[Coursera] Neural Networks for Machine Learning (University of Toronto) (neuralnets)
University of Toronto

neuralnets-2012-001 (378 files)
01_Lecture1/01_Why_do_we_need_machine_learning_13_min.mp4 15.78MB
01_Lecture1/01_Why_do_we_need_machine_learning_13_min.pdf 4.06MB
01_Lecture1/01_Why_do_we_need_machine_learning_13_min.pptx 3.80MB
01_Lecture1/01_Why_do_we_need_machine_learning_13_min.srt 18.78kB
01_Lecture1/01_Why_do_we_need_machine_learning_13_min.txt 12.17kB
01_Lecture1/02_What_are_neural_networks_8_min.mp4 10.23MB
01_Lecture1/02_What_are_neural_networks_8_min.srt 11.78kB
01_Lecture1/02_What_are_neural_networks_8_min.txt 7.67kB
01_Lecture1/03_Some_simple_models_of_neurons_8_min.mp4 9.71MB
01_Lecture1/03_Some_simple_models_of_neurons_8_min.srt 10.95kB
01_Lecture1/03_Some_simple_models_of_neurons_8_min.txt 7.11kB
01_Lecture1/04_A_simple_example_of_learning_6_min.mp4 6.89MB
01_Lecture1/04_A_simple_example_of_learning_6_min.srt 7.19kB
01_Lecture1/04_A_simple_example_of_learning_6_min.txt 4.76kB
01_Lecture1/05_Three_types_of_learning_8_min.mp4 9.39MB
01_Lecture1/05_Three_types_of_learning_8_min.srt 10.64kB
01_Lecture1/05_Three_types_of_learning_8_min.txt 6.99kB
02_Lecture2/01_Types_of_neural_network_architectures_7_min.mp4 9.20MB
02_Lecture2/01_Types_of_neural_network_architectures_7_min.pdf 504.78kB
02_Lecture2/01_Types_of_neural_network_architectures_7_min.pptx 409.21kB
02_Lecture2/01_Types_of_neural_network_architectures_7_min.srt 10.09kB
02_Lecture2/01_Types_of_neural_network_architectures_7_min.txt 6.62kB
02_Lecture2/02_Perceptrons-_The_first_generation_of_neural_networks_8_min.mp4 9.85MB
02_Lecture2/02_Perceptrons-_The_first_generation_of_neural_networks_8_min.srt 11.12kB
02_Lecture2/02_Perceptrons-_The_first_generation_of_neural_networks_8_min.txt 7.32kB
02_Lecture2/03_A_geometrical_view_of_perceptrons_6_min.mp4 7.68MB
02_Lecture2/03_A_geometrical_view_of_perceptrons_6_min.srt 8.49kB
02_Lecture2/03_A_geometrical_view_of_perceptrons_6_min.txt 5.51kB
02_Lecture2/04_Why_the_learning_works_5_min.mp4 6.18MB
02_Lecture2/04_Why_the_learning_works_5_min.srt 6.56kB
02_Lecture2/04_Why_the_learning_works_5_min.txt 4.31kB
02_Lecture2/05_What_perceptrons_cant_do_15_min.mp4 17.38MB
02_Lecture2/05_What_perceptrons_cant_do_15_min.srt 18.95kB
02_Lecture2/05_What_perceptrons_cant_do_15_min.txt 12.37kB
03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.mp4 14.18MB
03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.pdf 548.03kB
03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.pptx 1.19MB
03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.srt 15.45kB
03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.txt 10.13kB
03_Lecture3/02_The_error_surface_for_a_linear_neuron_5_min.mp4 6.18MB
03_Lecture3/02_The_error_surface_for_a_linear_neuron_5_min.srt 6.46kB
03_Lecture3/02_The_error_surface_for_a_linear_neuron_5_min.txt 4.21kB
03_Lecture3/03_Learning_the_weights_of_a_logistic_output_neuron_4_min.mp4 4.59MB
03_Lecture3/03_Learning_the_weights_of_a_logistic_output_neuron_4_min.srt 4.56kB
03_Lecture3/03_Learning_the_weights_of_a_logistic_output_neuron_4_min.txt 3.02kB
03_Lecture3/04_The_backpropagation_algorithm_12_min.mp4 14.00MB
03_Lecture3/04_The_backpropagation_algorithm_12_min.pdf 3.10MB
03_Lecture3/04_The_backpropagation_algorithm_12_min.srt 15.23kB
03_Lecture3/04_The_backpropagation_algorithm_12_min.txt 9.98kB
03_Lecture3/05_Using_the_derivatives_computed_by_backpropagation_10_min.mp4 11.70MB
