Advanced Machine Learning Specialization -移花宫-武林禁地，闲人禁止入内

Advanced Machine Learning Specialization

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Advanced Machine Learning Specialization

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文件列表

3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/01_introduction-to-bayesian-methods/06_mle-estimation-of-gaussian-mean_instructions.html 1.0 kB
1. intro-to-deep-learning/04_unsupervised-representation-learning/02_more-autoencoders/03_simple-autoencoder_instructions.html 1.1 kB
1. intro-to-deep-learning/02_introduction-to-neural-networks/02_tensorflow/04_logistic-regression-in-tensorflow_instructions.html 1.1 kB
1. intro-to-deep-learning/05_deep-learning-for-sequences/01_introduction-to-rnn/03_generating-names-with-rnns_instructions.html 1.1 kB
1. intro-to-deep-learning/02_introduction-to-neural-networks/02_tensorflow/02_mse-in-tensorflow_instructions.html 1.1 kB
1. intro-to-deep-learning/02_introduction-to-neural-networks/03_keras/02_my1stnn-keras-this-time_instructions.html 1.1 kB
1. intro-to-deep-learning/04_unsupervised-representation-learning/04_generative-adversarial-networks/04_generative-adversarial-networks_instructions.html 1.1 kB
2. competitive-data-science/14_Resources/02_cheet-sheets/01__resources.html 1.2 kB
1. intro-to-deep-learning/03_deep-learning-for-images/02_modern-cnns/03_your-first-cnn-on-cifar-10_instructions.html 1.2 kB
1. intro-to-deep-learning/06_final-project/01_final-project/01_image-captioning-final-project_instructions.html 1.2 kB
1. intro-to-deep-learning/03_deep-learning-for-images/03_applications-of-cnns/03_fine-tuning-inceptionv3-for-flowers-classification_instructions.html 1.2 kB
1. intro-to-deep-learning/01_introduction-to-optimization/04_stochastic-methods-for-optimization/03_linear-models-and-optimization_instructions.html 1.2 kB
3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/09_vae-paper_instructions.html 1.2 kB
2. competitive-data-science/03_final-project-description/01_final-project/03_final-project-advice-1_instructions.html 1.2 kB
2. competitive-data-science/09_hyperparameter-optimization/02_tips-and-tricks/02_additional-materials-and-links_instructions.html 1.2 kB
2. competitive-data-science/13_final-project/01_final-project/01_final-project_instructions.html 1.3 kB
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3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/08_variational-autoencoder_instructions.html 1.3 kB
2. competitive-data-science/06_data-leakages/01_data-leakages/06_additional-material-and-links_instructions.html 1.3 kB
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2. competitive-data-science/08_advanced-feature-engineering-i/01_mean-encodings/06_final-project-advice-3_instructions.html 1.3 kB
2. competitive-data-science/06_data-leakages/01_data-leakages/05_data-leakages_instructions.html 1.3 kB
2. competitive-data-science/01_introduction-recap/04_software-hardware-requirements/02_pandas-basics_instructions.html 1.3 kB
2. competitive-data-science/08_advanced-feature-engineering-i/01_mean-encodings/05_mean-encoding-implementation_instructions.html 1.4 kB
2. competitive-data-science/10_advanced-feature-engineering-ii/02_advanced-features-ii-programming-assignment/01_knn-features-implementation_instructions.html 1.4 kB
3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/12_pymc_instructions.html 1.5 kB
2. competitive-data-science/11_ensembling/01_ensembling/10_additional-materials-and-links_instructions.html 1.5 kB
3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/03_applications-and-examples/06_em-algorithm-for-gmm_instructions.html 1.5 kB
2. competitive-data-science/09_hyperparameter-optimization/01_hyperparameter-tuning/06_additional-material-and-links_instructions.html 1.6 kB
2. competitive-data-science/11_ensembling/01_ensembling/08_ensembling-implementation_instructions.html 1.6 kB
3. bayesian-methods-in-machine-learning/05_variational-autoencoder/03_honors-track-assignment/01_categorical-reparametrization-with-gumbel-softmax_instructions.html 1.6 kB
