Advanced Machine Learning Specialization
File List
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| 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 | 1.02 KB |
| 1. intro-to-deep-learning/04_unsupervised-representation-learning/02_more-autoencoders/03_simple-autoencoder_instructions.html | 1.05 KB |
| 1. intro-to-deep-learning/02_introduction-to-neural-networks/02_tensorflow/04_logistic-regression-in-tensorflow_instructions.html | 1.06 KB |
| 1. intro-to-deep-learning/05_deep-learning-for-sequences/01_introduction-to-rnn/03_generating-names-with-rnns_instructions.html | 1.06 KB |
| 1. intro-to-deep-learning/02_introduction-to-neural-networks/02_tensorflow/02_mse-in-tensorflow_instructions.html | 1.07 KB |
| 1. intro-to-deep-learning/02_introduction-to-neural-networks/03_keras/02_my1stnn-keras-this-time_instructions.html | 1.09 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 | 1.12 KB |
| 1. intro-to-deep-learning/03_deep-learning-for-images/02_modern-cnns/03_your-first-cnn-on-cifar-10_instructions.html | 1.14 KB |
| 1. intro-to-deep-learning/06_final-project/01_final-project/01_image-captioning-final-project_instructions.html | 1.14 KB |
| 1. intro-to-deep-learning/03_deep-learning-for-images/03_applications-of-cnns/03_fine-tuning-inceptionv3-for-flowers-classification_instructions.html | 1.16 KB |
| 1. intro-to-deep-learning/01_introduction-to-optimization/04_stochastic-methods-for-optimization/03_linear-models-and-optimization_instructions.html | 1.16 KB |
| 3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/09_vae-paper_instructions.html | 1.16 KB |
| 2. competitive-data-science/03_final-project-description/01_final-project/03_final-project-advice-1_instructions.html | 1.16 KB |
| 2. competitive-data-science/09_hyperparameter-optimization/02_tips-and-tricks/02_additional-materials-and-links_instructions.html | 1.18 KB |
| 2. competitive-data-science/13_final-project/01_final-project/01_final-project_instructions.html | 1.22 KB |
| 3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/08_gpy-and-gpyopt_instructions.html | 1.24 KB |
| 3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/08_variational-autoencoder_instructions.html | 1.25 KB |
| 2. competitive-data-science/06_data-leakages/01_data-leakages/06_additional-material-and-links_instructions.html | 1.25 KB |
| 2. competitive-data-science/05_validation/01_validation/07_additional-material-and-links_instructions.html | 1.25 KB |
| 2. competitive-data-science/08_advanced-feature-engineering-i/01_mean-encodings/06_final-project-advice-3_instructions.html | 1.27 KB |
| 2. competitive-data-science/06_data-leakages/01_data-leakages/05_data-leakages_instructions.html | 1.31 KB |
| 2. competitive-data-science/01_introduction-recap/04_software-hardware-requirements/02_pandas-basics_instructions.html | 1.31 KB |
| 2. competitive-data-science/08_advanced-feature-engineering-i/01_mean-encodings/05_mean-encoding-implementation_instructions.html | 1.38 KB |
| 2. competitive-data-science/10_advanced-feature-engineering-ii/02_advanced-features-ii-programming-assignment/01_knn-features-implementation_instructions.html | 1.39 KB |
| 3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/12_pymc_instructions.html | 1.45 KB |
| 2. competitive-data-science/11_ensembling/01_ensembling/10_additional-materials-and-links_instructions.html | 1.46 KB |
| 3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/03_applications-and-examples/06_em-algorithm-for-gmm_instructions.html | 1.47 KB |
| 2. competitive-data-science/09_hyperparameter-optimization/01_hyperparameter-tuning/06_additional-material-and-links_instructions.html | 1.52 KB |
| 2. competitive-data-science/11_ensembling/01_ensembling/08_ensembling-implementation_instructions.html | 1.57 KB |
| 3. bayesian-methods-in-machine-learning/05_variational-autoencoder/03_honors-track-assignment/01_categorical-reparametrization-with-gumbel-softmax_instructions.html | 1.57 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 | 1.65 KB |
