 1.5 MB
14_Lecture14/01_Learning_layers_of_features_by_stacking_RBMs_17_min.pptx 1.3 MB
03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.pptx 1.2 MB
14_Lecture14/01_Learning_layers_of_features_by_stacking_RBMs_17_min.pdf 1.2 MB
04_Lecture4/01_Learning_to_predict_the_next_word_13_min.pptx 1.1 MB
07_Lecture7/01_Modeling_sequences-_A_brief_overview.pdf 976.0 kB
04_Lecture4/01_Learning_to_predict_the_next_word_13_min.pdf 964.1 kB
05_Lecture5/04_Convolutional_nets_for_object_recognition_17min_0_.pdf 955.1 kB
10_Lecture10/01_Why_it_helps_to_combine_models_13_min.pptx 901.6 kB
10_Lecture10/01_Why_it_helps_to_combine_models_13_min.pdf 847.1 kB
14_Lecture14/01_Learning_layers_of_features_by_stacking_RBMs_17_min_2_.pdf 787.8 kB
15_Lecture15/05_Learning_binary_codes_for_image_retrieval_9_mins.pdf 759.2 kB
11_Lecture11/01_Hopfield_Nets_13_min.pptx 743.8 kB
09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.pdf 719.0 kB
11_Lecture11/01_Hopfield_Nets_13_min.pdf 711.2 kB
06_Lecture6/01_Overview_of_mini-batch_gradient_descent.pptx 672.6 kB
08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.pdf 658.3 kB
15_Lecture15/04_Semantic_Hashing_9_mins.pdf 641.6 kB
08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.pptx 568.2 kB
03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.pdf 548.0 kB
06_Lecture6/01_Overview_of_mini-batch_gradient_descent.pdf 546.8 kB
14_Lecture14/01_Learning_layers_of_features_by_stacking_RBMs_17_min_1_.pdf 513.9 kB
02_Lecture2/01_Types_of_neural_network_architectures_7_min.pdf 504.8 kB
14_Lecture14/01_Learning_layers_of_features_by_stacking_RBMs_17_min_0_Self-taught_learning-_transfer_learning_from_unlabeled_data.pdf 484.9 kB
13_Lecture13/01_The_ups_and_downs_of_back_propagation_10_min.pptx 424.8 kB
02_Lecture2/01_Types_of_neural_network_architectures_7_min.pptx 409.2 kB
16_Lecture16/01_OPTIONAL-_Learning_a_joint_model_of_images_and_captions_10_min.pdf 346.9 kB
16_Lecture16/04_OPTIONAL-_The_fog_of_progress_3_min.pdf 346.9 kB
16_Lecture16/01_OPTIONAL-_Learning_a_joint_model_of_images_and_captions_10_min.pptx 344.3 kB
16_Lecture16/04_OPTIONAL-_The_fog_of_progress_3_min.pptx 344.3 kB
07_Lecture7/05_Long-term_Short-term-memory.pdf 320.6 kB
13_Lecture13/01_The_ups_and_downs_of_back_propagation_10_min.pdf 314.6 kB
08_Lecture8/03_Learning_to_predict_the_next_character_using_HF_12__mins.pdf 273.4 kB
10_Lecture10/02_Mixtures_of_Experts_13_min.pdf 271.1 kB
13_Lecture13/04_The_wake-sleep_algorithm_13_min.pdf 261.5 kB
07_Lecture7/01_Modeling_sequences-_A_brief_overview.pptx 228.0 kB
04_Lecture4/05_Ways_to_deal_with_the_large_number_of_possible_outputs_15_min.png 154.4 kB
04_Lecture4/04_Neuro-probabilistic_language_models_8_min.pdf 140.1 kB
