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种子名称： Advanced Machine Learning Specialization
文件类型： 视频
文件数目： 159个文件
文件大小： 2.85 GB
收录时间： 2019-3-31 10:39
已经下载： 3次
资源热度： 91
最近下载： 2024-5-6 02:37

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Advanced Machine Learning Specialization.torrent

3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/02_conjugate-priors/02_conjugate-distributions.mp45.33MB

3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/01_introduction-to-bayesian-methods/03_how-to-define-a-model.mp45.85MB

3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/02_conjugate-priors/01_analytical-inference.mp47.62MB

2. competitive-data-science/11_ensembling/01_ensembling/01_introduction-into-ensemble-methods.mp48MB

3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/02_conjugate-priors/04_example-bernoulli.mp48.03MB

3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/07_extensions-of-lda.mp49.17MB

3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/01_variational-inference/01_why-approximate-inference.mp49.2MB

2. competitive-data-science/03_final-project-description/01_final-project/02_final-project-overview.mp49.31MB

3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/07_application-of-bayesian-optimization.mp49.55MB

3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/02_conjugate-priors/03_example-normal-precision.mp49.57MB

3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/03_gp-for-machine-learning.mp49.63MB

3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/01_topic-modeling.mp49.7MB

3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/01_introduction-to-bayesian-methods/02_bayesian-approach-to-statistics.mp49.77MB

3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/01_variational-inference/04_variational-em-review.mp410.14MB

1. intro-to-deep-learning/01_introduction-to-optimization/02_linear-model-as-the-simplest-neural-network/03_gradient-descent.mp410.25MB

3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/01_nonparametric-methods.mp410.54MB

3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/03_latent-dirichlet-allocation.mp410.56MB

1. intro-to-deep-learning/03_deep-learning-for-images/03_applications-of-cnns/01_learning-new-tasks-with-pre-trained-cnns.mp410.67MB

1. intro-to-deep-learning/01_introduction-to-optimization/03_regularization-in-machine-learning/02_model-regularization.mp410.75MB

2. competitive-data-science/01_introduction-recap/02_competition-mechanics/03_real-world-application-vs-competitions.mp411.02MB

2. competitive-data-science/10_advanced-feature-engineering-ii/01_advanced-features-ii/03_feature-interactions.mp411.11MB

3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/05_gradient-of-decoder.mp411.34MB

1. intro-to-deep-learning/01_introduction-to-optimization/04_stochastic-methods-for-optimization/01_stochastic-gradient-descent.mp411.4MB

3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm/04_m-step-details.mp411.43MB

3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/01_scaling-variational-inference-unbiased-estimates.mp411.49MB

2. competitive-data-science/10_advanced-feature-engineering-ii/01_advanced-features-ii/01_statistics-and-distance-based-features.mp411.6MB

2. competitive-data-science/10_advanced-feature-engineering-ii/01_advanced-features-ii/04_t-sne.mp411.62MB

2. competitive-data-science/01_introduction-recap/04_software-hardware-requirements/01_software-hardware-requirements.mp411.75MB

3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/02_dirichlet-distribution.mp411.88MB

1. intro-to-deep-learning/04_unsupervised-representation-learning/01_intro-to-unsupervised-learning/02_autoencoders-101.mp411.9MB

2. competitive-data-science/11_ensembling/01_ensembling/02_bagging.mp411.94MB

3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm/07_summary-of-expectation-maximization.mp412.1MB

2. competitive-data-science/04_exploratory-data-analysis/02_eda-examples/03_numerai-competition-eda.mp412.21MB

3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/06_log-derivative-trick.mp412.24MB

2. competitive-data-science/06_data-leakages/01_data-leakages/01_basic-data-leaks.mp412.28MB

3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/01_latent-variable-models/02_probabilistic-clustering.mp412.4MB

2. competitive-data-science/04_exploratory-data-analysis/01_exploratory-data-analysis/03_building-intuition-about-the-data.mp412.7MB

1. intro-to-deep-learning/04_unsupervised-representation-learning/01_intro-to-unsupervised-learning/01_unsupervised-learning-what-it-is-and-why-bother.mp412.74MB

3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/03_applications-and-examples/05_em-for-probabilistic-pca.mp412.98MB

2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/06_general-approaches-for-metrics-optimization.mp413.12MB

2. competitive-data-science/10_advanced-feature-engineering-ii/01_advanced-features-ii/02_matrix-factorizations.mp413.16MB

2. competitive-data-science/04_exploratory-data-analysis/02_eda-examples/01_springleaf-competition-eda-i.mp413.18MB

