神经网络-公众号：编程沉思录
|____Neural networks [9.9] - Computer vision - data set expansion-Km1Q5VcSKAg.mp4
|____Neural networks [9.9] - Computer vision - data set expansion-Km1Q5VcSKAg.en.srt
|____Neural networks [9.8] - Computer vision - example-Gk8VvSL3IMk.mp4
|____Neural networks [9.8] - Computer vision - example-Gk8VvSL3IMk.en.srt
|____Neural networks [9.7] - Computer vision - object recognition-eU83LSM3xnk.mp4
|____Neural networks [9.7] - Computer vision - object recognition-eU83LSM3xnk.en.srt
|____Neural networks [9.6] - Computer vision - convolutional network-cDdpwAIsuD8.mp4
|____Neural networks [9.5] - Computer vision - pooling and subsampling-I-JKxcpbRT4.mp4
|____Neural networks [9.5] - Computer vision - pooling and subsampling-I-JKxcpbRT4.en.srt
|____Neural networks [9.4] - Computer vision - discrete convolution-Y7TMwqAWEdo.mp4
|____Neural networks [9.4] - Computer vision - discrete convolution-Y7TMwqAWEdo.en.srt
|____Neural networks [9.3] - Computer vision - parameter sharing-aAT1t9p7ShM.mp4
|____Neural networks [9.3] - Computer vision - parameter sharing-aAT1t9p7ShM.en.srt
|____Neural networks [9.2] - Computer vision - local connectivity-vLf3KVe2Z1k.mp4
|____Neural networks [9.2] - Computer vision - local connectivity-vLf3KVe2Z1k.en.srt
|____Neural networks [9.1] - Computer vision - motivation-rxKrCa4bg1I.mp4
|____Neural networks [9.1] - Computer vision - motivation-rxKrCa4bg1I.en.srt
|____Neural networks [9.10] - Computer vision - convolutional RBM-y0SISi_T6s8.mp4
|____Neural networks [9.10] - Computer vision - convolutional RBM-y0SISi_T6s8.en.srt
|____Neural networks [8.9] - relationship with V1-MdomgSiL86Q.mp4
|____Neural networks [8.9] - relationship with V1-MdomgSiL86Q.en.srt
|____Neural networks [8.8] - Sparse coding - feature extraction-FL81zSjAEEg.mp4
|____Neural networks [8.8] - Sparse coding - feature extraction-FL81zSjAEEg.en.srt
|____Neural networks [8.7] - Sparse coding - ZCA preprocessing-eUiwhV1QcQ4.mp4
|____Neural networks [8.7] - Sparse coding - ZCA preprocessing-eUiwhV1QcQ4.en.srt
|____Neural networks [8.6] - Sparse coding - online dictionary learning algorithm-IePxTepLvQc.mp4
|____Neural networks [8.6] - Sparse coding - online dictionary learning algorithm-IePxTepLvQc.en.srt
|____Neural networks [8.5] - Sparse coding - dictionary learning algorithm-PzNMff7cYjM.mp4
|____Neural networks [8.5] - Sparse coding - dictionary learning algorithm-PzNMff7cYjM.en.srt
|____Neural networks [8.4] - Sparse coding - dictionary update with block-coordinate descent-UMdNfhgPKTc.mp4
|____Neural networks [8.4] - Sparse coding - dictionary update with block-coordinate descent-UMdNfhgPKTc.en.srt
|____Neural networks [8.3] - Sparse coding - dictionary update with projected gradient descent-bhqNSjJ_A20.mp4
|____Neural networks [8.3] - Sparse coding - dictionary update with projected gradient descent-bhqNSjJ_A20.en.srt
|____Neural networks [8.2] - Sparse coding - inference (ISTA algorithm)-L6qhzWWtqQs.mp4
|____Neural networks [8.2] - Sparse coding - inference (ISTA algorithm)-L6qhzWWtqQs.en.srt
|____Neural networks [8.1] - Sparse coding - definition-7a0_iEruGoM.mp4
|____Neural networks [8.1] - Sparse coding - definition-7a0_iEruGoM.en.srt
|____Neural networks [7.9] - Deep learning - DBN pre-training-35MUlYCColk.mp4
|____Neural networks [7.9] - Deep learning - DBN pre-training-35MUlYCColk.en.srt
|____Neural networks [7.8] - Deep learning - variational bound-pStDscJh2Wo.mp4
|____Neural networks [7.8] - Deep learning - variational bound-pStDscJh2Wo.en.srt
|____Neural networks [7.7] - Deep learning - deep belief network-vkb6AWYXZ5I.mp4
|____Neural networks [7.7] - Deep learning - deep belief network-vkb6AWYXZ5I.en.srt
|____Neural networks [7.6] - Deep learning - deep autoencoder-z5ZYm_wJ37c.mp4
|____Neural networks [7.6] - Deep learning - deep autoencoder-z5ZYm_wJ37c.en.srt
|____Neural networks [7.5] - Deep learning - dropout-UcKPdAM8cnI.mp4
|____Neural networks [7.5] - Deep learning - dropout-UcKPdAM8cnI.en.srt
|____Neural networks [7.4] - Deep learning - example-SXnG-lQ7RJo.mp4
|____Neural networks [7.4] - Deep learning - example-SXnG-lQ7RJo.en.srt
|____Neural networks [7.3] - Deep learning - unsupervised pre-training-Oq38pINmddk.mp4