05_Lecture5/04_Convolutional_nets_for_object_recognition_17min.pdf 125.4 kB
05_Lecture5/04_Convolutional_nets_for_object_recognition_17min.srt 26.2 kB
14_Lecture14/01_Learning_layers_of_features_by_stacking_RBMs_17_min.srt 23.4 kB
07_Lecture7/01_Modeling_sequences-_A_brief_overview.srt 23.2 kB
14_Lecture14/05_OPTIONAL_VIDEO-_RBMs_are_infinite_sigmoid_belief_nets_17_mins.srt 22.2 kB
05_Lecture5/03_Convolutional_nets_for_digit_recognition_16_min.srt 22.1 kB
06_Lecture6/02_A_bag_of_tricks_for_mini-batch_gradient_descent.srt 19.2 kB
16_Lecture16/03_OPTIONAL-_Bayesian_optimization_of_hyper-parameters_13_min.srt 19.0 kB
02_Lecture2/05_What_perceptrons_cant_do_15_min.srt 18.9 kB
01_Lecture1/01_Why_do_we_need_machine_learning_13_min.srt 18.8 kB
12_Lecture12/02_OPTIONAL_VIDEO-_More_efficient_ways_to_get_the_statistics_15_mins.srt 18.6 kB
04_Lecture4/05_Ways_to_deal_with_the_large_number_of_possible_outputs_15_min.srt 18.6 kB
08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.srt 18.4 kB
10_Lecture10/01_Why_it_helps_to_combine_models_13_min.srt 18.1 kB
08_Lecture8/02_Modeling_character_strings_with_multiplicative_connections_14_mins.srt 17.9 kB
13_Lecture13/04_The_wake-sleep_algorithm_13_min.srt 17.8 kB
13_Lecture13/02_Belief_Nets_13_min.srt 17.8 kB
10_Lecture10/02_Mixtures_of_Experts_13_min.srt 17.5 kB
05_Lecture5/04_Convolutional_nets_for_object_recognition_17min.txt 17.0 kB
04_Lecture4/01_Learning_to_predict_the_next_word_13_min.srt 16.9 kB
11_Lecture11/01_Hopfield_Nets_13_min.srt 16.8 kB
12_Lecture12/01_Boltzmann_machine_learning_12_min.srt 16.4 kB
11_Lecture11/05_How_a_Boltzmann_machine_models_data_12_min.srt 16.3 kB
09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.srt 16.2 kB
08_Lecture8/03_Learning_to_predict_the_next_character_using_HF_12__mins.srt 16.1 kB
06_Lecture6/05_Rmsprop-_Divide_the_gradient_by_a_running_average_of_its_recent_magnitude.srt 16.1 kB
14_Lecture14/01_Learning_layers_of_features_by_stacking_RBMs_17_min.txt 15.6 kB
03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.srt 15.4 kB
03_Lecture3/04_The_backpropagation_algorithm_12_min.srt 15.2 kB
11_Lecture11/02_Dealing_with_spurious_minima_11_min.srt 15.2 kB
07_Lecture7/01_Modeling_sequences-_A_brief_overview.txt 15.1 kB
13_Lecture13/03_Learning_sigmoid_belief_nets_12_min.srt 15.0 kB
14_Lecture14/05_OPTIONAL_VIDEO-_RBMs_are_infinite_sigmoid_belief_nets_17_mins.txt 14.7 kB
11_Lecture11/04_Using_stochastic_units_to_improv_search_11_min.srt 14.3 kB
05_Lecture5/03_Convolutional_nets_for_digit_recognition_16_min.txt 14.2 kB
13_Lecture13/01_The_ups_and_downs_of_back_propagation_10_min.srt 14.0 kB
12_Lecture12/03_Restricted_Boltzmann_Machines_11_min.srt 13.9 kB
03_Lecture3/05_Using_the_derivatives_computed_by_backpropagation_10_min.srt 13.9 kB