| 2. competitive-data-science/06_data-leakages/01_data-leakages/07_final-project-advice-2_instructions.html | 1.72 KB |
| 2. competitive-data-science/12_competitions-go-through/01_competitions-go-through/06_additional-material-and-links_instructions.html | 1.77 KB |
| 2. competitive-data-science/01_introduction-recap/03_recap-of-main-ml-algorithms/02_disclaimer_instructions.html | 1.87 KB |
| 2. competitive-data-science/04_exploratory-data-analysis/01_exploratory-data-analysis/07_additional-material-and-links_instructions.html | 1.87 KB |
| 3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/02_conjugate-priors/02_conjugate-distributions.en.txt | 1.98 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 | 2.06 KB |
| 2. competitive-data-science/01_introduction-recap/01_welcome-to-how-to-win-a-data-science-competition/03_week-1-overview_instructions.html | 2.1 KB |
| 2. competitive-data-science/12_competitions-go-through/01_competitions-go-through/01_week-5-overview_instructions.html | 2.27 KB |
| 2. competitive-data-science/03_final-project-description/01_final-project/01_final-project_instructions.html | 2.4 KB |
| 2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/01_week-3-overview_instructions.html | 2.48 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.txt | 2.52 KB |
| 2. competitive-data-science/10_advanced-feature-engineering-ii/01_advanced-features-ii/06_additional-materials-and-links_instructions.html | 2.57 KB |
| 2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/11_additional-material-and-links_instructions.html | 2.58 KB |
| 2. competitive-data-science/14_Resources/01_glossary/01__resources.html | 2.73 KB |
| 2. competitive-data-science/01_introduction-recap/04_software-hardware-requirements/04_additional-material-and-links_instructions.html | 2.75 KB |
| 2. competitive-data-science/04_exploratory-data-analysis/01_exploratory-data-analysis/01_week-2-overview_instructions.html | 2.83 KB |
| 3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/02_conjugate-priors/01_analytical-inference.en.txt | 2.91 KB |
| 3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/03_applications-and-examples/06_em-algorithm-for-gmm_grader.py | 3.01 KB |
| 2. competitive-data-science/09_hyperparameter-optimization/01_hyperparameter-tuning/01_week-4-overview_instructions.html | 3.03 KB |
| 1. intro-to-deep-learning/01_introduction-to-optimization/01_course-intro/01_welcome_instructions.html | 3.07 KB |
| 2. competitive-data-science/02_feature-preprocessing-and-generation-with-respect-to-models/02_feature-extraction-from-text-and-images/04_additional-material-and-links_instructions.html | 3.15 KB |
| 2. competitive-data-science/01_introduction-recap/03_recap-of-main-ml-algorithms/04_additional-materials-and-links_instructions.html | 3.24 KB |
| 3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/12_pymc_grader.py | 3.29 KB |
| 3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/02_conjugate-priors/04_example-bernoulli.en.txt | 3.29 KB |
| 2. competitive-data-science/03_final-project-description/01_final-project/02_final-project-overview.en.txt | 3.32 KB |
| 3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/02_conjugate-priors/02_conjugate-distributions.en.srt | 3.37 KB |
| 3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/08_gpy-and-gpyopt_grader.py | 3.39 KB |
| 2. competitive-data-science/05_validation/01_validation/03_validation-strategies_instructions.html | 3.47 KB |
| 3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/07_extensions-of-lda.en.txt | 3.59 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 | 3.81 KB |
| 3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/03_gp-for-machine-learning.en.txt | 3.83 KB |
| 3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/01_topic-modeling.en.txt | 3.88 KB |
| 3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/01_variational-inference/01_why-approximate-inference.en.txt | 3.93 KB |
