Neuralnets 2012 001 09 Lecture9 04 Introduction to the full Bayesian approach 12 min txt

Neuralnets 2012 001
09 Lecture9 04 Introduction to the full Bayesian approach 12 min txt

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/09_Lecture9/04_Introduction_to_the_full_Bayesian_approach_12_min.txt	8.8 KB
/09_Lecture9/04_Introduction_to_the_full_Bayesian_approach_12_min.srt	13.5 KB
/09_Lecture9/04_Introduction_to_the_full_Bayesian_approach_12_min.mp4	12.6 MB
/10_Lecture10/03_The_idea_of_full_Bayesian_learning_7_min.txt	6.9 KB
/10_Lecture10/03_The_idea_of_full_Bayesian_learning_7_min.srt	10.5 KB
/10_Lecture10/03_The_idea_of_full_Bayesian_learning_7_min.mp4	8.8 MB
/04_Lecture4/01_Learning_to_predict_the_next_word_13_min.txt	11.1 KB
/09_Lecture9/02_Limiting_the_size_of_the_weights_6_min.txt	5.6 KB
/01_Lecture1/01_Why_do_we_need_machine_learning_13_min.txt	12.2 KB
/02_Lecture2/04_Why_the_learning_works_5_min.txt	4.3 KB
/03_Lecture3/01_Learning_the_weights_of_a_linear_neuron_12_min.txt	10.1 KB
/03_Lecture3/04_The_backpropagation_algorithm_12_min.txt	10.0 KB
/09_Lecture9/03_Using_noise_as_a_regularizer_7_min.txt	6.0 KB
/04_Lecture4/01_Learning_to_predict_the_next_word_13_min.srt	16.9 KB
/05_Lecture5/03_Convolutional_nets_for_digit_recognition_16_min.txt	14.2 KB
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