|____Neural networks [7.3] - Deep learning - unsupervised pre-training-Oq38pINmddk.en.srt
|____Neural networks [7.2] - Deep learning - difficulty of training-YoiUlN_77LU.mp4
|____Neural networks [7.2] - Deep learning - difficulty of training-YoiUlN_77LU.en.srt
|____Neural networks [7.1] - Deep learning - motivation-vXMpKYRhpmI.mp4
|____Neural networks [7.1] - Deep learning - motivation-vXMpKYRhpmI.en.srt
|____Neural networks [6.7] - Autoencoder - contractive autoencoder-79sYlJ8Cvlc.mp4
|____Neural networks [6.7] - Autoencoder - contractive autoencoder-79sYlJ8Cvlc.en.srt
|____Neural networks [6.6] - Autoencoder - denoising autoencoder-t2NQ_c5BFOc.mp4
|____Neural networks [6.6] - Autoencoder - denoising autoencoder-t2NQ_c5BFOc.en.srt
|____Neural networks [6.5] - Autoencoder - undercomplete vs. overcomplete hidden layer-5rLgoM2Pkso.mp4
|____Neural networks [6.5] - Autoencoder - undercomplete vs. overcomplete hidden layer-5rLgoM2Pkso.en.srt
|____Neural networks [6.4] - Autoencoder - linear autoencoder-xq-I0Rl8mt0.mp4
|____Neural networks [6.4] - Autoencoder - linear autoencoder-xq-I0Rl8mt0.en.srt
|____Neural networks [6.3] - Autoencoder - example-6DO_jVbDP3I.mp4
|____Neural networks [6.3] - Autoencoder - example-6DO_jVbDP3I.en.srt
|____Neural networks [6.2] - Autoencoder - loss function-xTU79Zs4XKY.mp4
|____Neural networks [6.2] - Autoencoder - loss function-xTU79Zs4XKY.en.srt
|____Neural networks [6.1] - Autoencoder - definition-FzS3tMl4Nsc.mp4
|____Neural networks [6.1] - Autoencoder - definition-FzS3tMl4Nsc.en.srt
|____Neural networks [5.8] - Restricted Boltzmann machine - extensions-iPuqoQih9xk.mp4
|____Neural networks [5.8] - Restricted Boltzmann machine - extensions-iPuqoQih9xk.en.srt
|____Neural networks [5.7] - Restricted Boltzmann machine - example-n26NdEtma8U.mp4
|____Neural networks [5.7] - Restricted Boltzmann machine - example-n26NdEtma8U.en.srt
|____Neural networks [5.6] - Restricted Boltzmann machine - persistent CD-S0kFFiHzR8M.mp4
|____Neural networks [5.6] - Restricted Boltzmann machine - persistent CD-S0kFFiHzR8M.en.srt
|____Neural networks [5.5] - Restricted Boltzmann machine - contrastive divergence (parameter update)-wMb7cads0go.mp4
|____Neural networks [5.5] - Restricted Boltzmann machine - contrastive divergence (parameter update)-wMb7cads0go.en.srt
|____Neural networks [5.4] - Restricted Boltzmann machine - contrastive divergence-MD8qXWucJBY.mp4
|____Neural networks [5.4] - Restricted Boltzmann machine - contrastive divergence-MD8qXWucJBY.en.srt
|____Neural networks [5.3] - Restricted Boltzmann machine - free energy-e0Ts_7Y6hZU.mp4
|____Neural networks [5.3] - Restricted Boltzmann machine - free energy-e0Ts_7Y6hZU.en.srt
|____Neural networks [5.2] - Restricted Boltzmann machine - inference-lekCh_i32iE.mp4
|____Neural networks [5.2] - Restricted Boltzmann machine - inference-lekCh_i32iE.en.srt
|____Neural networks [5.1] - Restricted Boltzmann machine - definition-p4Vh_zMw-HQ.mp4
|____Neural networks [5.1] - Restricted Boltzmann machine - definition-p4Vh_zMw-HQ.en.srt
|____Neural networks [4.8] - Training CRFs - pseudolikelihood-ltRT1m7vaBU.mp4
|____Neural networks [4.8] - Training CRFs - pseudolikelihood-ltRT1m7vaBU.en.srt
|____Neural networks [4.7] - Training CRFs - general conditional random field-QY9k7tJistU.mp4
|____Neural networks [4.7] - Training CRFs - general conditional random field-QY9k7tJistU.en.srt
|____Neural networks [4.6] - Training CRFs - hidden Markov model-jdlJfM707MM.mp4
|____Neural networks [4.6] - Training CRFs - hidden Markov model-jdlJfM707MM.en.srt
|____Neural networks [4.5] - Training CRFs - maximum-entropy Markov model-aMi2xnYEwbc.mp4
|____Neural networks [4.5] - Training CRFs - maximum-entropy Markov model-aMi2xnYEwbc.en.srt
|____Neural networks [4.4] - Training CRFs - discriminative vs. generative learning-MD4mY3Zj5E4.mp4
|____Neural networks [4.4] - Training CRFs - discriminative vs. generative learning-MD4mY3Zj5E4.en.srt
|____Neural networks [4.3] - Training CRFs - pairwise log-factor gradient-1W2lkcGV2Zo.mp4
|____Neural networks [4.3] - Training CRFs - pairwise log-factor gradient-1W2lkcGV2Zo.en.srt
|____Neural networks [4.2] - Training CRFs - unary log-factor gradient-fU2W7KRoS2U.mp4
