[Coursera] Neural Networks for Machine Learning (University of Toronto) (neuralnets)
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neuralnets-2012-001 (378 files)
01_Lecture1/01_Why_do_we_need_machine_learning_13_min.mp415.78MB
01_Lecture1/01_Why_do_we_need_machine_learning_13_min.pdf4.06MB
01_Lecture1/01_Why_do_we_need_machine_learning_13_min.pptx3.80MB
01_Lecture1/01_Why_do_we_need_machine_learning_13_min.srt18.78kB
01_Lecture1/01_Why_do_we_need_machine_learning_13_min.txt12.17kB
01_Lecture1/02_What_are_neural_networks_8_min.mp410.23MB
01_Lecture1/02_What_are_neural_networks_8_min.srt11.78kB
01_Lecture1/02_What_are_neural_networks_8_min.txt7.67kB
01_Lecture1/03_Some_simple_models_of_neurons_8_min.mp49.71MB
01_Lecture1/03_Some_simple_models_of_neurons_8_min.srt10.95kB
01_Lecture1/03_Some_simple_models_of_neurons_8_min.txt7.11kB
01_Lecture1/04_A_simple_example_of_learning_6_min.mp46.89MB
01_Lecture1/04_A_simple_example_of_learning_6_min.srt7.19kB
01_Lecture1/04_A_simple_example_of_learning_6_min.txt4.76kB
01_Lecture1/05_Three_types_of_learning_8_min.mp49.39MB
01_Lecture1/05_Three_types_of_learning_8_min.srt10.64kB
01_Lecture1/05_Three_types_of_learning_8_min.txt6.99kB
02_Lecture2/01_Types_of_neural_network_architectures_7_min.mp49.20MB
02_Lecture2/01_Types_of_neural_network_architectures_7_min.pdf504.78kB
02_Lecture2/01_Types_of_neural_network_architectures_7_min.pptx409.21kB
02_Lecture2/01_Types_of_neural_network_architectures_7_min.srt10.09kB
02_Lecture2/01_Types_of_neural_network_architectures_7_min.txt6.62kB
02_Lecture2/02_Perceptrons-_The_first_generation_of_neural_networks_8_min.mp49.85MB
02_Lecture2/02_Perceptrons-_The_first_generation_of_neural_networks_8_min.srt11.12kB
02_Lecture2/02_Perceptrons-_The_first_generation_of_neural_networks_8_min.txt7.32kB
02_Lecture2/03_A_geometrical_view_of_perceptrons_6_min.mp47.68MB
02_Lecture2/03_A_geometrical_view_of_perceptrons_6_min.srt8.49kB
02_Lecture2/03_A_geometrical_view_of_perceptrons_6_min.txt5.51kB
02_Lecture2/04_Why_the_learning_works_5_min.mp46.18MB
02_Lecture2/04_Why_the_learning_works_5_min.srt6.56kB
02_Lecture2/04_Why_the_learning_works_5_min.txt4.31kB
02_Lecture2/05_What_perceptrons_cant_do_15_min.mp417.38MB
02_Lecture2/05_What_perceptrons_cant_do_15_min.srt18.95kB
02_Lecture2/05_What_perceptrons_cant_do_15_min.txt12.37kB
03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.mp414.18MB
03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.pdf548.03kB
03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.pptx1.19MB
03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.srt15.45kB
03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.txt10.13kB
03_Lecture3/02_The_error_surface_for_a_linear_neuron_5_min.mp46.18MB
03_Lecture3/02_The_error_surface_for_a_linear_neuron_5_min.srt6.46kB
03_Lecture3/02_The_error_surface_for_a_linear_neuron_5_min.txt4.21kB
03_Lecture3/03_Learning_the_weights_of_a_logistic_output_neuron_4_min.mp44.59MB
03_Lecture3/03_Learning_the_weights_of_a_logistic_output_neuron_4_min.srt4.56kB
03_Lecture3/03_Learning_the_weights_of_a_logistic_output_neuron_4_min.txt3.02kB
03_Lecture3/04_The_backpropagation_algorithm_12_min.mp414.00MB
03_Lecture3/04_The_backpropagation_algorithm_12_min.pdf3.10MB
03_Lecture3/04_The_backpropagation_algorithm_12_min.srt15.23kB
03_Lecture3/04_The_backpropagation_algorithm_12_min.txt9.98kB
03_Lecture3/05_Using_the_derivatives_computed_by_backpropagation_10_min.mp411.70MB
