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[Coursera] Neural Networks for Machine Learning by Geoffrey Hinton
magnet:?xt=urn:btih:671da68928377171085caf0a2861d8c559e98f54&dn=[Coursera] Neural Networks for Machine Learning by Geoffrey Hinton
磁力链接详情
Hash值:
671da68928377171085caf0a2861d8c559e98f54
点击数:
280
文件大小:
884.52 MB
文件数量:
78
创建日期:
2017-3-17 18:38
最后访问:
2024-6-26 07:42
访问标签:
Coursera
Neural
Networks
for
Machine
Learning
by
Geoffrey
Hinton
文件列表详情
01_Lecture1/01_Why_do_we_need_machine_learning_13_min.mp4 15.05 MB
01_Lecture1/02_What_are_neural_networks_8_min.mp4 9.76 MB
01_Lecture1/03_Some_simple_models_of_neurons_8_min.mp4 9.26 MB
01_Lecture1/04_A_simple_example_of_learning_6_min.mp4 6.57 MB
01_Lecture1/05_Three_types_of_learning_8_min.mp4 8.96 MB
02_Lecture2/01_Types_of_neural_network_architectures_7_min.mp4 8.78 MB
02_Lecture2/02_Perceptrons-_The_first_generation_of_neural_networks_8_min.mp4 9.39 MB
02_Lecture2/03_A_geometrical_view_of_perceptrons_6_min.mp4 7.32 MB
02_Lecture2/04_Why_the_learning_works_5_min.mp4 5.9 MB
02_Lecture2/05_What_perceptrons_cant_do_15_min.mp4 16.57 MB
03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.mp4 13.52 MB
03_Lecture3/02_The_error_surface_for_a_linear_neuron_5_min.mp4 5.89 MB
03_Lecture3/03_Learning_the_weights_of_a_logistic_output_neuron_4_min.mp4 4.37 MB
03_Lecture3/04_The_backpropagation_algorithm_12_min.mp4 13.35 MB
03_Lecture3/05_Using_the_derivatives_computed_by_backpropagation_10_min.mp4 11.15 MB
04_Lecture4/01_Learning_to_predict_the_next_word_13_min.mp4 14.28 MB
04_Lecture4/02_A_brief_diversion_into_cognitive_science_4_min.mp4 5.31 MB
04_Lecture4/03_Another_diversion-_The_softmax_output_function_7_min.mp4 8.03 MB
04_Lecture4/04_Neuro-probabilistic_language_models_8_min.mp4 8.93 MB
04_Lecture4/05_Ways_to_deal_with_the_large_number_of_possible_outputs_15_min.mp4 14.26 MB
05_Lecture5/01_Why_object_recognition_is_difficult_5_min.mp4 5.37 MB
05_Lecture5/02_Achieving_viewpoint_invariance_6_min.mp4 6.89 MB
05_Lecture5/03_Convolutional_nets_for_digit_recognition_16_min.mp4 18.46 MB
05_Lecture5/04_Convolutional_nets_for_object_recognition_17min.mp4 23.03 MB
06_Lecture6/01_Overview_of_mini-batch_gradient_descent.mp4 9.6 MB
06_Lecture6/02_A_bag_of_tricks_for_mini-batch_gradient_descent.mp4 14.9 MB
06_Lecture6/03_The_momentum_method.mp4 9.74 MB
06_Lecture6/04_Adaptive_learning_rates_for_each_connection.mp4 6.63 MB
06_Lecture6/05_Rmsprop-_Divide_the_gradient_by_a_running_average_of_its_recent_magnitude.mp4 15.12 MB
07_Lecture7/01_Modeling_sequences-_A_brief_overview.mp4 20.13 MB
07_Lecture7/02_Training_RNNs_with_back_propagation.mp4 7.33 MB
07_Lecture7/03_A_toy_example_of_training_an_RNN.mp4 7.24 MB
07_Lecture7/04_Why_it_is_difficult_to_train_an_RNN.mp4 8.89 MB
07_Lecture7/05_Long-term_Short-term-memory.mp4 10.23 MB
08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.mp4 16.24 MB
08_Lecture8/02_Modeling_character_strings_with_multiplicative_connections_14_mins.mp4 16.56 MB