3. bayesian-methods-in-machine-learning/05_variational-autoencoder/02_variational-dropout/04_relevant-papers_instructions.html 1.6 kB
2. competitive-data-science/11_ensembling/01_ensembling/11_final-project-advice-4_instructions.html 1.7 kB
2. competitive-data-science/06_data-leakages/01_data-leakages/07_final-project-advice-2_instructions.html 1.8 kB
2. competitive-data-science/12_competitions-go-through/01_competitions-go-through/06_additional-material-and-links_instructions.html 1.8 kB
2. competitive-data-science/01_introduction-recap/03_recap-of-main-ml-algorithms/02_disclaimer_instructions.html 1.9 kB
2. competitive-data-science/04_exploratory-data-analysis/01_exploratory-data-analysis/07_additional-material-and-links_instructions.html 1.9 kB
3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/02_conjugate-priors/02_conjugate-distributions.en.txt 2.0 kB
2. competitive-data-science/02_feature-preprocessing-and-generation-with-respect-to-models/01_feature-preprocessing-and-generation-with-respect-to-models/07_additional-material-and-links_instructions.html 2.1 kB
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3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/01_introduction-to-bayesian-methods/03_how-to-define-a-model.en.txt 2.6 kB
2. competitive-data-science/10_advanced-feature-engineering-ii/01_advanced-features-ii/06_additional-materials-and-links_instructions.html 2.6 kB
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2. competitive-data-science/01_introduction-recap/03_recap-of-main-ml-algorithms/04_additional-materials-and-links_instructions.html 3.3 kB
3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/12_pymc_grader.py 3.4 kB
3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/02_conjugate-priors/04_example-bernoulli.en.txt 3.4 kB
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3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/02_conjugate-priors/02_conjugate-distributions.en.srt 3.4 kB
3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/08_gpy-and-gpyopt_grader.py 3.5 kB
2. competitive-data-science/05_validation/01_validation/03_validation-strategies_instructions.html 3.6 kB
3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/07_extensions-of-lda.en.txt 3.7 kB
3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/07_application-of-bayesian-optimization.en.txt 3.9 kB
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3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/02_conjugate-priors/03_example-normal-precision.en.txt 4.2 kB
2. competitive-data-science/10_advanced-feature-engineering-ii/01_advanced-features-ii/01_statistics-and-distance-based-features.en.txt 4.2 kB
3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/01_introduction-to-bayesian-methods/03_how-to-define-a-model.en.srt 4.2 kB
1. intro-to-deep-learning/02_introduction-to-neural-networks/01_multilayer-perceptron-or-the-basic-principles-of-deep-learning/01_multilayer-perceptron.en.txt 4.3 kB
3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/01_introduction-to-bayesian-methods/02_bayesian-approach-to-statistics.en.txt 4.4 kB
1. intro-to-deep-learning/03_deep-learning-for-images/03_applications-of-cnns/01_learning-new-tasks-with-pre-trained-cnns.en.txt 4.4 kB
3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/03_applications-and-examples/03_k-means-m-step.en.txt 4.4 kB
2. competitive-data-science/11_ensembling/01_ensembling/01_introduction-into-ensemble-methods.en.txt 4.5 kB
3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/05_lda-e-step-z.en.txt 4.5 kB
1. intro-to-deep-learning/01_introduction-to-optimization/02_linear-model-as-the-simplest-neural-network/03_gradient-descent.en.txt 4.5 kB
3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/01_variational-inference/04_variational-em-review.en.txt 4.6 kB
3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/01_nonparametric-methods.en.txt 4.6 kB
1. intro-to-deep-learning/01_introduction-to-optimization/03_regularization-in-machine-learning/02_model-regularization.en.txt 4.6 kB
2. competitive-data-science/10_advanced-feature-engineering-ii/01_advanced-features-ii/04_t-sne.en.txt 4.8 kB