2. competitive-data-science/12_competitions-go-through/01_competitions-go-through/03_springleaf-marketing-response.mp413.29MB

1. intro-to-deep-learning/02_introduction-to-neural-networks/01_multilayer-perceptron-or-the-basic-principles-of-deep-learning/01_multilayer-perceptron.mp413.4MB

2. competitive-data-science/04_exploratory-data-analysis/01_exploratory-data-analysis/02_exploratory-data-analysis.mp413.51MB

3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/01_introduction-to-bayesian-methods/01_think-bayesian-statistics-review.mp413.55MB

1. intro-to-deep-learning/01_introduction-to-optimization/04_stochastic-methods-for-optimization/02_gradient-descent-extensions.mp413.63MB

1. intro-to-deep-learning/02_introduction-to-neural-networks/04_philosophy-of-deep-learning/02_deep-learning-as-a-language.mp413.66MB

2. competitive-data-science/01_introduction-recap/02_competition-mechanics/01_competition-mechanics.mp413.69MB

2. competitive-data-science/09_hyperparameter-optimization/01_hyperparameter-tuning/02_hyperparameter-tuning-i.mp413.73MB

3. bayesian-methods-in-machine-learning/05_variational-autoencoder/02_variational-dropout/03_sparse-variational-dropout.mp413.76MB

3. bayesian-methods-in-machine-learning/05_variational-autoencoder/02_variational-dropout/01_learning-with-priors.mp413.81MB

1. intro-to-deep-learning/02_introduction-to-neural-networks/01_multilayer-perceptron-or-the-basic-principles-of-deep-learning/02_training-a-neural-network.mp413.95MB

2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/09_classification-metrics-optimization-ii.mp413.96MB

2. competitive-data-science/02_feature-preprocessing-and-generation-with-respect-to-models/01_feature-preprocessing-and-generation-with-respect-to-models/01_overview.mp414.08MB

3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/02_gaussian-processes.mp414.14MB

2. competitive-data-science/05_validation/01_validation/02_validation-strategies.mp414.19MB

1. intro-to-deep-learning/01_introduction-to-optimization/03_regularization-in-machine-learning/01_overfitting-problem-and-model-validation.mp414.22MB

1. intro-to-deep-learning/05_deep-learning-for-sequences/02_modern-rnns/01_the-training-of-rnns-is-not-that-easy.mp414.51MB

3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/03_using-cnns-with-a-mixture-of-gaussians.mp414.61MB

1. intro-to-deep-learning/04_unsupervised-representation-learning/04_generative-adversarial-networks/01_generative-models-101.mp414.62MB

2. competitive-data-science/04_exploratory-data-analysis/01_exploratory-data-analysis/06_dataset-cleaning-and-other-things-to-check.mp414.7MB

2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/08_classification-metrics-optimization-i.mp414.71MB

3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/07_reparameterization-trick.mp414.74MB

1. intro-to-deep-learning/02_introduction-to-neural-networks/01_multilayer-perceptron-or-the-basic-principles-of-deep-learning/03_backpropagation-primer.mp414.96MB

3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/03_applications-and-examples/03_k-means-m-step.mp415.26MB

1. intro-to-deep-learning/04_unsupervised-representation-learning/02_more-autoencoders/02_autoencoder-applications-image-generation-data-visualization-more.mp415.33MB

3. bayesian-methods-in-machine-learning/05_variational-autoencoder/02_variational-dropout/02_dropout-as-bayesian-procedure.mp415.47MB

3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/05_example-of-gibbs-sampling.mp415.54MB

2. competitive-data-science/08_advanced-feature-engineering-i/01_mean-encodings/02_regularization.mp415.63MB

1. intro-to-deep-learning/02_introduction-to-neural-networks/02_tensorflow/03_gradients-optimization-in-tensorflow.mp415.65MB

2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/02_motivation.mp415.76MB

3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/09_markov-chain-monte-carlo-summary.mp415.83MB

1. intro-to-deep-learning/05_deep-learning-for-sequences/01_introduction-to-rnn/01_motivation-for-recurrent-layers.mp415.99MB

2. competitive-data-science/12_competitions-go-through/01_competitions-go-through/05_walmart-trip-type-classification.mp416.29MB

1. intro-to-deep-learning/02_introduction-to-neural-networks/04_philosophy-of-deep-learning/01_what-deep-learning-is-and-is-not.mp416.32MB

2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/04_regression-metrics-review-ii.mp416.61MB