03_Lecture3/05_Using_the_derivatives_computed_by_backpropagation_10_min.srt13.91kB
03_Lecture3/05_Using_the_derivatives_computed_by_backpropagation_10_min.txt9.12kB
04_Lecture4/01_Learning_to_predict_the_next_word_13_min.mp414.97MB
04_Lecture4/01_Learning_to_predict_the_next_word_13_min.pdf964.08kB
04_Lecture4/01_Learning_to_predict_the_next_word_13_min.pptx1.15MB
04_Lecture4/01_Learning_to_predict_the_next_word_13_min.srt16.88kB
04_Lecture4/01_Learning_to_predict_the_next_word_13_min.txt11.06kB
04_Lecture4/02_A_brief_diversion_into_cognitive_science_4_min.mp45.57MB
04_Lecture4/02_A_brief_diversion_into_cognitive_science_4_min.srt5.89kB
04_Lecture4/02_A_brief_diversion_into_cognitive_science_4_min.txt3.83kB
04_Lecture4/03_Another_diversion-_The_softmax_output_function_7_min.mp48.42MB
04_Lecture4/03_Another_diversion-_The_softmax_output_function_7_min.srt9.29kB
04_Lecture4/03_Another_diversion-_The_softmax_output_function_7_min.txt6.08kB
04_Lecture4/04_Neuro-probabilistic_language_models_8_min.mp49.37MB
04_Lecture4/04_Neuro-probabilistic_language_models_8_min.pdf140.10kB
04_Lecture4/04_Neuro-probabilistic_language_models_8_min.srt10.96kB
04_Lecture4/04_Neuro-probabilistic_language_models_8_min.txt7.16kB
04_Lecture4/05_Ways_to_deal_with_the_large_number_of_possible_outputs_15_min.mp414.95MB
04_Lecture4/05_Ways_to_deal_with_the_large_number_of_possible_outputs_15_min.png154.42kB
04_Lecture4/05_Ways_to_deal_with_the_large_number_of_possible_outputs_15_min.srt18.56kB
04_Lecture4/05_Ways_to_deal_with_the_large_number_of_possible_outputs_15_min.txt12.02kB
05_Lecture5/01_Why_object_recognition_is_difficult_5_min.mp45.63MB
05_Lecture5/01_Why_object_recognition_is_difficult_5_min.pdf1.63MB
05_Lecture5/01_Why_object_recognition_is_difficult_5_min.pptx1.73MB
05_Lecture5/01_Why_object_recognition_is_difficult_5_min.srt6.31kB
05_Lecture5/01_Why_object_recognition_is_difficult_5_min.txt4.13kB
05_Lecture5/02_Achieving_viewpoint_invariance_6_min.mp47.23MB
05_Lecture5/02_Achieving_viewpoint_invariance_6_min.srt8.31kB
05_Lecture5/02_Achieving_viewpoint_invariance_6_min.txt5.36kB
05_Lecture5/03_Convolutional_nets_for_digit_recognition_16_min.mp419.36MB
05_Lecture5/03_Convolutional_nets_for_digit_recognition_16_min.srt22.06kB
05_Lecture5/03_Convolutional_nets_for_digit_recognition_16_min.txt14.23kB
05_Lecture5/04_Convolutional_nets_for_object_recognition_17min.mp424.15MB
05_Lecture5/04_Convolutional_nets_for_object_recognition_17min.pdf125.35kB
05_Lecture5/04_Convolutional_nets_for_object_recognition_17min.srt26.25kB
05_Lecture5/04_Convolutional_nets_for_object_recognition_17min.txt17.01kB
05_Lecture5/04_Convolutional_nets_for_object_recognition_17min_0_hard_Gradient-based_learning_applied_to_document_recognition.pdf955.06kB
06_Lecture6/01_Overview_of_mini-batch_gradient_descent.mp410.07MB
06_Lecture6/01_Overview_of_mini-batch_gradient_descent.pdf546.84kB
06_Lecture6/01_Overview_of_mini-batch_gradient_descent.pptx672.62kB
06_Lecture6/01_Overview_of_mini-batch_gradient_descent.srt12.24kB
06_Lecture6/01_Overview_of_mini-batch_gradient_descent.txt7.98kB
06_Lecture6/02_A_bag_of_tricks_for_mini-batch_gradient_descent.mp415.62MB
06_Lecture6/02_A_bag_of_tricks_for_mini-batch_gradient_descent.srt19.22kB
06_Lecture6/02_A_bag_of_tricks_for_mini-batch_gradient_descent.txt12.45kB
06_Lecture6/03_The_momentum_method.mp410.21MB
06_Lecture6/03_The_momentum_method.srt11.40kB
06_Lecture6/03_The_momentum_method.txt7.41kB
