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[人工智能] 684个视频教程 国外关于深度学习的新版视频教程 全英文+英文字幕
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 684个视频教程 国外关于深度学习的新版视频教程 全英文+英文字幕 
684个视频教程 国外关于深度学习的新版视频教程 全英文+英文字幕 
684个视频教程 国外关于深度学习的新版视频教程 全英文+英文字幕 
课程介绍: 都是一些关于大数据深度学习的视频教程,国外教授录制,带英文字幕. 详细目录: ├─00_Neural Networks for Machine Learning │ └─00_Neural Networks for Machine Learning │ ├─hinton-ml │ │ 1.Why do we need machine learning │ │ 1.Why do we need machine learning.mp4 │ │ 10.What perceptrons can't do [15 min].mp4 │ │ 10.What perceptrons can't do [15 min].srt │ │ 11.Learning the weights of a linear neuron [12 min].mp4 │ │ 11.Learning the weights of a linear neuron [12 min].srt │ │ 12.The error surface for a linear neuron [5 min].mp4 │ │ 12.The error surface for a linear neuron [5 min].srt │ │ 13.Learning the weights of a logistic output neuron [4 min].mp4 │ │ 13.Learning the weights of a logistic output neuron [4 min].srt │ │ 14.The backpropagation algorithm [12 min].mp4 │ │ 14.The backpropagation algorithm [12 min].srt │ │ 15.Using the derivatives computed by backpropagation [10 min].mp4 │ │ 15.Using the derivatives computed by backpropagation [10 min].srt │ │ 16.Learning to predict the next word [13 min].mp4 │ │ 16.Learning to predict the next word [13 min].srt │ │ 17.A brief diversion into cognitive science [4 min].mp4 │ │ 17.A brief diversion into cognitive science [4 min].srt │ │ 19.Neuro-probabilistic language models [8 min].mp4 │ │ 19.Neuro-probabilistic language models [8 min].srt │ │ 2.What are neural networks │ │ 2.What are neural networks.mp4 │ │ 20.Ways to deal with the large number of possible outputs [15 min].mp4 │ │ 20.Ways to deal with the large number of possible outputs [15 min].srt │ │ 21.Why object recognition is difficult [5 min].mp4 │ │ 21.Why object recognition is difficult [5 min].srt │ │ 22.Achieving viewpoint invariance [6 min].mp4 │ │ 22.Achieving viewpoint invariance [6 min].srt │ │ 23.Convolutional nets for digit recognition [16 min].mp4 │ │ 23.Convolutional nets for digit recognition [16 min].srt │ │ 24.Convolutional nets for object recognition [17min].mp4 │ │ 24.Convolutional nets for object recognition [17min].srt │ │ 25.Overview of mini-batch gradient descent.mp4 │ │ 25.Overview of mini-batch gradient descent.srt │ │ 26.A bag of tricks for mini-batch gradient descent.mp4 │ │ 26.A bag of tricks for mini-batch gradient descent.srt │ │ 27.The momentum method.mp4 │ │ 27.The momentum method.srt │ │ 28.Adaptive learning rates for each connection.mp4 │ │ 28.Adaptive learning rates for each connection.srt │ │ 3.Some simple models of neurons [8 min].mp4 │ │ 3.Some simple models of neurons [8 min].srt │ │ 31.Training RNNs with back propagation.mp4 │ │ 31.Training RNNs with back propagation.srt │ │ 32.A toy example of training an RNN.mp4 │ │ 32.A toy example of training an RNN.srt │ │ 33.Why it is difficult to train an RNN.mp4 │ │ 33.Why it is difficult to train an RNN.srt │ │ 34.Long-term Short-term-memory.mp4 │ │ 34.Long-term Short-term-memory.srt │ │ 35.A brief overview of Hessian Free optimization.mp4 │ │ 35.A brief overview of Hessian Free optimization.srt │ │ 37.Learning to predict the next character using HF [12 mins].mp4 │ │ 37.Learning to predict the next character using HF [12 mins].srt │ │ 38.Echo State Networks [9 min].mp4 │ │ 38.Echo State Networks [9 min].srt │ │ 39.Overview of ways to improve generalization [12 min].mp4 │ │ 39.Overview of ways to improve generalization [12 min].srt │ │ 4.A simple example of learning [6 min].mp4 │ │ 4.A simple example of learning [6 min].srt │ │ 40.Limiting the size of the weights [6 min].mp4 │ │ 40.Limiting the size of the weights [6 min].srt │ │ 41.Using noise as a regularizer [7 min].mp4 │ │ 41.Using noise as a regularizer [7 min].srt │ │ 42.Introduction to the full Bayesian approach [12 min].mp4 │ │ 42.Introduction to the full Bayesian approach [12 min].srt │ │ 43.The Bayesian interpretation of weight decay [11 min].mp4 │ │ 43.The Bayesian interpretation of weight decay [11 min].srt │ │ 44.MacKay's quick and dirty method of setting weight costs [4 min].mp4 │ │ 44.MacKay's quick and dirty method of setting weight costs [4 min].srt │ │ 45.Why it helps to combine models [13 min].mp4 │ │ 45.Why it helps to combine models [13 min].srt │ │ 46.Mixtures of Experts [13 min].mp4 │ │ 46.Mixtures of Experts [13 min].srt │ │ 47.The idea of full Bayesian learning [7 min].mp4 │ │ 47.The idea of full Bayesian learning [7 min].srt │ │ 48.Making full Bayesian learning practical [7 min].mp4 │ │ 48.Making full Bayesian learning practical [7 min].srt │ │ 49.Dropout [9 min].mp4 │ │ 49.Dropout [9 min].srt │ │ 5.Three types of learning [8 min].mp4 │ │ 5.Three types of learning [8 min].srt │ │ 50.Hopfield Nets [13 min].mp4 │ │ 50.Hopfield Nets [13 min].srt │ │ 51.Dealing with spurious minima [11 min].mp4 │ │ 51.Dealing with spurious minima [11 min].srt │ │ 52.Hopfield nets with hidden units [10 min].mp4 │ │ 52.Hopfield nets with hidden units [10 min].srt │ │ 53.Using stochastic units to improv search [11 min].mp4 │ │ 53.Using stochastic units to improv search [11 min].srt │ │ 54.How a Boltzmann machine models data [12 min].mp4 │ │ 54.How a Boltzmann machine models data [12 min].srt │ │ 55.Boltzmann machine learning [12 min].mp4 │ │ 55.Boltzmann machine learning [12 min].srt │ │ 57.Restricted Boltzmann Machines [11 min].mp4 │ │ 57.Restricted Boltzmann Machines [11 min].srt │ │ 58.An example of RBM learning [7 mins].mp4 │ │ 58.An example of RBM learning [7 mins].srt │ │ 59.RBMs for collaborative filtering [8 mins].mp4 │ │ 59.RBMs for collaborative filtering [8 mins].srt │ │ 6.Types of neural network architectures [7 min].mp4 │ │ 6.Types of neural network architectures [7 min].srt │ │ 60.The ups and downs of back propagation [10 min].mp4 │ │ 60.The ups and downs of back propagation [10 min].srt │ │ 61.Belief Nets [13 min].mp4 │ │ 61.Belief Nets [13 min].srt │ │ 62.Learning sigmoid belief nets [12 min].mp4 │ │ 62.Learning sigmoid belief nets [12 min].srt │ │ 63.The wake-sleep algorithm [13 min].mp4 │ │ 63.The wake-sleep algorithm [13 min].srt │ │ 64.Learning layers of features by stacking RBMs [17 min].mp4 │ │ 64.Learning layers of features by stacking RBMs [17 min].srt │ │ 65.Discriminative learning for DBNs [9 mins].mp4 │ │ 65.Discriminative learning for DBNs [9 mins].srt │ │ 66(1).What happens during discriminative fine-tuning │ │ 66.What happens during discriminative fine-tuning │ │ 67.Modeling real-valued data with an RBM [10 mins].mp4 │ │ 67.Modeling real-valued data with an RBM [10 mins].srt │ │ 69.From PCA to autoencoders [5 mins].mp4 │ │ 69.From PCA to autoencoders [5 mins].srt │ │ 70.Deep auto encoders [4 mins].mp4 │ │ 70.Deep auto encoders [4 mins].srt │ │ 71.Deep auto encoders for document retrieval [8 mins].mp4 │ │ 71.Deep auto encoders for document retrieval [8 mins].srt │ │ 72.Semantic Hashing [9 mins].mp4 │ │ 72.Semantic Hashing [9 mins].srt │ │ 73.Learning binary codes for image retrieval [9 mins].mp4 │ │ 73.Learning binary codes for image retrieval [9 mins].srt │ │ 74.Shallow autoencoders for pre-training [7 mins].mp4 │ │ 74.Shallow autoencoders for pre-training [7 mins].srt │ │ 8.A geometrical view of perceptrons [6 min].mp4 │ │ 8.A geometrical view of perceptrons [6 min].srt │ │ 9.Why the learning works [5 min].mp4 │ │ 9.Why the learning works [5 min].srt │ │ │ └─neuralnets-2012-001 │ ├─01_Lecture1 │ │ 01_Why_do_we_need_machine_learning_13_min.mp4 │ │ 01_Why_do_we_need_machine_learning_13_min.pdf │ │ 01_Why_do_we_need_machine_learning_13_min.pptx │ │ 01_Why_do_we_need_machine_learning_13_min.srt │ │ 01_Why_do_we_need_machine_learning_13_min.txt │ │ 02_What_are_neural_networks_8_min.mp4 │ │ 02_What_are_neural_networks_8_min.srt │ │ 02_What_are_neural_networks_8_min.txt │ │ 03_Some_simple_models_of_neurons_8_min.mp4 │ │ 03_Some_simple_models_of_neurons_8_min.srt │ │ 03_Some_simple_models_of_neurons_8_min.txt │ │ 04_A_simple_example_of_learning_6_min.mp4 │ │ 04_A_simple_example_of_learning_6_min.srt │ │ 04_A_simple_example_of_learning_6_min.txt │ │ 05_Three_types_of_learning_8_min.mp4 │ │ 05_Three_types_of_learning_8_min.srt │ │ 05_Three_types_of_learning_8_min.txt │ │ │ ├─02_Lecture2 │ │ 01_Types_of_neural_network_architectures_7_min.mp4 │ │ 01_Types_of_neural_network_architectures_7_min.pdf │ │ 01_Types_of_neural_network_architectures_7_min.pptx │ │ 01_Types_of_neural_network_architectures_7_min.srt │ │ 01_Types_of_neural_network_architectures_7_min.txt │ │ 03_A_geometrical_view_of_perceptrons_6_min.mp4 │ │ 03_A_geometrical_view_of_perceptrons_6_min.srt │ │ 03_A_geometrical_view_of_perceptrons_6_min.txt │ │ 04_Why_the_learning_works_5_min.mp4 │ │ 04_Why_the_learning_works_5_min.srt │ │ 04_Why_the_learning_works_5_min.txt │ │ 05_What_perceptrons_cant_do_15_min.mp4 │ │ 05_What_perceptrons_cant_do_15_min.srt │ │ 05_What_perceptrons_cant_do_15_min.txt │ │ │ ├─03_Lecture3 │ │ 02_The_error_surface_for_a_linear_neuron_5_min.mp4 │ │ 02_The_error_surface_for_a_linear_neuron_5_min.srt │ │ 02_The_error_surface_for_a_linear_neuron_5_min.txt │ │ 04_The_backpropagation_algorithm_12_min.mp4 │ │ 04_The_backpropagation_algorithm_12_min.pdf │ │ 04_The_backpropagation_algorithm_12_min.srt │ │ 04_The_backpropagation_algorithm_12_min.txt │ │ │ ├─04_Lecture4 │ │ 01_Learning_to_predict_the_next_word_13_min.mp4 │ │ 01_Learning_to_predict_the_next_word_13_min.pdf │ │ 01_Learning_to_predict_the_next_word_13_min.pptx │ │ 01_Learning_to_predict_the_next_word_13_min.srt │ │ 01_Learning_to_predict_the_next_word_13_min.txt │ │ 04_Neuro-probabilistic_language_models_8_min.mp4 │ │ 04_Neuro-probabilistic_language_models_8_min.pdf │ │ 04_Neuro-probabilistic_language_models_8_min.srt │ │ 04_Neuro-probabilistic_language_models_8_min.txt │ │ │ ├─05_Lecture5 │ │ 01_Why_object_recognition_is_difficult_5_min.mp4 │ │ 01_Why_object_recognition_is_difficult_5_min.pdf │ │ 01_Why_object_recognition_is_difficult_5_min.pptx │ │ 01_Why_object_recognition_is_difficult_5_min.srt │ │ 01_Why_object_recognition_is_difficult_5_min.txt │ │ 02_Achieving_viewpoint_invariance_6_min.mp4 │ │ 02_Achieving_viewpoint_invariance_6_min.srt │ │ 02_Achieving_viewpoint_invariance_6_min.txt │ │ │ ├─06_Lecture6 │ │ 01_Overview_of_mini-batch_gradient_descent.mp4 │ │ 01_Overview_of_mini-batch_gradient_descent.pdf │ │ 01_Overview_of_mini-batch_gradient_descent.pptx │ │ 01_Overview_of_mini-batch_gradient_descent.srt │ │ 01_Overview_of_mini-batch_gradient_descent.txt │ │ 03_The_momentum_method.mp4 │ │ 03_The_momentum_method.srt │ │ 03_The_momentum_method.txt │ │ 04_Adaptive_learning_rates_for_each_connection.mp4 │ │ 04_Adaptive_learning_rates_for_each_connection.srt │ │ 04_Adaptive_learning_rates_for_each_connection.txt │ │ │ ├─07_Lecture7 │ │ 01_Modeling_sequences-_A_brief_overview.mp4 │ │ 01_Modeling_sequences-_A_brief_overview.pdf │ │ 01_Modeling_sequences-_A_brief_overview.pptx │ │ 01_Modeling_sequences-_A_brief_overview.srt │ │ 01_Modeling_sequences-_A_brief_overview.txt │ │ 02_Training_RNNs_with_back_propagation.mp4 │ │ 02_Training_RNNs_with_back_propagation.srt │ │ 02_Training_RNNs_with_back_propagation.txt │ │ 03_A_toy_example_of_training_an_RNN.mp4 │ │ 03_A_toy_example_of_training_an_RNN.srt │ │ 03_A_toy_example_of_training_an_RNN.txt │ │ 04_Why_it_is_difficult_to_train_an_RNN.mp4 │ │ 04_Why_it_is_difficult_to_train_an_RNN.srt │ │ 04_Why_it_is_difficult_to_train_an_RNN.txt │ │ 05_Long-term_Short-term-memory.mp4 │ │ 05_Long-term_Short-term-memory.pdf │ │ 05_Long-term_Short-term-memory.srt │ │ 05_Long-term_Short-term-memory.txt │ │ │ ├─08_Lecture8 │ │ 01_A_brief_overview_of_Hessian_Free_optimization.mp4 │ │ 01_A_brief_overview_of_Hessian_Free_optimization.pdf │ │ 01_A_brief_overview_of_Hessian_Free_optimization.srt │ │ 01_A_brief_overview_of_Hessian_Free_optimization.txt │ │ 04_Echo_State_Networks_9_min.mp4 │ │ 04_Echo_State_Networks_9_min.srt │ │ 04_Echo_State_Networks_9_min.txt │ │ │ ├─09_Lecture9 │ │ 02_Limiting_the_size_of_the_weights_6_min.mp4 │ │ 02_Limiting_the_size_of_the_weights_6_min.srt │ │ 02_Limiting_the_size_of_the_weights_6_min.txt │ │ 03_Using_noise_as_a_regularizer_7_min.mp4 │ │ 03_Using_noise_as_a_regularizer_7_min.srt │ │ 03_Using_noise_as_a_regularizer_7_min.txt │ │ │ ├─10_Lecture10 │ │ 01_Why_it_helps_to_combine_models_13_min.mp4 │ │ 01_Why_it_helps_to_combine_models_13_min.pdf │ │ 01_Why_it_helps_to_combine_models_13_min.pptx │ │ 01_Why_it_helps_to_combine_models_13_min.srt │ │ 01_Why_it_helps_to_combine_models_13_min.txt │ │ 02_Mixtures_of_Experts_13_min.mp4 │ │ 02_Mixtures_of_Experts_13_min.pdf │ │ 02_Mixtures_of_Experts_13_min.srt │ │ 02_Mixtures_of_Experts_13_min.txt │ │ 03_The_idea_of_full_Bayesian_learning_7_min.mp4 │ │ 03_The_idea_of_full_Bayesian_learning_7_min.srt │ │ 03_The_idea_of_full_Bayesian_learning_7_min.txt │ │ 05_Dropout_9_min.mp4 │ │ 05_Dropout_9_min.pdf │ │ 05_Dropout_9_min.srt │ │ 05_Dropout_9_min.txt │ │ │ ├─11_Lecture11 │ │ 01_Hopfield_Nets_13_min.mp4 │ │ 01_Hopfield_Nets_13_min.pdf │ │ 01_Hopfield_Nets_13_min.pptx │ │ 01_Hopfield_Nets_13_min.srt │ │ 01_Hopfield_Nets_13_min.txt │ │ 02_Dealing_with_spurious_minima_11_min.mp4 │ │ 02_Dealing_with_spurious_minima_11_min.srt │ │ 02_Dealing_with_spurious_minima_11_min.txt │ │ 03_Hopfield_nets_with_hidden_units_10_min.mp4 │ │ 03_Hopfield_nets_with_hidden_units_10_min.srt │ │ 03_Hopfield_nets_with_hidden_units_10_min.txt │ │ 05_How_a_Boltzmann_machine_models_data_12_min.mp4 │ │ 05_How_a_Boltzmann_machine_models_data_12_min.srt │ │ 05_How_a_Boltzmann_machine_models_data_12_min.txt │ │ │ ├─12_Lecture12 │ │ 01_Boltzmann_machine_learning_12_min.mp4 │ │ 01_Boltzmann_machine_learning_12_min.pdf │ │ 01_Boltzmann_machine_learning_12_min.pptx │ │ 01_Boltzmann_machine_learning_12_min.srt │ │ 01_Boltzmann_machine_learning_12_min.txt │ │ 03_Restricted_Boltzmann_Machines_11_min.mp4 │ │ 03_Restricted_Boltzmann_Machines_11_min.srt │ │ 03_Restricted_Boltzmann_Machines_11_min.txt │ │ 04_An_example_of_RBM_learning_7_mins.mp4 │ │ 04_An_example_of_RBM_learning_7_mins.srt │ │ 04_An_example_of_RBM_learning_7_mins.txt │ │ 05_RBMs_for_collaborative_filtering_8_mins.mp4 │ │ 05_RBMs_for_collaborative_filtering_8_mins.srt │ │ 05_RBMs_for_collaborative_filtering_8_mins.txt │ │ │ ├─13_Lecture13 │ │ 01_The_ups_and_downs_of_back_propagation_10_min.mp4 │ │ 01_The_ups_and_downs_of_back_propagation_10_min.pdf │ │ 01_The_ups_and_downs_of_back_propagation_10_min.srt │ │ 01_The_ups_and_downs_of_back_propagation_10_min.txt │ │ 02_Belief_Nets_13_min.mp4 │ │ 02_Belief_Nets_13_min.srt │ │ 02_Belief_Nets_13_min.txt │ │ 03_Learning_sigmoid_belief_nets_12_min.mp4 │ │ 03_Learning_sigmoid_belief_nets_12_min.pdf │ │ 03_Learning_sigmoid_belief_nets_12_min.srt │ │ 03_Learning_sigmoid_belief_nets_12_min.txt │ │ 04_The_wake-sleep_algorithm_13_min.mp4 │ │ 04_The_wake-sleep_algorithm_13_min.pdf │ │ 04_The_wake-sleep_algorithm_13_min.srt │ │ 04_The_wake-sleep_algorithm_13_min.txt │ │ │ ├─14_Lecture14 │ │ 02_Discriminative_learning_for_DBNs_9_mins.mp4 │ │ 02_Discriminative_learning_for_DBNs_9_mins.srt │ │ 02_Discriminative_learning_for_DBNs_9_mins.txt │ │ │ ├─15_Lecture15 │ │ 01_From_PCA_to_autoencoders_5_mins.mp4 │ │ 01_From_PCA_to_autoencoders_5_mins.pdf │ │ 01_From_PCA_to_autoencoders_5_mins.pptx │ │ 01_From_PCA_to_autoencoders_5_mins.srt │ │ 01_From_PCA_to_autoencoders_5_mins.txt │ │ 02_Deep_auto_encoders_4_mins.mp4 │ │ 02_Deep_auto_encoders_4_mins.srt │ │ 02_Deep_auto_encoders_4_mins.txt │ │ 04_Semantic_Hashing_9_mins.mp4 │ │ 04_Semantic_Hashing_9_mins.pdf │ │ 04_Semantic_Hashing_9_mins.srt │ │ 04_Semantic_Hashing_9_mins.txt │ │ 06_Shallow_autoencoders_for_pre-training_7_mins.mp4 │ │ 