2. competitive-data-science/04_exploratory-data-analysis/02_eda-examples/03_numerai-competition-eda.en.txt 4.8 kB
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2. competitive-data-science/01_introduction-recap/04_software-hardware-requirements/03_explanation-for-quiz-questions_instructions.html 5.0 kB
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1. intro-to-deep-learning/01_introduction-to-optimization/04_stochastic-methods-for-optimization/01_stochastic-gradient-descent.en.txt 5.1 kB
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1. intro-to-deep-learning/01_introduction-to-optimization/02_linear-model-as-the-simplest-neural-network/03_gradient-descent.mp4 10.7 MB
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1. intro-to-deep-learning/01_introduction-to-optimization/03_regularization-in-machine-learning/01_overfitting-problem-and-model-validation.mp4 14.9 MB
1. intro-to-deep-learning/05_deep-learning-for-sequences/02_modern-rnns/01_the-training-of-rnns-is-not-that-easy.mp4 15.2 MB
3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/03_using-cnns-with-a-mixture-of-gaussians.mp4 15.3 MB
1. intro-to-deep-learning/04_unsupervised-representation-learning/04_generative-adversarial-networks/01_generative-models-101.mp4 15.3 MB
2. competitive-data-science/04_exploratory-data-analysis/01_exploratory-data-analysis/06_dataset-cleaning-and-other-things-to-check.mp4 15.4 MB
2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/08_classification-metrics-optimization-i.mp4 15.4 MB
3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/07_reparameterization-trick.mp4 15.5 MB
1. intro-to-deep-learning/02_introduction-to-neural-networks/01_multilayer-perceptron-or-the-basic-principles-of-deep-learning/03_backpropagation-primer.mp4 15.7 MB
3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/03_applications-and-examples/03_k-means-m-step.mp4 16.0 MB
1. intro-to-deep-learning/04_unsupervised-representation-learning/02_more-autoencoders/02_autoencoder-applications-image-generation-data-visualization-more.mp4 16.1 MB
3. bayesian-methods-in-machine-learning/05_variational-autoencoder/02_variational-dropout/02_dropout-as-bayesian-procedure.mp4 16.2 MB
3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/05_example-of-gibbs-sampling.mp4 16.3 MB
2. competitive-data-science/08_advanced-feature-engineering-i/01_mean-encodings/02_regularization.mp4 16.4 MB
1. intro-to-deep-learning/02_introduction-to-neural-networks/02_tensorflow/03_gradients-optimization-in-tensorflow.mp4 16.4 MB
2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/02_motivation.mp4 16.5 MB
3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/09_markov-chain-monte-carlo-summary.mp4 16.6 MB
1. intro-to-deep-learning/05_deep-learning-for-sequences/01_introduction-to-rnn/01_motivation-for-recurrent-layers.mp4 16.8 MB
2. competitive-data-science/12_competitions-go-through/01_competitions-go-through/05_walmart-trip-type-classification.mp4 17.1 MB
1. intro-to-deep-learning/02_introduction-to-neural-networks/04_philosophy-of-deep-learning/01_what-deep-learning-is-and-is-not.mp4 17.1 MB
2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/04_regression-metrics-review-ii.mp4 17.4 MB
1. intro-to-deep-learning/03_deep-learning-for-images/03_applications-of-cnns/02_a-glimpse-of-other-computer-vision-tasks.mp4 17.7 MB
3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/06_metropolis-hastings.mp4 17.7 MB
3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm/01_jensens-inequality-kullback-leibler-divergence.mp4 17.7 MB
2. competitive-data-science/08_advanced-feature-engineering-i/01_mean-encodings/01_concept-of-mean-encoding.mp4 17.7 MB
3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/03_applications-and-examples/02_k-means-from-probabilistic-perspective.mp4 17.8 MB
3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/01_latent-variable-models/03_gaussian-mixture-model.mp4 18.4 MB
2. competitive-data-science/01_introduction-recap/01_welcome-to-how-to-win-a-data-science-competition/02_course-overview.mp4 18.5 MB