06_Lecture6/04_Adaptive_learning_rates_for_each_connection.mp46.95MB
06_Lecture6/04_Adaptive_learning_rates_for_each_connection.srt7.91kB
06_Lecture6/04_Adaptive_learning_rates_for_each_connection.txt5.19kB
06_Lecture6/05_Rmsprop-_Divide_the_gradient_by_a_running_average_of_its_recent_magnitude.mp415.85MB
06_Lecture6/05_Rmsprop-_Divide_the_gradient_by_a_running_average_of_its_recent_magnitude.srt16.06kB
06_Lecture6/05_Rmsprop-_Divide_the_gradient_by_a_running_average_of_its_recent_magnitude.txt10.47kB
07_Lecture7/01_Modeling_sequences-_A_brief_overview.mp421.11MB
07_Lecture7/01_Modeling_sequences-_A_brief_overview.pdf975.96kB
07_Lecture7/01_Modeling_sequences-_A_brief_overview.pptx228.02kB
07_Lecture7/01_Modeling_sequences-_A_brief_overview.srt23.19kB
07_Lecture7/01_Modeling_sequences-_A_brief_overview.txt15.05kB
07_Lecture7/02_Training_RNNs_with_back_propagation.mp47.68MB
07_Lecture7/02_Training_RNNs_with_back_propagation.srt8.57kB
07_Lecture7/02_Training_RNNs_with_back_propagation.txt5.65kB
07_Lecture7/03_A_toy_example_of_training_an_RNN.mp47.59MB
07_Lecture7/03_A_toy_example_of_training_an_RNN.srt7.70kB
07_Lecture7/03_A_toy_example_of_training_an_RNN.txt5.01kB
07_Lecture7/04_Why_it_is_difficult_to_train_an_RNN.mp49.32MB
07_Lecture7/04_Why_it_is_difficult_to_train_an_RNN.srt10.03kB
07_Lecture7/04_Why_it_is_difficult_to_train_an_RNN.txt6.60kB
07_Lecture7/05_Long-term_Short-term-memory.mp410.73MB
07_Lecture7/05_Long-term_Short-term-memory.pdf320.59kB
07_Lecture7/05_Long-term_Short-term-memory.srt11.90kB
07_Lecture7/05_Long-term_Short-term-memory.txt7.86kB
08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.mp417.03MB
08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.pdf658.31kB
08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.pptx568.19kB
08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.srt18.38kB
08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.txt11.92kB
08_Lecture8/02_Modeling_character_strings_with_multiplicative_connections_14_mins.mp417.36MB
08_Lecture8/02_Modeling_character_strings_with_multiplicative_connections_14_mins.srt17.90kB
08_Lecture8/02_Modeling_character_strings_with_multiplicative_connections_14_mins.txt11.78kB
08_Lecture8/03_Learning_to_predict_the_next_character_using_HF_12__mins.mp414.60MB
08_Lecture8/03_Learning_to_predict_the_next_character_using_HF_12__mins.pdf273.40kB
08_Lecture8/03_Learning_to_predict_the_next_character_using_HF_12__mins.srt16.11kB
08_Lecture8/03_Learning_to_predict_the_next_character_using_HF_12__mins.txt10.37kB
08_Lecture8/04_Echo_State_Networks_9_min.mp411.82MB
08_Lecture8/04_Echo_State_Networks_9_min.srt12.27kB
08_Lecture8/04_Echo_State_Networks_9_min.txt8.04kB
09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.mp414.23MB
09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.pdf718.98kB
09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.pptx1.55MB
09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.srt16.18kB
09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.txt10.59kB
09_Lecture9/02_Limiting_the_size_of_the_weights_6_min.mp47.72MB
09_Lecture9/02_Limiting_the_size_of_the_weights_6_min.srt8.61kB
09_Lecture9/02_Limiting_the_size_of_the_weights_6_min.txt5.63kB
09_Lecture9/03_Using_noise_as_a_regularizer_7_min.mp48.90MB
09_Lecture9/03_Using_noise_as_a_regularizer_7_min.srt9.08kB
09_Lecture9/03_Using_noise_as_a_regularizer_7_min.txt5.98kB
09_Lecture9/04_Introduction_to_the_full_Bayesian_approach_12_min.mp412.59MB