|____Neural networks [4.2] - Training CRFs - unary log-factor gradient-fU2W7KRoS2U.en.srt
|____Slides
          |____review.pdf
          |____9_10_convolutional_rbm.pdf
          |____9_09_data_set_expansion.pdf
          |____9_08_example.pdf
          |____9_07_object_recognition.pdf
          |____9_06_convolutional_network.pdf
          |____9_05_pooling_and_subsampling.pdf
          |____9_04_discrete_convolution.pdf
          |____9_03_parameter_sharing.pdf
          |____9_02_local_connectivity.pdf
          |____9_01.motivation.pdf
          |____8_09_relationship_with_V1.pdf
          |____8_08_feature_extraction.pdf
          |____8_07_ZCA_preprocessing.pdf
          |____8_06_online_dictionary_learning_algorithm.pdf
          |____8_05_dictionary_learning_algorithm.pdf
          |____8_04_dictionary_update_block-coordinate_descent.pdf
          |____8_03_dictionary_update_projected_gradient_descent.pdf
          |____8_02_inference_ISTA_algorithm.pdf
          |____8_01_definition.pdf
          |____7_09_dbn_pretraining.pdf
          |____7_08_variational_bound.pdf
          |____7_07_deep_belief_network.pdf
          |____7_06_deep_autoencoder.pdf
          |____7_05_dropout.pdf
          |____7_04_example.pdf
          |____7_03_unsupervised_pretraining.pdf
          |____7_02_difficulty_of_training.pdf
          |____7_01_motivation.pdf
          |____6_07_contractive_autoencoder.pdf
          |____6_06_denoising_autoencoder.pdf
          |____6_05_undercomplete_vs_overcomplete_hidden_layer.pdf
          |____6_04_linear_autoencoder.pdf
          |____6_03_example.pdf
          |____6_02_loss_function.pdf
          |____6_01_definition.pdf
          |____5_08_extensions.pdf
          |____5_07_example.pdf
          |____5_06_persistent_CD.pdf
          |____5_05_contrastive_divergence_parameter_update.pdf
          |____5_04_contrastive_divergence.pdf
          |____5_03_free_energy.pdf
          |____5_02_inference.pdf
          |____5_01_definition.pdf
          |____4_08_pseudolikelihood.pdf
          |____4_07_general_crf.pdf
          |____4_06_hidden_markov_model.pdf
          |____4_05_maximum-entropy_markov_model.pdf
          |____4_04_discriminative_vs_generative.pdf
          |____4_03_pairwise_log-factor_gradient.pdf
          |____4_02_unary_log-factor_gradient.pdf
          |____4_01_loss_function.pdf
          |____3_10_belief_propagation.pdf
          |____3_09_factor_graph.pdf
          |____3_07_factors_sufficient_statistics_linear_crf.pdf
          |____3_06_performing_classification.pdf
          |____3_05_computing_marginals.pdf
          |____3_04_computing_partition_function.pdf
          |____3_03_context_window.pdf
          |____3_02_linear_chain_crf.pdf
          |____3_01_motivation.pdf
          |____2_11_optimization.pdf
          |____2_10_model_selection.pdf
          |____2_09_parameter_initialization.pdf
          |____2_08_regularization.pdf
          |____2_07_backpropagation.pdf
          |____2_06_parameter_gradient.pdf
          |____2_05_activation_function_derivative.pdf
          |____2_04_hidden_layer_gradient.pdf
          |____2_03_output_layer_gradient.pdf
          |____2_02_loss_function.pdf
          |____2_01_empirical_risk_minimization.pdf
          |____1_06_biological_inspiration.pdf
          |____1_05_capacity_of_neural_network.pdf
          |____1_04_multilayer_neural_network.pdf
          |____1_03_capacity_of_single_neuron.pdf
          |____1_02_activation_function.pdf
          |____1_01_artificial_neuron.pdf
          |____10_14_recursive_network_training.pdf
          |____10_13_tree_inference.pdf
          |____10_12_merging_representations.pdf
          |____10_11_recursive_network.pdf
          |____10_10_multitask_learning.pdf
          |____10_09_convolutional_network.pdf
          |____10_08_word_tagging.pdf
          |____10_07_hierarchical_output_layer.pdf
          |____10_06_neural_network_language_model.pdf
          |____10_05_language_modeling.pdf
          |____10_04_word_representations.pdf
          |____10_03_one-hot_encoding.pdf
          |____10_02_preprocessing.pdf
          |____10_01_motivation.pdf

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