1. intro-to-deep-learning/03_deep-learning-for-images/02_modern-cnns/02_overview-of-modern-cnn-architectures.mp4 18.6 MB
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.mp4 18.6 MB
3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/06_bayesian-optimization.mp4 19.1 MB
2. competitive-data-science/01_introduction-recap/02_competition-mechanics/02_kaggle-overview-screencast.mp4 19.2 MB
2. competitive-data-science/01_introduction-recap/03_recap-of-main-ml-algorithms/01_recap-of-main-ml-algorithms.mp4 19.2 MB
1. intro-to-deep-learning/05_deep-learning-for-sequences/01_introduction-to-rnn/02_simple-rnn-and-backpropagation.mp4 19.2 MB
1. intro-to-deep-learning/02_introduction-to-neural-networks/03_keras/01_keras-introduction.mp4 19.4 MB
3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/01_latent-variable-models/05_example-of-gmm-training.mp4 19.4 MB
1. intro-to-deep-learning/05_deep-learning-for-sequences/02_modern-rnns/02_dealing-with-vanishing-and-exploding-gradients.mp4 19.6 MB
2. competitive-data-science/05_validation/01_validation/01_validation-and-overfitting.mp4 19.7 MB
3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/01_latent-variable-models/04_training-gmm.mp4 19.8 MB
2. competitive-data-science/06_data-leakages/01_data-leakages/02_leaderboard-probing-and-examples-of-rare-data-leaks.mp4 19.8 MB
3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/02_modeling-a-distribution-of-images.mp4 19.9 MB
3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm.mp4 19.9 MB
1. intro-to-deep-learning/02_introduction-to-neural-networks/02_tensorflow/01_going-deeper-with-tensorflow.mp4 20.1 MB
1. intro-to-deep-learning/01_introduction-to-optimization/02_linear-model-as-the-simplest-neural-network/01_linear-regression.mp4 20.1 MB
2. competitive-data-science/06_data-leakages/01_data-leakages/03_expedia-challenge.mp4 20.4 MB
2. competitive-data-science/11_ensembling/01_ensembling/06_ensembling-tips-and-tricks.mp4 20.5 MB
1. intro-to-deep-learning/04_unsupervised-representation-learning/04_generative-adversarial-networks/02_generative-adversarial-networks.mp4 20.7 MB
2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/07_regression-metrics-optimization.mp4 20.9 MB
3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/11_bayesian-neural-networks.mp4 21.0 MB
2. competitive-data-science/12_competitions-go-through/01_competitions-go-through/02_crowdflower-competition.mp4 21.1 MB
1. intro-to-deep-learning/04_unsupervised-representation-learning/03_word-embeddings/01_natural-language-processing-primer.mp4 21.2 MB
3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/07_metropolis-hastings-choosing-the-critic.mp4 21.3 MB
3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/08_example-of-metropolis-hastings.mp4 21.5 MB
1. intro-to-deep-learning/04_unsupervised-representation-learning/02_more-autoencoders/01_autoencoder-applications.mp4 21.5 MB
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.mp4 21.9 MB
3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/05_nuances-of-gp.mp4 22.3 MB
2. competitive-data-science/02_feature-preprocessing-and-generation-with-respect-to-models/02_feature-extraction-from-text-and-images/01_bag-of-words.mp4 22.3 MB
3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/01_latent-variable-models/01_latent-variable-models.mp4 22.3 MB
2. competitive-data-science/11_ensembling/01_ensembling/05_stacknet.mp4 22.4 MB
2. competitive-data-science/11_ensembling/01_ensembling/03_boosting.mp4 22.6 MB
2. competitive-data-science/08_advanced-feature-engineering-i/01_mean-encodings/03_extensions-and-generalizations.mp4 22.7 MB
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.mp4 23.4 MB
1. intro-to-deep-learning/04_unsupervised-representation-learning/04_generative-adversarial-networks/03_applications-of-adversarial-approach.mp4 23.9 MB
1. intro-to-deep-learning/03_deep-learning-for-images/01_introduction-to-cnn/01_motivation-for-convolutional-layers.mp4 23.9 MB
1. intro-to-deep-learning/01_introduction-to-optimization/02_linear-model-as-the-simplest-neural-network/02_linear-classification.mp4 23.9 MB