| 3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/03_latent-dirichlet-allocation.en.txt | 4 KB |
| 2. competitive-data-science/06_data-leakages/01_data-leakages/04_comments-on-quiz_instructions.html | 4.02 KB |
| 3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/02_conjugate-priors/03_example-normal-precision.en.txt | 4.07 KB |
| 2. competitive-data-science/10_advanced-feature-engineering-ii/01_advanced-features-ii/01_statistics-and-distance-based-features.en.txt | 4.1 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 | 4.14 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 | 4.15 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 | 4.28 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 | 4.29 KB |
| 3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/03_applications-and-examples/03_k-means-m-step.en.txt | 4.34 KB |
| 2. competitive-data-science/11_ensembling/01_ensembling/01_introduction-into-ensemble-methods.en.txt | 4.36 KB |
| 3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/05_lda-e-step-z.en.txt | 4.37 KB |
| 1. intro-to-deep-learning/01_introduction-to-optimization/02_linear-model-as-the-simplest-neural-network/03_gradient-descent.en.txt | 4.4 KB |
| 3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/01_variational-inference/04_variational-em-review.en.txt | 4.51 KB |
| 3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/01_nonparametric-methods.en.txt | 4.51 KB |
| 1. intro-to-deep-learning/01_introduction-to-optimization/03_regularization-in-machine-learning/02_model-regularization.en.txt | 4.52 KB |
| 2. competitive-data-science/10_advanced-feature-engineering-ii/01_advanced-features-ii/04_t-sne.en.txt | 4.66 KB |
| 2. competitive-data-science/04_exploratory-data-analysis/02_eda-examples/03_numerai-competition-eda.en.txt | 4.67 KB |
| 2. competitive-data-science/10_advanced-feature-engineering-ii/01_advanced-features-ii/03_feature-interactions.en.txt | 4.72 KB |
| 3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/05_gradient-of-decoder.en.txt | 4.75 KB |
| 3. bayesian-methods-in-machine-learning/05_variational-autoencoder/02_variational-dropout/03_sparse-variational-dropout.en.txt | 4.78 KB |
| 1. intro-to-deep-learning/02_introduction-to-neural-networks/02_tensorflow/03_gradients-optimization-in-tensorflow.en.txt | 4.81 KB |
| 2. competitive-data-science/06_data-leakages/01_data-leakages/01_basic-data-leaks.en.txt | 4.82 KB |
| 3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/02_conjugate-priors/01_analytical-inference.en.srt | 4.86 KB |
| 2. competitive-data-science/01_introduction-recap/04_software-hardware-requirements/03_explanation-for-quiz-questions_instructions.html | 4.87 KB |
| 2. competitive-data-science/01_introduction-recap/04_software-hardware-requirements/01_software-hardware-requirements.en.txt | 4.91 KB |
| 2. competitive-data-science/12_competitions-go-through/01_competitions-go-through/03_springleaf-marketing-response.en.txt | 4.95 KB |
| 1. intro-to-deep-learning/01_introduction-to-optimization/04_stochastic-methods-for-optimization/01_stochastic-gradient-descent.en.txt | 4.95 KB |
| 1. intro-to-deep-learning/02_introduction-to-neural-networks/03_keras/01_keras-introduction.en.txt | 4.95 KB |
| 3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm/04_m-step-details.en.txt | 4.96 KB |
| 2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/06_general-approaches-for-metrics-optimization.en.txt | 4.96 KB |
| 1. intro-to-deep-learning/02_introduction-to-neural-networks/01_multilayer-perceptron-or-the-basic-principles-of-deep-learning/03_backpropagation-primer.en.txt | 4.98 KB |
| 3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/02_dirichlet-distribution.en.txt | 5 KB |
| 3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/04_derivation-of-main-formula.en.txt | 5.02 KB |
| 3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/01_latent-variable-models/02_probabilistic-clustering.en.txt | 5.03 KB |
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