16_Lecture16/02_OPTIONAL-_Hierarchical_Coordinate_Frames_10_mins.srt 13.7 kB
09_Lecture9/04_Introduction_to_the_full_Bayesian_approach_12_min.srt 13.5 kB
09_Lecture9/05_The_Bayesian_interpretation_of_weight_decay_11_min.srt 13.3 kB
15_Lecture15/05_Learning_binary_codes_for_image_retrieval_9_mins.srt 13.2 kB
14_Lecture14/02_Discriminative_learning_for_DBNs_9_mins.srt 13.0 kB
16_Lecture16/03_OPTIONAL-_Bayesian_optimization_of_hyper-parameters_13_min.txt 12.7 kB
11_Lecture11/03_Hopfield_nets_with_hidden_units_10_min.srt 12.6 kB
06_Lecture6/02_A_bag_of_tricks_for_mini-batch_gradient_descent.txt 12.5 kB
14_Lecture14/04_Modeling_real-valued_data_with_an_RBM_10_mins.srt 12.4 kB
02_Lecture2/05_What_perceptrons_cant_do_15_min.txt 12.4 kB
08_Lecture8/04_Echo_State_Networks_9_min.srt 12.3 kB
06_Lecture6/01_Overview_of_mini-batch_gradient_descent.srt 12.2 kB
12_Lecture12/02_OPTIONAL_VIDEO-_More_efficient_ways_to_get_the_statistics_15_mins.txt 12.2 kB
01_Lecture1/01_Why_do_we_need_machine_learning_13_min.txt 12.2 kB
04_Lecture4/05_Ways_to_deal_with_the_large_number_of_possible_outputs_15_min.txt 12.0 kB
10_Lecture10/05_Dropout_9_min.srt 12.0 kB
08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.txt 11.9 kB
07_Lecture7/05_Long-term_Short-term-memory.srt 11.9 kB
08_Lecture8/02_Modeling_character_strings_with_multiplicative_connections_14_mins.txt 11.8 kB
01_Lecture1/02_What_are_neural_networks_8_min.srt 11.8 kB
10_Lecture10/01_Why_it_helps_to_combine_models_13_min.txt 11.8 kB
15_Lecture15/04_Semantic_Hashing_9_mins.srt 11.6 kB
13_Lecture13/02_Belief_Nets_13_min.txt 11.5 kB
13_Lecture13/04_The_wake-sleep_algorithm_13_min.txt 11.5 kB
10_Lecture10/02_Mixtures_of_Experts_13_min.txt 11.4 kB
06_Lecture6/03_The_momentum_method.srt 11.4 kB
02_Lecture2/02_Perceptrons-_The_first_generation_of_neural_networks_8_min.srt 11.1 kB
04_Lecture4/01_Learning_to_predict_the_next_word_13_min.txt 11.1 kB
04_Lecture4/04_Neuro-probabilistic_language_models_8_min.srt 11.0 kB
01_Lecture1/03_Some_simple_models_of_neurons_8_min.srt 11.0 kB
12_Lecture12/05_RBMs_for_collaborative_filtering_8_mins.srt 10.9 kB
14_Lecture14/03_What_happens_during_discriminative_fine-tuning_8_mins.srt 10.9 kB
11_Lecture11/01_Hopfield_Nets_13_min.txt 10.9 kB
15_Lecture15/03_Deep_auto_encoders_for_document_retrieval_8_mins.srt 10.8 kB
12_Lecture12/01_Boltzmann_machine_learning_12_min.txt 10.7 kB
01_Lecture1/05_Three_types_of_learning_8_min.srt 10.6 kB
09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.txt 10.6 kB
16_Lecture16/01_OPTIONAL-_Learning_a_joint_model_of_images_and_captions_10_min.srt 10.6 kB
11_Lecture11/05_How_a_Boltzmann_machine_models_data_12_min.txt 10.5 kB