03_Lecture3/05_Using_the_derivatives_computed_by_backpropagation_10_min.srt 13.91kB
03_Lecture3/05_Using_the_derivatives_computed_by_backpropagation_10_min.txt 9.12kB
04_Lecture4/01_Learning_to_predict_the_next_word_13_min.mp4 14.97MB
04_Lecture4/01_Learning_to_predict_the_next_word_13_min.pdf 964.08kB
04_Lecture4/01_Learning_to_predict_the_next_word_13_min.pptx 1.15MB
04_Lecture4/01_Learning_to_predict_the_next_word_13_min.srt 16.88kB
04_Lecture4/01_Learning_to_predict_the_next_word_13_min.txt 11.06kB
04_Lecture4/02_A_brief_diversion_into_cognitive_science_4_min.mp4 5.57MB
04_Lecture4/02_A_brief_diversion_into_cognitive_science_4_min.srt 5.89kB
04_Lecture4/02_A_brief_diversion_into_cognitive_science_4_min.txt 3.83kB
04_Lecture4/03_Another_diversion-_The_softmax_output_function_7_min.mp4 8.42MB
04_Lecture4/03_Another_diversion-_The_softmax_output_function_7_min.srt 9.29kB
04_Lecture4/03_Another_diversion-_The_softmax_output_function_7_min.txt 6.08kB
04_Lecture4/04_Neuro-probabilistic_language_models_8_min.mp4 9.37MB
04_Lecture4/04_Neuro-probabilistic_language_models_8_min.pdf 140.10kB
04_Lecture4/04_Neuro-probabilistic_language_models_8_min.srt 10.96kB
04_Lecture4/04_Neuro-probabilistic_language_models_8_min.txt 7.16kB
04_Lecture4/05_Ways_to_deal_with_the_large_number_of_possible_outputs_15_min.mp4 14.95MB
04_Lecture4/05_Ways_to_deal_with_the_large_number_of_possible_outputs_15_min.png 154.42kB
04_Lecture4/05_Ways_to_deal_with_the_large_number_of_possible_outputs_15_min.srt 18.56kB
04_Lecture4/05_Ways_to_deal_with_the_large_number_of_possible_outputs_15_min.txt 12.02kB
05_Lecture5/01_Why_object_recognition_is_difficult_5_min.mp4 5.63MB
05_Lecture5/01_Why_object_recognition_is_difficult_5_min.pdf 1.63MB
05_Lecture5/01_Why_object_recognition_is_difficult_5_min.pptx 1.73MB
05_Lecture5/01_Why_object_recognition_is_difficult_5_min.srt 6.31kB
05_Lecture5/01_Why_object_recognition_is_difficult_5_min.txt 4.13kB
05_Lecture5/02_Achieving_viewpoint_invariance_6_min.mp4 7.23MB
05_Lecture5/02_Achieving_viewpoint_invariance_6_min.srt 8.31kB
05_Lecture5/02_Achieving_viewpoint_invariance_6_min.txt 5.36kB
05_Lecture5/03_Convolutional_nets_for_digit_recognition_16_min.mp4 19.36MB
05_Lecture5/03_Convolutional_nets_for_digit_recognition_16_min.srt 22.06kB
05_Lecture5/03_Convolutional_nets_for_digit_recognition_16_min.txt 14.23kB
05_Lecture5/04_Convolutional_nets_for_object_recognition_17min.mp4 24.15MB
05_Lecture5/04_Convolutional_nets_for_object_recognition_17min.pdf 125.35kB
05_Lecture5/04_Convolutional_nets_for_object_recognition_17min.srt 26.25kB
05_Lecture5/04_Convolutional_nets_for_object_recognition_17min.txt 17.01kB
05_Lecture5/04_Convolutional_nets_for_object_recognition_17min_0_hard_Gradient-based_learning_applied_to_document_recognition.pdf 955.06kB
06_Lecture6/01_Overview_of_mini-batch_gradient_descent.mp4 10.07MB
06_Lecture6/01_Overview_of_mini-batch_gradient_descent.pdf 546.84kB
06_Lecture6/01_Overview_of_mini-batch_gradient_descent.pptx 672.62kB
06_Lecture6/01_Overview_of_mini-batch_gradient_descent.srt 12.24kB
06_Lecture6/01_Overview_of_mini-batch_gradient_descent.txt 7.98kB
06_Lecture6/02_A_bag_of_tricks_for_mini-batch_gradient_descent.mp4 15.62MB
06_Lecture6/02_A_bag_of_tricks_for_mini-batch_gradient_descent.srt 19.22kB
06_Lecture6/02_A_bag_of_tricks_for_mini-batch_gradient_descent.txt 12.45kB
06_Lecture6/03_The_momentum_method.mp4 10.21MB
06_Lecture6/03_The_momentum_method.srt 11.40kB
06_Lecture6/03_The_momentum_method.txt 7.41kB