1. intro-to-deep-learning/03_deep-learning-for-images/03_applications-of-cnns/02_a-glimpse-of-other-computer-vision-tasks.mp416.86MB

3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/06_metropolis-hastings.mp416.86MB

3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm/01_jensens-inequality-kullback-leibler-divergence.mp416.87MB

2. competitive-data-science/08_advanced-feature-engineering-i/01_mean-encodings/01_concept-of-mean-encoding.mp416.92MB

3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/03_applications-and-examples/02_k-means-from-probabilistic-perspective.mp416.93MB

3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/01_latent-variable-models/03_gaussian-mixture-model.mp417.5MB

2. competitive-data-science/01_introduction-recap/01_welcome-to-how-to-win-a-data-science-competition/02_course-overview.mp417.6MB

1. intro-to-deep-learning/03_deep-learning-for-images/02_modern-cnns/02_overview-of-modern-cnn-architectures.mp417.7MB

2. competitive-data-science/02_feature-preprocessing-and-generation-with-respect-to-models/01_feature-preprocessing-and-generation-with-respect-to-models/04_datetime-and-coordinates.mp417.73MB

3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/06_bayesian-optimization.mp418.17MB

2. competitive-data-science/01_introduction-recap/02_competition-mechanics/02_kaggle-overview-screencast.mp418.32MB

2. competitive-data-science/01_introduction-recap/03_recap-of-main-ml-algorithms/01_recap-of-main-ml-algorithms.mp418.32MB

1. intro-to-deep-learning/05_deep-learning-for-sequences/01_introduction-to-rnn/02_simple-rnn-and-backpropagation.mp418.36MB

1. intro-to-deep-learning/02_introduction-to-neural-networks/03_keras/01_keras-introduction.mp418.51MB

3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/01_latent-variable-models/05_example-of-gmm-training.mp418.53MB

1. intro-to-deep-learning/05_deep-learning-for-sequences/02_modern-rnns/02_dealing-with-vanishing-and-exploding-gradients.mp418.65MB

2. competitive-data-science/05_validation/01_validation/01_validation-and-overfitting.mp418.82MB

3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/01_latent-variable-models/04_training-gmm.mp418.87MB

2. competitive-data-science/06_data-leakages/01_data-leakages/02_leaderboard-probing-and-examples-of-rare-data-leaks.mp418.92MB

3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/02_modeling-a-distribution-of-images.mp418.98MB

3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm.mp419.01MB

1. intro-to-deep-learning/02_introduction-to-neural-networks/02_tensorflow/01_going-deeper-with-tensorflow.mp419.14MB

1. intro-to-deep-learning/01_introduction-to-optimization/02_linear-model-as-the-simplest-neural-network/01_linear-regression.mp419.18MB

2. competitive-data-science/06_data-leakages/01_data-leakages/03_expedia-challenge.mp419.49MB

2. competitive-data-science/11_ensembling/01_ensembling/06_ensembling-tips-and-tricks.mp419.55MB

1. intro-to-deep-learning/04_unsupervised-representation-learning/04_generative-adversarial-networks/02_generative-adversarial-networks.mp419.79MB

2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/07_regression-metrics-optimization.mp419.95MB

3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/11_bayesian-neural-networks.mp420.02MB

2. competitive-data-science/12_competitions-go-through/01_competitions-go-through/02_crowdflower-competition.mp420.14MB

1. intro-to-deep-learning/04_unsupervised-representation-learning/03_word-embeddings/01_natural-language-processing-primer.mp420.21MB

3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/07_metropolis-hastings-choosing-the-critic.mp420.27MB

3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/08_example-of-metropolis-hastings.mp420.49MB

1. intro-to-deep-learning/04_unsupervised-representation-learning/02_more-autoencoders/01_autoencoder-applications.mp420.53MB

2. competitive-data-science/02_feature-preprocessing-and-generation-with-respect-to-models/01_feature-preprocessing-and-generation-with-respect-to-models/05_handling-missing-values.mp420.93MB

3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/05_nuances-of-gp.mp421.26MB

2. competitive-data-science/02_feature-preprocessing-and-generation-with-respect-to-models/02_feature-extraction-from-text-and-images/01_bag-of-words.mp421.3MB

3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/01_latent-variable-models/01_latent-variable-models.mp421.31MB

2. competitive-data-science/11_ensembling/01_ensembling/05_stacknet.mp421.4MB

2. competitive-data-science/11_ensembling/01_ensembling/03_boosting.mp421.56MB

2. competitive-data-science/08_advanced-feature-engineering-i/01_mean-encodings/03_extensions-and-generalizations.mp421.68MB