10_Lecture10/03_The_idea_of_full_Bayesian_learning_7_min.srt 10.5 kB
15_Lecture15/01_From_PCA_to_autoencoders_5_mins.srt 10.5 kB
06_Lecture6/05_Rmsprop-_Divide_the_gradient_by_a_running_average_of_its_recent_magnitude.txt 10.5 kB
08_Lecture8/03_Learning_to_predict_the_next_character_using_HF_12__mins.txt 10.4 kB
15_Lecture15/06_Shallow_autoencoders_for_pre-training_7_mins.srt 10.3 kB
03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.txt 10.1 kB
12_Lecture12/04_An_example_of_RBM_learning_7_mins.srt 10.1 kB
02_Lecture2/01_Types_of_neural_network_architectures_7_min.srt 10.1 kB
07_Lecture7/04_Why_it_is_difficult_to_train_an_RNN.srt 10.0 kB
11_Lecture11/02_Dealing_with_spurious_minima_11_min.txt 10.0 kB
03_Lecture3/04_The_backpropagation_algorithm_12_min.txt 10.0 kB
13_Lecture13/03_Learning_sigmoid_belief_nets_12_min.txt 9.8 kB
11_Lecture11/04_Using_stochastic_units_to_improv_search_11_min.txt 9.3 kB
04_Lecture4/03_Another_diversion-_The_softmax_output_function_7_min.srt 9.3 kB
16_Lecture16/02_OPTIONAL-_Hierarchical_Coordinate_Frames_10_mins.txt 9.2 kB
03_Lecture3/05_Using_the_derivatives_computed_by_backpropagation_10_min.txt 9.1 kB
12_Lecture12/03_Restricted_Boltzmann_Machines_11_min.txt 9.1 kB
09_Lecture9/03_Using_noise_as_a_regularizer_7_min.srt 9.1 kB
13_Lecture13/01_The_ups_and_downs_of_back_propagation_10_min.txt 9.1 kB
15_Lecture15/05_Learning_binary_codes_for_image_retrieval_9_mins.txt 8.9 kB
09_Lecture9/05_The_Bayesian_interpretation_of_weight_decay_11_min.txt 8.8 kB
09_Lecture9/04_Introduction_to_the_full_Bayesian_approach_12_min.txt 8.8 kB
14_Lecture14/02_Discriminative_learning_for_DBNs_9_mins.txt 8.7 kB
10_Lecture10/04_Making_full_Bayesian_learning_practical_7_min.srt 8.7 kB
09_Lecture9/02_Limiting_the_size_of_the_weights_6_min.srt 8.6 kB
07_Lecture7/02_Training_RNNs_with_back_propagation.srt 8.6 kB
02_Lecture2/03_A_geometrical_view_of_perceptrons_6_min.srt 8.5 kB
14_Lecture14/04_Modeling_real-valued_data_with_an_RBM_10_mins.txt 8.3 kB
05_Lecture5/02_Achieving_viewpoint_invariance_6_min.srt 8.3 kB
11_Lecture11/03_Hopfield_nets_with_hidden_units_10_min.txt 8.3 kB
08_Lecture8/04_Echo_State_Networks_9_min.txt 8.0 kB
06_Lecture6/01_Overview_of_mini-batch_gradient_descent.txt 8.0 kB
06_Lecture6/04_Adaptive_learning_rates_for_each_connection.srt 7.9 kB
07_Lecture7/05_Long-term_Short-term-memory.txt 7.9 kB
15_Lecture15/04_Semantic_Hashing_9_mins.txt 7.8 kB
10_Lecture10/05_Dropout_9_min.txt 7.8 kB
07_Lecture7/03_A_toy_example_of_training_an_RNN.srt 7.7 kB
01_Lecture1/02_What_are_neural_networks_8_min.txt 7.7 kB
06_Lecture6/03_The_momentum_method.txt 7.4 kB
14_Lecture14/03_What_happens_during_discriminative_fine-tuning_8_mins.txt 7.3 kB
02_Lecture2/02_Perceptrons-_The_first_generation_of_neural_networks_8_min.txt 7.3 kB