06_Lecture6/04_Adaptive_learning_rates_for_each_connection.mp4 6.95MB
06_Lecture6/04_Adaptive_learning_rates_for_each_connection.srt 7.91kB
06_Lecture6/04_Adaptive_learning_rates_for_each_connection.txt 5.19kB
06_Lecture6/05_Rmsprop-_Divide_the_gradient_by_a_running_average_of_its_recent_magnitude.mp4 15.85MB
06_Lecture6/05_Rmsprop-_Divide_the_gradient_by_a_running_average_of_its_recent_magnitude.srt 16.06kB
06_Lecture6/05_Rmsprop-_Divide_the_gradient_by_a_running_average_of_its_recent_magnitude.txt 10.47kB
07_Lecture7/01_Modeling_sequences-_A_brief_overview.mp4 21.11MB
07_Lecture7/01_Modeling_sequences-_A_brief_overview.pdf 975.96kB
07_Lecture7/01_Modeling_sequences-_A_brief_overview.pptx 228.02kB
07_Lecture7/01_Modeling_sequences-_A_brief_overview.srt 23.19kB
07_Lecture7/01_Modeling_sequences-_A_brief_overview.txt 15.05kB
07_Lecture7/02_Training_RNNs_with_back_propagation.mp4 7.68MB
07_Lecture7/02_Training_RNNs_with_back_propagation.srt 8.57kB
07_Lecture7/02_Training_RNNs_with_back_propagation.txt 5.65kB
07_Lecture7/03_A_toy_example_of_training_an_RNN.mp4 7.59MB
07_Lecture7/03_A_toy_example_of_training_an_RNN.srt 7.70kB
07_Lecture7/03_A_toy_example_of_training_an_RNN.txt 5.01kB
07_Lecture7/04_Why_it_is_difficult_to_train_an_RNN.mp4 9.32MB
07_Lecture7/04_Why_it_is_difficult_to_train_an_RNN.srt 10.03kB
07_Lecture7/04_Why_it_is_difficult_to_train_an_RNN.txt 6.60kB
07_Lecture7/05_Long-term_Short-term-memory.mp4 10.73MB
07_Lecture7/05_Long-term_Short-term-memory.pdf 320.59kB
07_Lecture7/05_Long-term_Short-term-memory.srt 11.90kB
07_Lecture7/05_Long-term_Short-term-memory.txt 7.86kB
08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.mp4 17.03MB
08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.pdf 658.31kB
08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.pptx 568.19kB
08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.srt 18.38kB
08_Lecture8/01_A_brief_overview_of_Hessian_Free_optimization.txt 11.92kB
08_Lecture8/02_Modeling_character_strings_with_multiplicative_connections_14_mins.mp4 17.36MB
08_Lecture8/02_Modeling_character_strings_with_multiplicative_connections_14_mins.srt 17.90kB
08_Lecture8/02_Modeling_character_strings_with_multiplicative_connections_14_mins.txt 11.78kB
08_Lecture8/03_Learning_to_predict_the_next_character_using_HF_12__mins.mp4 14.60MB
08_Lecture8/03_Learning_to_predict_the_next_character_using_HF_12__mins.pdf 273.40kB
08_Lecture8/03_Learning_to_predict_the_next_character_using_HF_12__mins.srt 16.11kB
08_Lecture8/03_Learning_to_predict_the_next_character_using_HF_12__mins.txt 10.37kB
08_Lecture8/04_Echo_State_Networks_9_min.mp4 11.82MB
08_Lecture8/04_Echo_State_Networks_9_min.srt 12.27kB
08_Lecture8/04_Echo_State_Networks_9_min.txt 8.04kB
09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.mp4 14.23MB
09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.pdf 718.98kB
09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.pptx 1.55MB
09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.srt 16.18kB
09_Lecture9/01_Overview_of_ways_to_improve_generalization_12_min.txt 10.59kB
09_Lecture9/02_Limiting_the_size_of_the_weights_6_min.mp4 7.72MB
09_Lecture9/02_Limiting_the_size_of_the_weights_6_min.srt 8.61kB
09_Lecture9/02_Limiting_the_size_of_the_weights_6_min.txt 5.63kB
09_Lecture9/03_Using_noise_as_a_regularizer_7_min.mp4 8.90MB
09_Lecture9/03_Using_noise_as_a_regularizer_7_min.srt 9.08kB
09_Lecture9/03_Using_noise_as_a_regularizer_7_min.txt 5.98kB