06_Shallow_autoencoders_for_pre-training_7_mins.srt │ │ 06_Shallow_autoencoders_for_pre-training_7_mins.txt │ │ │ └─16_Lecture16 │ 04_OPTIONAL-_The_fog_of_progress_3_min.mp4 │ 04_OPTIONAL-_The_fog_of_progress_3_min.pdf │ 04_OPTIONAL-_The_fog_of_progress_3_min.pptx │ 04_OPTIONAL-_The_fog_of_progress_3_min.srt │ 04_OPTIONAL-_The_fog_of_progress_3_min.txt │ ├─003_Probabilistic Graphical Models │ └─003_Probabilistic Graphical Models │ └─pgm-003 │ ├─01_Introduction_and_Overview │ │ 01_Welcome.mp4 │ │ 01_Welcome.srt │ │ 01_Welcome.txt │ │ 02_Overview_and_Motivation.mp4 │ │ 02_Overview_and_Motivation.srt │ │ 02_Overview_and_Motivation.txt │ │ 03_Distributions.mp4 │ │ 03_Distributions.srt │ │ 03_Distributions.txt │ │ 04_Factors.mp4 │ │ 04_Factors.srt │ │ 04_Factors.txt │ │ │ ├─02_Bayesian_Network_Fundamentals │ │ 01_Semantics_amp_Factorization.mp4 │ │ 01_Semantics_amp_Factorization.srt │ │ 01_Semantics_amp_Factorization.txt │ │ 02_Reasoning_Patterns.mp4 │ │ 02_Reasoning_Patterns.srt │ │ 02_Reasoning_Patterns.txt │ │ 03_Flow_of_Probabilistic_Influence.mp4 │ │ 03_Flow_of_Probabilistic_Influence.srt │ │ 03_Flow_of_Probabilistic_Influence.txt │ │ 04_Conditional_Independence.mp4 │ │ 04_Conditional_Independence.srt │ │ 04_Conditional_Independence.txt │ │ 05_Independencies_in_Bayesian_Networks.mp4 │ │ 05_Independencies_in_Bayesian_Networks.srt │ │ 05_Independencies_in_Bayesian_Networks.txt │ │ 06_Naive_Bayes.mp4 │ │ 06_Naive_Bayes.srt │ │ 06_Naive_Bayes.txt │ │ 07_Application_-_Medical_Diagnosis.mp4 │ │ 07_Application_-_Medical_Diagnosis.srt │ │ 07_Application_-_Medical_Diagnosis.txt │ │ 08_Knowledge_Engineering_Example_-_SAMIAM.mp4 │ │ 08_Knowledge_Engineering_Example_-_SAMIAM.srt │ │ 08_Knowledge_Engineering_Example_-_SAMIAM.txt │ │ │ ├─03_Template_Models │ │ 01_Overview_of_Template_Models.mp4 │ │ 01_Overview_of_Template_Models.srt │ │ 01_Overview_of_Template_Models.txt │ │ 02_Temporal_Models_-_DBNs.mp4 │ │ 02_Temporal_Models_-_DBNs.srt │ │ 02_Temporal_Models_-_DBNs.txt │ │ 03_Temporal_Models_-_HMMs.mp4 │ │ 03_Temporal_Models_-_HMMs.srt │ │ 03_Temporal_Models_-_HMMs.txt │ │ 04_Plate_Models.mp4 │ │ 04_Plate_Models.srt │ │ 04_Plate_Models.txt │ │ │ ├─04_ML-class_Octave_Tutorial │ │ 01_Basic_Operations.mp4 │ │ 01_Basic_Operations.srt │ │ 01_Basic_Operations.txt │ │ 02_Moving_Data_Around.mp4 │ │ 02_Moving_Data_Around.srt │ │ 02_Moving_Data_Around.txt │ │ 03_Computing_On_Data.mp4 │ │ 03_Computing_On_Data.srt │ │ 03_Computing_On_Data.txt │ │ 04_Plotting_Data.mp4 │ │ 04_Plotting_Data.srt │ │ 04_Plotting_Data.txt │ │ 05_Control_Statements-_for_while_if_statements.mp4 │ │ 05_Control_Statements-_for_while_if_statements.srt │ │ 05_Control_Statements-_for_while_if_statements.txt │ │ 06_Vectorization.mp4 │ │ 06_Vectorization.srt │ │ 06_Vectorization.txt │ │ 07_Working_on_and_Submitting_Programming_Exercises.mp4 │ │ 07_Working_on_and_Submitting_Programming_Exercises.srt │ │ 07_Working_on_and_Submitting_Programming_Exercises.txt │ │ │ ├─05_Structured_CPDs │ │ 01_Overview-_Structured_CPDs.mp4 │ │ 01_Overview-_Structured_CPDs.srt │ │ 01_Overview-_Structured_CPDs.txt │ │ 02_Tree-Structured_CPDs.mp4 │ │ 02_Tree-Structured_CPDs.srt │ │ 02_Tree-Structured_CPDs.txt │ │ 03_Independence_of_Causal_Influence.mp4 │ │ 03_Independence_of_Causal_Influence.srt │ │ 03_Independence_of_Causal_Influence.txt │ │ 04_Continuous_Variables.mp4 │ │ 04_Continuous_Variables.srt │ │ 04_Continuous_Variables.txt │ │ │ ├─06_Markov_Network_Fundamentals │ │ 01_Pairwise_Markov_Networks.mp4 │ │ 01_Pairwise_Markov_Networks.srt │ │ 01_Pairwise_Markov_Networks.txt │ │ 02_General_Gibbs_Distribution.mp4 │ │ 02_General_Gibbs_Distribution.srt │ │ 02_General_Gibbs_Distribution.txt │ │ 03_Conditional_Random_Fields.mp4 │ │ 03_Conditional_Random_Fields.srt │ │ 03_Conditional_Random_Fields.txt │ │ 04_Independencies_in_Markov_Networks.mp4 │ │ 04_Independencies_in_Markov_Networks.srt │ │ 04_Independencies_in_Markov_Networks.txt │ │ 05_I-maps_and_perfect_maps.mp4 │ │ 05_I-maps_and_perfect_maps.srt │ │ 05_I-maps_and_perfect_maps.txt │ │ 06_Log-Linear_Models.mp4 │ │ 06_Log-Linear_Models.srt │ │ 06_Log-Linear_Models.txt │ │ 07_Shared_Features_in_Log-Linear_Models.mp4 │ │ 07_Shared_Features_in_Log-Linear_Models.srt │ │ 07_Shared_Features_in_Log-Linear_Models.txt │ │ │ ├─07_Representation_Wrapup-_Knowledge_Engineering │ │ 01_Knowledge_Engineering.mp4 │ │ 01_Knowledge_Engineering.srt │ │ 01_Knowledge_Engineering.txt │ │ │ ├─08_Inference-_Variable_Elimination │ │ 01_Overview-_Conditional_Probability_Queries.mp4 │ │ 01_Overview-_Conditional_Probability_Queries.srt │ │ 01_Overview-_Conditional_Probability_Queries.txt │ │ 02_Overview-_MAP_Inference.mp4 │ │ 02_Overview-_MAP_Inference.srt │ │ 02_Overview-_MAP_Inference.txt │ │ 03_Variable_Elimination_Algorithm.mp4 │ │ 03_Variable_Elimination_Algorithm.srt │ │ 03_Variable_Elimination_Algorithm.txt │ │ 04_Complexity_of_Variable_Elimination.mp4 │ │ 04_Complexity_of_Variable_Elimination.srt │ │ 04_Complexity_of_Variable_Elimination.txt │ │ 06_Finding_Elimination_Orderings.mp4 │ │ 06_Finding_Elimination_Orderings.srt │ │ 06_Finding_Elimination_Orderings.txt │ │ │ ├─09_Inference-_Belief_Propagation_Part_1 │ │ 01_Belief_Propagation.mp4 │ │ 01_Belief_Propagation.srt │ │ 01_Belief_Propagation.txt │ │ 02_Properties_of_Cluster_Graphs.mp4 │ │ 02_Properties_of_Cluster_Graphs.srt │ │ 02_Properties_of_Cluster_Graphs.txt │ │ │ ├─10_Inference-_Belief_Propagation_Part_2 │ │ 01_Properties_of_Belief_Propagation.mp4 │ │ 01_Properties_of_Belief_Propagation.srt │ │ 01_Properties_of_Belief_Propagation.txt │ │ 02_Clique_Tree_Algorithm_-_Correctness.mp4 │ │ 02_Clique_Tree_Algorithm_-_Correctness.srt │ │ 02_Clique_Tree_Algorithm_-_Correctness.txt │ │ 03_Clique_Tree_Algorithm_-_Computation.mp4 │ │ 03_Clique_Tree_Algorithm_-_Computation.srt │ │ 03_Clique_Tree_Algorithm_-_Computation.txt │ │ 04_Clique_Trees_and_Independence.mp4 │ │ 04_Clique_Trees_and_Independence.srt │ │ 04_Clique_Trees_and_Independence.txt │ │ 05_Clique_Trees_and_VE.mp4 │ │ 05_Clique_Trees_and_VE.srt │ │ 05_Clique_Trees_and_VE.txt │ │ 06_BP_In_Practice.mp4 │ │ 06_BP_In_Practice.srt │ │ 06_BP_In_Practice.txt │ │ 07_Loopy_BP_and_Message_Decoding.mp4 │ │ 07_Loopy_BP_and_Message_Decoding.srt │ │ 07_Loopy_BP_and_Message_Decoding.txt │ │ │ ├─11_Inference-_MAP_Estimation_Part_1 │ │ 01_Max_Sum_Message_Passing.mp4 │ │ 01_Max_Sum_Message_Passing.srt │ │ 01_Max_Sum_Message_Passing.txt │ │ 02_Finding_a_MAP_Assignment.mp4 │ │ 02_Finding_a_MAP_Assignment.srt │ │ 02_Finding_a_MAP_Assignment.txt │ │ │ ├─12_Inference-_MAP_Estimation_Part_2 │ │ 01_Tractable_MAP_Problems.mp4 │ │ 01_Tractable_MAP_Problems.srt │ │ 01_Tractable_MAP_Problems.txt │ │ 02_Dual_Decomposition_-_Intuition.mp4 │ │ 02_Dual_Decomposition_-_Intuition.srt │ │ 02_Dual_Decomposition_-_Intuition.txt │ │ 03_Dual_Decomposition_-_Algorithm.mp4 │ │ 03_Dual_Decomposition_-_Algorithm.srt │ │ 03_Dual_Decomposition_-_Algorithm.txt │ │ │ ├─13_Inference-_Sampling_Methods │ │ 01_Simple_Sampling.mp4 │ │ 01_Simple_Sampling.srt │ │ 01_Simple_Sampling.txt │ │ 02_Markov_Chain_Monte_Carlo.mp4 │ │ 02_Markov_Chain_Monte_Carlo.srt │ │ 02_Markov_Chain_Monte_Carlo.txt │ │ 03_Using_a_Markov_Chain.mp4 │ │ 03_Using_a_Markov_Chain.srt │ │ 03_Using_a_Markov_Chain.txt │ │ 04_Gibbs_Sampling.mp4 │ │ 04_Gibbs_Sampling.srt │ │ 04_Gibbs_Sampling.txt │ │ 05_Metropolis_Hastings_Algorithm.mp4 │ │ 05_Metropolis_Hastings_Algorithm.srt │ │ 05_Metropolis_Hastings_Algorithm.txt │ │ │ ├─14_Inference-_Temporal_Models_and_Wrap-up │ │ 01_Inference_in_Temporal_Models.mp4 │ │ 01_Inference_in_Temporal_Models.srt │ │ 01_Inference_in_Temporal_Models.txt │ │ 02_Inference-_Summary.mp4 │ │ 02_Inference-_Summary.srt │ │ 02_Inference-_Summary.txt │ │ │ ├─15_Decision_Theory │ │ 01_Maximum_Expected_Utility.mp4 │ │ 01_Maximum_Expected_Utility.srt │ │ 01_Maximum_Expected_Utility.txt │ │ 02_Utility_Functions.mp4 │ │ 02_Utility_Functions.srt │ │ 02_Utility_Functions.txt │ │ 03_Value_of_Perfect_Information.mp4 │ │ 03_Value_of_Perfect_Information.srt │ │ 03_Value_of_Perfect_Information.txt │ │ │ ├─16_ML-class_Revision │ │ 01_Regularization-_The_Problem_of_Overfitting.mp4 │ │ 01_Regularization-_The_Problem_of_Overfitting.srt │ │ 01_Regularization-_The_Problem_of_Overfitting.txt │ │ 02_Regularization-_Cost_Function.mp4 │ │ 02_Regularization-_Cost_Function.srt │ │ 02_Regularization-_Cost_Function.txt │ │ 03_Evaluating_a_Hypothesis.mp4 │ │ 03_Evaluating_a_Hypothesis.srt │ │ 03_Evaluating_a_Hypothesis.txt │ │ 04_Model_Selection_and_Train_Validation_Test_Sets.mp4 │ │ 04_Model_Selection_and_Train_Validation_Test_Sets.srt │ │ 04_Model_Selection_and_Train_Validation_Test_Sets.txt │ │ 05_Diagnosing_Bias_vs_Variance.mp4 │ │ 05_Diagnosing_Bias_vs_Variance.srt │ │ 05_Diagnosing_Bias_vs_Variance.txt │ │ 06_Regularization_and_Bias_Variance.mp4 │ │ 06_Regularization_and_Bias_Variance.srt │ │ 06_Regularization_and_Bias_Variance.txt │ │ │ ├─17_Learning-_Overview │ │ 01_Learning-_Overview.mp4 │ │ 01_Learning-_Overview.srt │ │ 01_Learning-_Overview.txt │ │ │ ├─18_Learning-_Parameter_Estimation_in_BNs │ │ 01_Maximum_Likelihood_Estimation.mp4 │ │ 01_Maximum_Likelihood_Estimation.srt │ │ 01_Maximum_Likelihood_Estimation.txt │ │ 03_Bayesian_Estimation.mp4 │ │ 03_Bayesian_Estimation.srt │ │ 03_Bayesian_Estimation.txt │ │ 04_Bayesian_Prediction.mp4 │ │ 04_Bayesian_Prediction.srt │ │ 04_Bayesian_Prediction.txt │ │ │ ├─19_Learning-_Parameter_Estimation_in_MNs │ │ 01_Maximum_Likelihood_for_Log-Linear_Models.mp4 │ │ 01_Maximum_Likelihood_for_Log-Linear_Models.srt │ │ 01_Maximum_Likelihood_for_Log-Linear_Models.txt │ │ 03_MAP_Estimation_for_MRFs_and_CRFs.mp4 │ │ 03_MAP_Estimation_for_MRFs_and_CRFs.srt │ │ 03_MAP_Estimation_for_MRFs_and_CRFs.txt │ │ │ ├─20_Structure_Learning │ │ 01_Structure_Learning_Overview.mp4 │ │ 01_Structure_Learning_Overview.srt │ │ 01_Structure_Learning_Overview.txt │ │ 02_Likelihood_Scores.mp4 │ │ 02_Likelihood_Scores.srt │ │ 02_Likelihood_Scores.txt │ │ 03_BIC_and_Asymptotic_Consistency.mp4 │ │ 03_BIC_and_Asymptotic_Consistency.srt │ │ 03_BIC_and_Asymptotic_Consistency.txt │ │ 04_Bayesian_Scores.mp4 │ │ 04_Bayesian_Scores.srt │ │ 04_Bayesian_Scores.txt │ │ 05_Learning_Tree_Structured_Networks.mp4 │ │ 05_Learning_Tree_Structured_Networks.srt │ │ 05_Learning_Tree_Structured_Networks.txt │ │ 06_Learning_General_Graphs-_Heuristic_Search.mp4 │ │ 06_Learning_General_Graphs-_Heuristic_Search.srt │ │ 06_Learning_General_Graphs-_Heuristic_Search.txt │ │ 07_Learning_General_Graphs-_Search_and_Decomposability.mp4 │ │ 07_Learning_General_Graphs-_Search_and_Decomposability.srt │ │ 07_Learning_General_Graphs-_Search_and_Decomposability.txt │ │ │ ├─21_Learning_With_Incomplete_Data │ │ 01_Learning_With_Incomplete_Data_-_Overview.mp4 │ │ 01_Learning_With_Incomplete_Data_-_Overview.srt │ │ 01_Learning_With_Incomplete_Data_-_Overview.txt │ │ 02_Expectation_Maximization_-_Intro.mp4 │ │ 02_Expectation_Maximization_-_Intro.srt │ │ 02_Expectation_Maximization_-_Intro.txt │ │ 03_Analysis_of_EM_Algorithm.mp4 │ │ 03_Analysis_of_EM_Algorithm.srt │ │ 03_Analysis_of_EM_Algorithm.txt │ │ 04_EM_in_Practice.mp4 │ │ 04_EM_in_Practice.srt │ │ 04_EM_in_Practice.txt │ │ 05_Latent_Variables.mp4 │ │ 05_Latent_Variables.srt │ │ 05_Latent_Variables.txt │ │ │ ├─22_Learning-_Wrapup │ │ 01_Summary-_Learning.mp4 │ │ 01_Summary-_Learning.srt │ │ 01_Summary-_Learning.txt │ │ │ └─23_Summary │ 01_Class_Summary.mp4 │ 01_Class_Summary.srt │ 01_Class_Summary.txt │ ├─004_Natural Language Processing Collins │ └─004_Natural Language Processing Collins │ └─nlangp-001 │ ├─01_Week_1_-_Introduction_to_Natural_Language_Processing │ ├─02_Week_1_-_The_Language_Modeling_Problem │ │ 03_Markov_Processes_(Part_1).mp4 │ │ 03_Markov_Processes_(Part_1).srt │ │ 03_Markov_Processes_(Part_1).txt │ │ 04_Markov_Processes_(Part_2).mp4 │ │ 04_Markov_Processes_(Part_2).srt │ │ 04_Markov_Processes_(Part_2).txt │ │ 05_Trigram_Language_Models.mp4 │ │ 05_Trigram_Language_Models.srt │ │ 05_Trigram_Language_Models.txt │ │ │ ├─03_Week_1_-_Parameter_Estimation_in_Language_Models │ ├─04_Week_1_-_Summary │ │ 01_Summary.mp4 │ │ 01_Summary.srt │ │ 01_Summary.txt │ │ │ ├─05_Week_2_-_Tagging_Problems_and_Hidden_Markov_Models │ │ 08_Summary.mp4 │ │ 08_Summary.srt │ │ 08_Summary.txt │ │ │ ├─06_Week_3_-_Parsing_and_Context-Free_Grammars │ │ 01_Introduction.mp4 │ │ 01_Introduction.srt │ │ 01_Introduction.txt │ │ 10_Examples_of_Ambiguity.mp4 │ │ 10_Examples_of_Ambiguity.srt │ │ 10_Examples_of_Ambiguity.txt │ │ │ ├─07_Week_3_-_Probabilistic_Context-Free_Grammars │ │ 01_Introduction.mp4 │ │ 01_Introduction.pdf │ │ 01_Introduction.srt │ │ 01_Introduction.txt │ │ │ ├─08_Week_4_-_Weaknesses_of_PCFGs │ │ 01_Weaknesses_of_PCFGs.mp4 │ │ 01_Weaknesses_of_PCFGs.pdf │ │ 01_Weaknesses_of_PCFGs.srt │ │ 01_Weaknesses_of_PCFGs.txt │ │ │ ├─09_Week_4_-_Lexicalized_PCFGs │ │ 01_Introduction.mp4 │ │ 01_Introduction.pdf │ │ 01_Introduction.srt │ │ 01_Introduction.txt │ │ 02_Lexicalization_of_a_Treebank.mp4 │ │ 02_Lexicalization_of_a_Treebank.srt │ │ 02_Lexicalization_of_a_Treebank.txt │ │ 03_Lexicalized_PCFGs-_Basic_Definitions.mp4 │ │ 03_Lexicalized_PCFGs-_Basic_Definitions.srt │ │ 03_Lexicalized_PCFGs-_Basic_Definitions.txt │ │ │ ├─10_Week_5_-_Introduction_to_Machine_Translation │ │ 01_Opening_Comments.mp4 │ │ 01_Opening_Comments.pdf │ │ 01_Opening_Comments.srt │ │ 01_Opening_Comments.txt │ │ 02_introduction.mp4 │ │ 02_introduction.srt │ │ 02_introduction.txt │ │ 03_Challenges_in_MT.mp4 │ │ 03_Challenges_in_MT.srt │ │ 03_Challenges_in_MT.txt │ │ │ ├─11_Week_5_-_The_IBM_Translation_Models │ │ 01_Introduction.mp4 │ │ 01_Introduction.pdf │ │ 01_Introduction.srt │ │ 01_Introduction.txt │ │ 02_IBM_Model_1_(Part_1).mp4 │ │ 02_IBM_Model_1_(Part_1).srt │ │ 02_IBM_Model_1_(Part_1).txt │ │ 03_IBM_Model_1_(Part_2).mp4 │ │ 03_IBM_Model_1_(Part_2).srt │ │ 03_IBM_Model_1_(Part_2).txt │ │ 04_IBM_Model_2.mp4 │ │ 04_IBM_Model_2.srt │ │ 04_IBM_Model_2.txt │ │ 09_Summary.mp4 │ │ 09_Summary.srt │ │ 09_Summary.txt │ │ │ ├─12_Week_6_-_Phrase-based_Translation_Models │ │ 01_Introduction.mp4 │ │ 01_Introduction.pdf │ │ 01_Introduction.srt │ │ 01_Introduction.txt │ │ │ ├─13_Week_6_-_Decoding_of_Phrase-based_Translation_Models │ ├─14_Week_7_-_Log-linear_Models │ │ 01_Introduction.mp4 │ │ 01_Introduction.pdf │ │ 01_Introduction.srt │ │ 01_Introduction.txt │ │ 02_Two_Example_Problems.mp4 │ │ 02_Two_Example_Problems.srt │ │ 02_Two_Example_Problems.txt │ │ 03_Features_in_Log-Linear_Models_(Part_1).mp4 │ │ 03_Features_in_Log-Linear_Models_(Part_1).srt │ │ 03_Features_in_Log-Linear_Models_(Part_1).txt │ │ 04_Features_in_Log-Linear_Models_(Part_2).mp4 │ │ 04_Features_in_Log-Linear_Models_(Part_2).srt │ │ 04_Features_in_Log-Linear_Models_(Part_2).txt │ │ │ ├─15_Week_8_-_Log-linear_Models_for_Tagging │ │ 01_Introduction.mp4 │ │ 01_Introduction.pdf │ │ 01_Introduction.srt │ │ 01_Introduction.txt │ │ 07_An_Example_Application.mp4 │ │ 07_An_Example_Application.srt │ │ 07_An_Example_Application.txt │ │ 08_Summary.mp4 │ │ 08_Summary.srt │ │ 08_Summary.txt │ │ │ ├─16_Week_8_-_Log-Linear_Models_for_History-based_Parsing │ │ 01_Introduction.mp4 │ │ 01_Introduction.pdf │ │ 01_Introduction.srt │ │ 01_Introduction.txt │ │ 06_Summary.mp4 │ │ 06_Summary.srt │ │ 06_Summary.txt │ │ │ ├─17_Week_9_-_Unsupervised_Learning-_Brown_Clustering │ │ 01_Introduction.mp4 │ │ 01_Introduction.srt │ │ 01_Introduction.txt │ │ │ ├─18_Week_9_-_Global_Linear_Models │ │ 01_Introduction.mp4 │ │ 01_Introduction.pdf │ │ 01_Introduction.srt │ │ 01_Introduction.txt 资源批量下载地址: ---> http://bbs.vlan5.com/forum-94-1.html 更多精品资源,打包下载(可按知识点/发布日期/培训班/讲师等方式批量下载视频/文档/资料/电子书) 本贴附件下载链接:
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