15_Lecture15/03_Deep_auto_encoders_for_document_retrieval_8_mins.txt 7.2 kB
01_Lecture1/04_A_simple_example_of_learning_6_min.srt 7.2 kB
12_Lecture12/05_RBMs_for_collaborative_filtering_8_mins.txt 7.2 kB
04_Lecture4/04_Neuro-probabilistic_language_models_8_min.txt 7.2 kB
16_Lecture16/01_OPTIONAL-_Learning_a_joint_model_of_images_and_captions_10_min.txt 7.1 kB
01_Lecture1/03_Some_simple_models_of_neurons_8_min.txt 7.1 kB
15_Lecture15/01_From_PCA_to_autoencoders_5_mins.txt 7.0 kB
01_Lecture1/05_Three_types_of_learning_8_min.txt 7.0 kB
15_Lecture15/06_Shallow_autoencoders_for_pre-training_7_mins.txt 7.0 kB
10_Lecture10/03_The_idea_of_full_Bayesian_learning_7_min.txt 6.9 kB
12_Lecture12/04_An_example_of_RBM_learning_7_mins.txt 6.6 kB
02_Lecture2/01_Types_of_neural_network_architectures_7_min.txt 6.6 kB
07_Lecture7/04_Why_it_is_difficult_to_train_an_RNN.txt 6.6 kB
02_Lecture2/04_Why_the_learning_works_5_min.srt 6.6 kB
03_Lecture3/02_The_error_surface_for_a_linear_neuron_5_min.srt 6.5 kB
05_Lecture5/01_Why_object_recognition_is_difficult_5_min.srt 6.3 kB
04_Lecture4/03_Another_diversion-_The_softmax_output_function_7_min.txt 6.1 kB
09_Lecture9/03_Using_noise_as_a_regularizer_7_min.txt 6.0 kB
04_Lecture4/02_A_brief_diversion_into_cognitive_science_4_min.srt 5.9 kB
10_Lecture10/04_Making_full_Bayesian_learning_practical_7_min.txt 5.7 kB
07_Lecture7/02_Training_RNNs_with_back_propagation.txt 5.7 kB
09_Lecture9/02_Limiting_the_size_of_the_weights_6_min.txt 5.6 kB
02_Lecture2/03_A_geometrical_view_of_perceptrons_6_min.txt 5.5 kB
15_Lecture15/02_Deep_auto_encoders_4_mins.srt 5.5 kB
05_Lecture5/02_Achieving_viewpoint_invariance_6_min.txt 5.4 kB
06_Lecture6/04_Adaptive_learning_rates_for_each_connection.txt 5.2 kB
07_Lecture7/03_A_toy_example_of_training_an_RNN.txt 5.0 kB
01_Lecture1/04_A_simple_example_of_learning_6_min.txt 4.8 kB
03_Lecture3/03_Learning_the_weights_of_a_logistic_output_neuron_4_min.srt 4.6 kB
09_Lecture9/06_MacKays_quick_and_dirty_method_of_setting_weight_costs_4_min.srt 4.5 kB
02_Lecture2/04_Why_the_learning_works_5_min.txt 4.3 kB
03_Lecture3/02_The_error_surface_for_a_linear_neuron_5_min.txt 4.2 kB
05_Lecture5/01_Why_object_recognition_is_difficult_5_min.txt 4.1 kB
04_Lecture4/02_A_brief_diversion_into_cognitive_science_4_min.txt 3.8 kB
15_Lecture15/02_Deep_auto_encoders_4_mins.txt 3.7 kB
16_Lecture16/04_OPTIONAL-_The_fog_of_progress_3_min.srt 3.6 kB
03_Lecture3/03_Learning_the_weights_of_a_logistic_output_neuron_4_min.txt 3.0 kB
09_Lecture9/06_MacKays_quick_and_dirty_method_of_setting_weight_costs_4_min.txt 3.0 kB
16_Lecture16/04_OPTIONAL-_The_fog_of_progress_3_min.txt 2.4 kB

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