2. competitive-data-science/02_feature-preprocessing-and-generation-with-respect-to-models/01_feature-preprocessing-and-generation-with-respect-to-models/03_categorical-and-ordinal-features.mp422.28MB

1. intro-to-deep-learning/04_unsupervised-representation-learning/04_generative-adversarial-networks/03_applications-of-adversarial-approach.mp422.79MB

1. intro-to-deep-learning/03_deep-learning-for-images/01_introduction-to-cnn/01_motivation-for-convolutional-layers.mp422.79MB

1. intro-to-deep-learning/01_introduction-to-optimization/02_linear-model-as-the-simplest-neural-network/02_linear-classification.mp422.82MB

3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/03_applications-and-examples/04_probabilistic-pca.mp423.12MB

1. intro-to-deep-learning/03_deep-learning-for-images/01_introduction-to-cnn/02_our-first-cnn-architecture.mp423.28MB

2. competitive-data-science/11_ensembling/01_ensembling/04_stacking.mp423.3MB

2. competitive-data-science/09_hyperparameter-optimization/01_hyperparameter-tuning/03_hyperparameter-tuning-ii.mp423.79MB

2. competitive-data-science/04_exploratory-data-analysis/01_exploratory-data-analysis/05_visualizations.mp423.89MB

3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/01_introduction-to-bayesian-methods/05_linear-regression.mp424.35MB

1. intro-to-deep-learning/05_deep-learning-for-sequences/02_modern-rnns/03_modern-rnns-lstm-and-gru.mp424.55MB

3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/01_monte-carlo-estimation.mp425.13MB

2. competitive-data-science/09_hyperparameter-optimization/01_hyperparameter-tuning/04_hyperparameter-tuning-iii.mp425.68MB

3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm/05_example-em-for-discrete-mixture-e-step.mp425.69MB

2. competitive-data-science/02_feature-preprocessing-and-generation-with-respect-to-models/02_feature-extraction-from-text-and-images/02_word2vec-cnn.mp425.84MB

3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/05_lda-e-step-z.mp426.09MB

2. competitive-data-science/04_exploratory-data-analysis/01_exploratory-data-analysis/04_exploring-anonymized-data.mp426.31MB

3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/03_markov-chains.mp426.45MB

1. intro-to-deep-learning/04_unsupervised-representation-learning/03_word-embeddings/02_word-embeddings.mp426.45MB

2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/03_regression-metrics-review-i.mp426.45MB

3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/02_sampling-from-1-d-distributions.mp426.59MB

2. competitive-data-science/02_feature-preprocessing-and-generation-with-respect-to-models/01_feature-preprocessing-and-generation-with-respect-to-models/02_numeric-features.mp426.85MB

2. competitive-data-science/04_exploratory-data-analysis/02_eda-examples/02_springleaf-competition-eda-ii.mp427.56MB

3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/10_mcmc-for-lda.mp427.63MB

3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/01_introduction-to-bayesian-methods/04_example-thief-alarm.mp427.67MB

3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/04_scaling-variational-em.mp427.69MB

1. intro-to-deep-learning/05_deep-learning-for-sequences/03_applications-of-rnns/01_practical-use-cases-for-rnns.mp429.11MB

3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm/06_example-em-for-discrete-mixture-m-step.mp429.3MB

3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/04_gibbs-sampling.mp429.32MB

3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/03_applications-and-examples/01_general-em-for-gmm.mp429.47MB

2. competitive-data-science/05_validation/01_validation/04_data-splitting-strategies.mp430.05MB

3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm/03_e-step-details.mp430.4MB

3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/04_derivation-of-main-formula.mp431.09MB

1. intro-to-deep-learning/03_deep-learning-for-images/02_modern-cnns/01_training-tips-and-tricks-for-deep-cnns.mp431.33MB

2. competitive-data-science/09_hyperparameter-optimization/02_tips-and-tricks/01_practical-guide.mp432.82MB

3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/04_lda-e-step-theta.mp433.36MB

3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/01_variational-inference/03_example-ising-model.mp433.5MB

3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/01_variational-inference/02_mean-field-approximation.mp435.44MB

2. competitive-data-science/12_competitions-go-through/01_competitions-go-through/04_microsoft-malware-classification-challenge.mp437.84MB

2. competitive-data-science/05_validation/01_validation/05_problems-occurring-during-validation.mp439.49MB

2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/05_classification-metrics-review.mp439.59MB

3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/06_lda-m-step-prediction.mp440.57MB