3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/03_applications-and-examples/04_probabilistic-pca.mp4 24.2 MB
1. intro-to-deep-learning/03_deep-learning-for-images/01_introduction-to-cnn/02_our-first-cnn-architecture.mp4 24.4 MB
2. competitive-data-science/11_ensembling/01_ensembling/04_stacking.mp4 24.4 MB
2. competitive-data-science/09_hyperparameter-optimization/01_hyperparameter-tuning/03_hyperparameter-tuning-ii.mp4 24.9 MB
2. competitive-data-science/04_exploratory-data-analysis/01_exploratory-data-analysis/05_visualizations.mp4 25.0 MB
3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/01_introduction-to-bayesian-methods/05_linear-regression.mp4 25.5 MB
1. intro-to-deep-learning/05_deep-learning-for-sequences/02_modern-rnns/03_modern-rnns-lstm-and-gru.mp4 25.7 MB
3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/01_monte-carlo-estimation.mp4 26.3 MB
2. competitive-data-science/09_hyperparameter-optimization/01_hyperparameter-tuning/04_hyperparameter-tuning-iii.mp4 26.9 MB
3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm/05_example-em-for-discrete-mixture-e-step.mp4 26.9 MB
2. competitive-data-science/02_feature-preprocessing-and-generation-with-respect-to-models/02_feature-extraction-from-text-and-images/02_word2vec-cnn.mp4 27.1 MB
3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/05_lda-e-step-z.mp4 27.4 MB
2. competitive-data-science/04_exploratory-data-analysis/01_exploratory-data-analysis/04_exploring-anonymized-data.mp4 27.6 MB
3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/03_markov-chains.mp4 27.7 MB
1. intro-to-deep-learning/04_unsupervised-representation-learning/03_word-embeddings/02_word-embeddings.mp4 27.7 MB
2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/03_regression-metrics-review-i.mp4 27.7 MB
3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/02_sampling-from-1-d-distributions.mp4 27.9 MB
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.mp4 28.2 MB
2. competitive-data-science/04_exploratory-data-analysis/02_eda-examples/02_springleaf-competition-eda-ii.mp4 28.9 MB
3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/10_mcmc-for-lda.mp4 29.0 MB
3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/01_introduction-to-bayesian-methods/04_example-thief-alarm.mp4 29.0 MB
3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/04_scaling-variational-em.mp4 29.0 MB
1. intro-to-deep-learning/05_deep-learning-for-sequences/03_applications-of-rnns/01_practical-use-cases-for-rnns.mp4 30.5 MB
3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm/06_example-em-for-discrete-mixture-m-step.mp4 30.7 MB
3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/04_gibbs-sampling.mp4 30.7 MB
3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/03_applications-and-examples/01_general-em-for-gmm.mp4 30.9 MB
2. competitive-data-science/05_validation/01_validation/04_data-splitting-strategies.mp4 31.5 MB
3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm/03_e-step-details.mp4 31.9 MB
3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/04_derivation-of-main-formula.mp4 32.6 MB
1. intro-to-deep-learning/03_deep-learning-for-images/02_modern-cnns/01_training-tips-and-tricks-for-deep-cnns.mp4 32.8 MB
2. competitive-data-science/09_hyperparameter-optimization/02_tips-and-tricks/01_practical-guide.mp4 34.4 MB
3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/04_lda-e-step-theta.mp4 35.0 MB
3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/01_variational-inference/03_example-ising-model.mp4 35.1 MB
3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/01_variational-inference/02_mean-field-approximation.mp4 37.2 MB
2. competitive-data-science/12_competitions-go-through/01_competitions-go-through/04_microsoft-malware-classification-challenge.mp4 39.7 MB
2. competitive-data-science/05_validation/01_validation/05_problems-occurring-during-validation.mp4 41.4 MB
2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/05_classification-metrics-review.mp4 41.5 MB
3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/06_lda-m-step-prediction.mp4 42.5 MB

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