09_Lecture9/04_Introduction_to_the_full_Bayesian_approach_12_min.mp4 12.59MB
09_Lecture9/04_Introduction_to_the_full_Bayesian_approach_12_min.srt 13.49kB
09_Lecture9/04_Introduction_to_the_full_Bayesian_approach_12_min.txt 8.78kB
09_Lecture9/05_The_Bayesian_interpretation_of_weight_decay_11_min.mp4 12.87MB
09_Lecture9/05_The_Bayesian_interpretation_of_weight_decay_11_min.srt 13.33kB
09_Lecture9/05_The_Bayesian_interpretation_of_weight_decay_11_min.txt 8.81kB
09_Lecture9/06_MacKays_quick_and_dirty_method_of_setting_weight_costs_4_min.mp4 4.59MB
09_Lecture9/06_MacKays_quick_and_dirty_method_of_setting_weight_costs_4_min.srt 4.51kB
09_Lecture9/06_MacKays_quick_and_dirty_method_of_setting_weight_costs_4_min.txt 2.97kB
10_Lecture10/01_Why_it_helps_to_combine_models_13_min.mp4 15.86MB
10_Lecture10/01_Why_it_helps_to_combine_models_13_min.pdf 847.10kB
10_Lecture10/01_Why_it_helps_to_combine_models_13_min.pptx 901.58kB
10_Lecture10/01_Why_it_helps_to_combine_models_13_min.srt 18.10kB
10_Lecture10/01_Why_it_helps_to_combine_models_13_min.txt 11.75kB
10_Lecture10/02_Mixtures_of_Experts_13_min.mp4 15.71MB
10_Lecture10/02_Mixtures_of_Experts_13_min.pdf 271.11kB
10_Lecture10/02_Mixtures_of_Experts_13_min.srt 17.47kB
10_Lecture10/02_Mixtures_of_Experts_13_min.txt 11.42kB
10_Lecture10/03_The_idea_of_full_Bayesian_learning_7_min.mp4 8.80MB
10_Lecture10/03_The_idea_of_full_Bayesian_learning_7_min.srt 10.53kB
10_Lecture10/03_The_idea_of_full_Bayesian_learning_7_min.txt 6.88kB
10_Lecture10/04_Making_full_Bayesian_learning_practical_7_min.mp4 8.53MB
10_Lecture10/04_Making_full_Bayesian_learning_practical_7_min.srt 8.66kB
10_Lecture10/04_Making_full_Bayesian_learning_practical_7_min.txt 5.71kB
10_Lecture10/05_Dropout_9_min.mp4 10.16MB
10_Lecture10/05_Dropout_9_min.pdf 1.67MB
10_Lecture10/05_Dropout_9_min.srt 11.97kB
10_Lecture10/05_Dropout_9_min.txt 7.75kB
11_Lecture11/01_Hopfield_Nets_13_min.mp4 15.36MB
11_Lecture11/01_Hopfield_Nets_13_min.pdf 711.19kB
11_Lecture11/01_Hopfield_Nets_13_min.pptx 743.83kB
11_Lecture11/01_Hopfield_Nets_13_min.srt 16.76kB
11_Lecture11/01_Hopfield_Nets_13_min.txt 10.86kB
11_Lecture11/02_Dealing_with_spurious_minima_11_min.mp4 13.39MB
11_Lecture11/02_Dealing_with_spurious_minima_11_min.srt 15.21kB
11_Lecture11/02_Dealing_with_spurious_minima_11_min.txt 10.00kB
11_Lecture11/03_Hopfield_nets_with_hidden_units_10_min.mp4 11.86MB
11_Lecture11/03_Hopfield_nets_with_hidden_units_10_min.srt 12.59kB
11_Lecture11/03_Hopfield_nets_with_hidden_units_10_min.txt 8.29kB
11_Lecture11/04_Using_stochastic_units_to_improv_search_11_min.mp4 12.34MB
11_Lecture11/04_Using_stochastic_units_to_improv_search_11_min.srt 14.33kB
11_Lecture11/04_Using_stochastic_units_to_improv_search_11_min.txt 9.34kB
11_Lecture11/05_How_a_Boltzmann_machine_models_data_12_min.mp4 13.93MB
11_Lecture11/05_How_a_Boltzmann_machine_models_data_12_min.srt 16.27kB
11_Lecture11/05_How_a_Boltzmann_machine_models_data_12_min.txt 10.53kB
12_Lecture12/01_Boltzmann_machine_learning_12_min.mp4 14.71MB
12_Lecture12/01_Boltzmann_machine_learning_12_min.pdf 1.83MB
12_Lecture12/01_Boltzmann_machine_learning_12_min.pptx 1.97MB
12_Lecture12/01_Boltzmann_machine_learning_12_min.srt 16.41kB
12_Lecture12/01_Boltzmann_machine_learning_12_min.txt 10.70kB
12_Lecture12/02_OPTIONAL_VIDEO-_More_efficient_ways_to_get_the_statistics_15_mins.mp4 17.76MB
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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}
    }


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