08_Lecture8/03_Learning_to_predict_the_next_character_using_HF_12__mins.mp4 13.92 MB
08_Lecture8/04_Echo_State_Networks_9_min.mp4 11.28 MB
09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.mp4 13.57 MB
09_Lecture9/02_Limiting_the_size_of_the_weights_6_min.mp4 7.36 MB
09_Lecture9/03_Using_noise_as_a_regularizer_7_min.mp4 8.48 MB
09_Lecture9/04_Introduction_to_the_full_Bayesian_approach_12_min.mp4 12 MB
09_Lecture9/05_The_Bayesian_interpretation_of_weight_decay_11_min.mp4 12.27 MB
09_Lecture9/06_MacKays_quick_and_dirty_method_of_setting_weight_costs_4_min.mp4 4.37 MB
10_Lecture10/01_Why_it_helps_to_combine_models_13_min.mp4 15.12 MB
10_Lecture10/02_Mixtures_of_Experts_13_min.mp4 14.98 MB
10_Lecture10/03_The_idea_of_full_Bayesian_learning_7_min.mp4 8.39 MB
10_Lecture10/04_Making_full_Bayesian_learning_practical_7_min.mp4 8.13 MB
10_Lecture10/05_Dropout_9_min.mp4 9.69 MB
11_Lecture11/01_Hopfield_Nets_13_min.mp4 14.65 MB
11_Lecture11/02_Dealing_with_spurious_minima_11_min.mp4 12.77 MB
11_Lecture11/03_Hopfield_nets_with_hidden_units_10_min.mp4 11.31 MB
11_Lecture11/04_Using_stochastic_units_to_improv_search_11_min.mp4 11.76 MB
11_Lecture11/05_How_a_Boltzmann_machine_models_data_12_min.mp4 13.28 MB
12_Lecture12/01_Boltzmann_machine_learning_12_min.mp4 14.03 MB
12_Lecture12/02_OPTIONAL_VIDEO-_More_efficient_ways_to_get_the_statistics_15_mins.mp4 16.93 MB
12_Lecture12/03_Restricted_Boltzmann_Machines_11_min.mp4 12.68 MB
12_Lecture12/04_An_example_of_RBM_learning_7_mins.mp4 8.71 MB
12_Lecture12/05_RBMs_for_collaborative_filtering_8_mins.mp4 9.53 MB
13_Lecture13/01_The_ups_and_downs_of_back_propagation_10_min.mp4 11.83 MB
13_Lecture13/02_Belief_Nets_13_min.mp4 14.86 MB
13_Lecture13/03_Learning_sigmoid_belief_nets_12_min.mp4 13.59 MB
13_Lecture13/04_The_wake-sleep_algorithm_13_min.mp4 15.68 MB
14_Lecture14/01_Learning_layers_of_features_by_stacking_RBMs_17_min.mp4 20.07 MB
14_Lecture14/02_Discriminative_learning_for_DBNs_9_mins.mp4 11.29 MB
14_Lecture14/03_What_happens_during_discriminative_fine-tuning_8_mins.mp4 10.17 MB
14_Lecture14/04_Modeling_real-valued_data_with_an_RBM_10_mins.mp4 11.2 MB
14_Lecture14/05_OPTIONAL_VIDEO-_RBMs_are_infinite_sigmoid_belief_nets_17_mins.mp4 19.44 MB
15_Lecture15/01_From_PCA_to_autoencoders_5_mins.mp4 9.68 MB
15_Lecture15/02_Deep_auto_encoders_4_mins.mp4 4.92 MB
15_Lecture15/03_Deep_auto_encoders_for_document_retrieval_8_mins.mp4 10.25 MB
15_Lecture15/04_Semantic_Hashing_9_mins.mp4 9.99 MB
15_Lecture15/05_Learning_binary_codes_for_image_retrieval_9_mins.mp4 11.51 MB
15_Lecture15/06_Shallow_autoencoders_for_pre-training_7_mins.mp4 8.25 MB
16_Lecture16/01_OPTIONAL-_Learning_a_joint_model_of_images_and_captions_10_min.mp4 13.83 MB
16_Lecture16/02_OPTIONAL-_Hierarchical_Coordinate_Frames_10_mins.mp4 11.16 MB
16_Lecture16/03_OPTIONAL-_Bayesian_optimization_of_hyper-parameters_13_min.mp4 15.8 MB
16_Lecture16/04_OPTIONAL-_The_fog_of_progress_3_min.mp4 2.78 MB
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