09_Lecture9/04_Introduction_to_the_full_Bayesian_approach_12_min.srt13.49kB
09_Lecture9/04_Introduction_to_the_full_Bayesian_approach_12_min.txt8.78kB
09_Lecture9/05_The_Bayesian_interpretation_of_weight_decay_11_min.mp412.87MB
09_Lecture9/05_The_Bayesian_interpretation_of_weight_decay_11_min.srt13.33kB
09_Lecture9/05_The_Bayesian_interpretation_of_weight_decay_11_min.txt8.81kB
09_Lecture9/06_MacKays_quick_and_dirty_method_of_setting_weight_costs_4_min.mp44.59MB
09_Lecture9/06_MacKays_quick_and_dirty_method_of_setting_weight_costs_4_min.srt4.51kB
09_Lecture9/06_MacKays_quick_and_dirty_method_of_setting_weight_costs_4_min.txt2.97kB
10_Lecture10/01_Why_it_helps_to_combine_models_13_min.mp415.86MB
10_Lecture10/01_Why_it_helps_to_combine_models_13_min.pdf847.10kB
10_Lecture10/01_Why_it_helps_to_combine_models_13_min.pptx901.58kB
10_Lecture10/01_Why_it_helps_to_combine_models_13_min.srt18.10kB
10_Lecture10/01_Why_it_helps_to_combine_models_13_min.txt11.75kB
10_Lecture10/02_Mixtures_of_Experts_13_min.mp415.71MB
10_Lecture10/02_Mixtures_of_Experts_13_min.pdf271.11kB
10_Lecture10/02_Mixtures_of_Experts_13_min.srt17.47kB
10_Lecture10/02_Mixtures_of_Experts_13_min.txt11.42kB
10_Lecture10/03_The_idea_of_full_Bayesian_learning_7_min.mp48.80MB
10_Lecture10/03_The_idea_of_full_Bayesian_learning_7_min.srt10.53kB
10_Lecture10/03_The_idea_of_full_Bayesian_learning_7_min.txt6.88kB
10_Lecture10/04_Making_full_Bayesian_learning_practical_7_min.mp48.53MB
10_Lecture10/04_Making_full_Bayesian_learning_practical_7_min.srt8.66kB
10_Lecture10/04_Making_full_Bayesian_learning_practical_7_min.txt5.71kB
10_Lecture10/05_Dropout_9_min.mp410.16MB
10_Lecture10/05_Dropout_9_min.pdf1.67MB
10_Lecture10/05_Dropout_9_min.srt11.97kB
10_Lecture10/05_Dropout_9_min.txt7.75kB
11_Lecture11/01_Hopfield_Nets_13_min.mp415.36MB
11_Lecture11/01_Hopfield_Nets_13_min.pdf711.19kB
11_Lecture11/01_Hopfield_Nets_13_min.pptx743.83kB
11_Lecture11/01_Hopfield_Nets_13_min.srt16.76kB
11_Lecture11/01_Hopfield_Nets_13_min.txt10.86kB
11_Lecture11/02_Dealing_with_spurious_minima_11_min.mp413.39MB
11_Lecture11/02_Dealing_with_spurious_minima_11_min.srt15.21kB
11_Lecture11/02_Dealing_with_spurious_minima_11_min.txt10.00kB
11_Lecture11/03_Hopfield_nets_with_hidden_units_10_min.mp411.86MB
11_Lecture11/03_Hopfield_nets_with_hidden_units_10_min.srt12.59kB
11_Lecture11/03_Hopfield_nets_with_hidden_units_10_min.txt8.29kB
11_Lecture11/04_Using_stochastic_units_to_improv_search_11_min.mp412.34MB
11_Lecture11/04_Using_stochastic_units_to_improv_search_11_min.srt14.33kB
11_Lecture11/04_Using_stochastic_units_to_improv_search_11_min.txt9.34kB
11_Lecture11/05_How_a_Boltzmann_machine_models_data_12_min.mp413.93MB
11_Lecture11/05_How_a_Boltzmann_machine_models_data_12_min.srt16.27kB
11_Lecture11/05_How_a_Boltzmann_machine_models_data_12_min.txt10.53kB
12_Lecture12/01_Boltzmann_machine_learning_12_min.mp414.71MB
12_Lecture12/01_Boltzmann_machine_learning_12_min.pdf1.83MB
12_Lecture12/01_Boltzmann_machine_learning_12_min.pptx1.97MB
12_Lecture12/01_Boltzmann_machine_learning_12_min.srt16.41kB
12_Lecture12/01_Boltzmann_machine_learning_12_min.txt10.70kB
12_Lecture12/02_OPTIONAL_VIDEO-_More_efficient_ways_to_get_the_statistics_15_mins.mp417.76MB
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Type: Course
Tags: Coursera, neuralnets

Bibtex:
@article{,
    title = {[Coursera] Neural Networks for Machine Learning (University of Toronto) (neuralnets)},
    author = {University of Toronto}
    }