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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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