[Coursera] Neural Networks for Machine Learning by Geoffrey Hinton

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文件数目：284个文件
文件大小：934.59 MB
收录时间：2017-09-24
访问次数：9
相关内容：Coursera Neural Networks Machine Learning Geoffrey Hinton

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05_Lecture5/04_Convolutional_nets_for_object_recognition_17min.mp4
23.03 MB
07_Lecture7/01_Modeling_sequences-_A_brief_overview.mp4
20.13 MB
14_Lecture14/01_Learning_layers_of_features_by_stacking_RBMs_17_min.mp4
20.07 MB
14_Lecture14/05_OPTIONAL_VIDEO-_RBMs_are_infinite_sigmoid_belief_nets_17_mins.mp4
19.44 MB
05_Lecture5/03_Convolutional_nets_for_digit_recognition_16_min.mp4
18.46 MB
12_Lecture12/02_OPTIONAL_VIDEO-_More_efficient_ways_to_get_the_statistics_15_mins.mp4
16.93 MB
02_Lecture2/05_What_perceptrons_cant_do_15_min.mp4
16.57 MB
08_Lecture8/02_Modeling_character_strings_with_multiplicative_connections_14_mins.mp4
16.56 MB
08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.mp4
16.24 MB
16_Lecture16/03_OPTIONAL-_Bayesian_optimization_of_hyper-parameters_13_min.mp4
15.8 MB
13_Lecture13/04_The_wake-sleep_algorithm_13_min.mp4
15.68 MB
10_Lecture10/01_Why_it_helps_to_combine_models_13_min.mp4
15.12 MB
06_Lecture6/05_Rmsprop-_Divide_the_gradient_by_a_running_average_of_its_recent_magnitude.mp4
15.12 MB
01_Lecture1/01_Why_do_we_need_machine_learning_13_min.mp4
15.05 MB
10_Lecture10/02_Mixtures_of_Experts_13_min.mp4
14.98 MB
06_Lecture6/02_A_bag_of_tricks_for_mini-batch_gradient_descent.mp4
14.9 MB
13_Lecture13/02_Belief_Nets_13_min.mp4
14.86 MB
11_Lecture11/01_Hopfield_Nets_13_min.mp4
14.65 MB
04_Lecture4/01_Learning_to_predict_the_next_word_13_min.mp4
14.28 MB
04_Lecture4/05_Ways_to_deal_with_the_large_number_of_possible_outputs_15_min.mp4
14.26 MB
12_Lecture12/01_Boltzmann_machine_learning_12_min.mp4
14.03 MB
08_Lecture8/03_Learning_to_predict_the_next_character_using_HF_12__mins.mp4
13.92 MB
16_Lecture16/01_OPTIONAL-_Learning_a_joint_model_of_images_and_captions_10_min.mp4
13.83 MB
13_Lecture13/03_Learning_sigmoid_belief_nets_12_min.mp4
13.59 MB
09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.mp4
13.57 MB
03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.mp4
13.52 MB
03_Lecture3/04_The_backpropagation_algorithm_12_min.mp4
13.35 MB
11_Lecture11/05_How_a_Boltzmann_machine_models_data_12_min.mp4
13.28 MB
11_Lecture11/02_Dealing_with_spurious_minima_11_min.mp4
12.77 MB
12_Lecture12/03_Restricted_Boltzmann_Machines_11_min.mp4
12.68 MB
09_Lecture9/05_The_Bayesian_interpretation_of_weight_decay_11_min.mp4
12.27 MB
09_Lecture9/04_Introduction_to_the_full_Bayesian_approach_12_min.mp4
12 MB
13_Lecture13/01_The_ups_and_downs_of_back_propagation_10_min.mp4
11.83 MB
11_Lecture11/04_Using_stochastic_units_to_improv_search_11_min.mp4
11.76 MB
15_Lecture15/05_Learning_binary_codes_for_image_retrieval_9_mins.mp4
11.51 MB
11_Lecture11/03_Hopfield_nets_with_hidden_units_10_min.mp4
11.31 MB
14_Lecture14/02_Discriminative_learning_for_DBNs_9_mins.mp4
11.29 MB
08_Lecture8/04_Echo_State_Networks_9_min.mp4
11.28 MB
14_Lecture14/04_Modeling_real-valued_data_with_an_RBM_10_mins.mp4
11.2 MB
16_Lecture16/02_OPTIONAL-_Hierarchical_Coordinate_Frames_10_mins.mp4
11.16 MB
03_Lecture3/05_Using_the_derivatives_computed_by_backpropagation_10_min.mp4
11.15 MB
15_Lecture15/03_Deep_auto_encoders_for_document_retrieval_8_mins.mp4
10.25 MB
07_Lecture7/05_Long-term_Short-term-memory.mp4
10.23 MB
14_Lecture14/03_What_happens_during_discriminative_fine-tuning_8_mins.mp4
10.17 MB
15_Lecture15/04_Semantic_Hashing_9_mins.mp4
9.99 MB
01_Lecture1/02_What_are_neural_networks_8_min.mp4
9.76 MB
06_Lecture6/03_The_momentum_method.mp4
9.74 MB
10_Lecture10/05_Dropout_9_min.mp4
9.69 MB
15_Lecture15/01_From_PCA_to_autoencoders_5_mins.mp4
9.68 MB
06_Lecture6/01_Overview_of_mini-batch_gradient_descent.mp4
9.6 MB