Index of /hlarochelle/ift725
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Parent Directory
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1_01_artificial_neuron.pdf
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1_02_activation_function.pdf
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1_03_capacity_of_single_neuron.pdf
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1_04_multilayer_neural_network.pdf
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1_05_capacity_of_neural_network.pdf
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1_06_biological_inspiration.pdf
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2_01_empirical_risk_minimization.pdf
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2_02_loss_function.pdf
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2_03_output_layer_gradient.pdf
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2_04_hidden_layer_gradient.pdf
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2_05_activation_function_derivative.pdf
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2_06_parameter_gradient.pdf
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2_07_backpropagation.pdf
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2_08_regularization.pdf
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2_09_parameter_initialization.pdf
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2_10_model_selection.pdf
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2_11_optimization.pdf
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3_01_motivation.pdf
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3_02_linear_chain_crf.pdf
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3_03_context_window.pdf
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3_04_computing_partition_function.pdf
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3_05_computing_marginals.pdf
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3_06_performing_classification.pdf
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3_07_factors_sufficient_statistics_linear_crf.pdf
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3_08_markov_network.pdf
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3_09_factor_graph.pdf
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3_10_belief_propagation.pdf
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4_01_loss_function.pdf
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4_02_unary_log-factor_gradient.pdf
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4_03_pairwise_log-factor_gradient.pdf
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4_04_discriminative_vs_generative.pdf
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4_05_maximum-entropy_markov_model.pdf
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4_06_hidden_markov_model.pdf
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4_07_general_crf.pdf
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4_08_pseudolikelihood.pdf
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5_01_definition.pdf
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5_02_inference.pdf
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5_03_free_energy.pdf
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5_04_contrastive_divergence.pdf
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5_05_contrastive_divergence_parameter_update.pdf
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5_06_persistent_CD.pdf
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5_07_example.pdf
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5_08_extensions.pdf
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6_01_definition.pdf
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6_02_loss_function.pdf
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6_03_example.pdf
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6_04_linear_autoencoder.pdf
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6_05_undercomplete_vs_overcomplete_hidden_layer.pdf
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6_06_denoising_autoencoder.pdf
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6_07_contractive_autoencoder.pdf
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7_01_motivation.pdf
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7_02_difficulty_of_training.pdf
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7_03_unsupervised_pretraining.pdf
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7_04_example.pdf
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7_05_dropout.pdf
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7_06_deep_autoencoder.pdf
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7_07_deep_belief_network.pdf
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7_08_variational_bound.pdf
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7_09_dbn_pretraining.pdf
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8_01_definition.pdf
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8_02_inference_ISTA_algorithm.pdf
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8_03_dictionary_update_projected_gradient_descent.pdf
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8_04_dictionary_update_block-coordinate_descent.pdf
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8_05_dictionary_learning_algorithm.pdf
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8_06_online_dictionary_learning_algorithm.pdf
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8_07_ZCA_preprocessing.pdf
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8_08_feature_extraction.pdf
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8_09_relationship_with_V1.pdf
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9_01.motivation.pdf
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9_02_local_connectivity.pdf
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9_03_parameter_sharing.pdf
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9_04_discrete_convolution.pdf
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9_05_pooling_and_subsampling.pdf
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9_06_convolutional_network.pdf
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9_07_object_recognition.pdf
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9_08_example.pdf
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9_09_data_set_expansion.pdf
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9_10_convolutional_rbm.pdf
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10_01_motivation.pdf
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10_02_preprocessing.pdf
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10_03_one-hot_encoding.pdf
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10_04_word_representations.pdf
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10_05_language_modeling.pdf
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10_06_neural_network_language_model.pdf
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10_07_hierarchical_output_layer.pdf
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10_08_word_tagging.pdf
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10_09_convolutional_network.pdf
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10_10_multitask_learning.pdf
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10_11_recursive_network.pdf
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10_12_merging_representations.pdf
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10_13_tree_inference.pdf
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10_14_recursive_network_training.pdf
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autoencoder.pdf
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bprop.pdf
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convolutional_network.pdf
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crf.pdf
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crf_learn.pdf
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deep.pdf
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fprop.pdf
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nlp-language-model.pdf
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nlp-recursive-net.pdf
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nlp-tagging.pdf
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nlp-word-representations.pdf
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rbm.pdf
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review.pdf
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sparse_coding.pdf
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