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Advanced Machine Learning Specialization

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Advanced Machine Learning Specialization

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3.3 GB

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709

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9A31F0C4690810429C38E93EF0B80AE51A3B6840

/.../01_introduction-to-bayesian-methods/

06_mle-estimation-of-gaussian-mean_instructions.html

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03_how-to-define-a-model.en.txt

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03_how-to-define-a-model.en.srt

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02_bayesian-approach-to-statistics.en.txt

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01_think-bayesian-statistics-review.en.txt

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05_linear-regression.en.txt

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02_bayesian-approach-to-statistics.en.srt

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04_example-thief-alarm.en.txt

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01_think-bayesian-statistics-review.en.srt

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05_linear-regression.en.srt

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04_example-thief-alarm.en.srt

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06_mle-estimation-of-gaussian-mean_MLE_for_Gaussian.pdf

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03_how-to-define-a-model_w1a3.pdf

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02_bayesian-approach-to-statistics_w1a2.pdf

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04_example-thief-alarm_w1a4.pdf

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01_think-bayesian-statistics-review_w1a1.pdf

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05_linear-regression_w1a5.pdf

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03_how-to-define-a-model.mp4

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02_bayesian-approach-to-statistics.mp4

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01_think-bayesian-statistics-review.mp4

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05_linear-regression.mp4

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04_example-thief-alarm.mp4

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/.../02_more-autoencoders/

03_simple-autoencoder_instructions.html

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02_autoencoder-applications-image-generation-data-visualization-more.en.txt

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01_autoencoder-applications.en.txt

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02_autoencoder-applications-image-generation-data-visualization-more.en.srt

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01_autoencoder-applications.en.srt

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02_autoencoder-applications-image-generation-data-visualization-more.mp4

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01_autoencoder-applications.mp4

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/.../02_introduction-to-neural-networks/02_tensorflow/

04_logistic-regression-in-tensorflow_instructions.html

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02_mse-in-tensorflow_instructions.html

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03_gradients-optimization-in-tensorflow.en.txt

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01_going-deeper-with-tensorflow.en.txt

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03_gradients-optimization-in-tensorflow.en.srt

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01_going-deeper-with-tensorflow.en.srt

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03_gradients-optimization-in-tensorflow.mp4

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01_going-deeper-with-tensorflow.mp4

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/.../01_introduction-to-rnn/

03_generating-names-with-rnns_instructions.html

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01_motivation-for-recurrent-layers.en.txt

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02_simple-rnn-and-backpropagation.en.txt

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01_motivation-for-recurrent-layers.en.srt

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02_simple-rnn-and-backpropagation.en.srt

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01_motivation-for-recurrent-layers.mp4

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02_simple-rnn-and-backpropagation.mp4

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/.../02_introduction-to-neural-networks/03_keras/

02_my1stnn-keras-this-time_instructions.html

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01_keras-introduction.en.txt

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01_keras-introduction.en.srt

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01_keras-introduction.mp4

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/.../04_generative-adversarial-networks/

04_generative-adversarial-networks_instructions.html

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01_generative-models-101.en.txt

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02_generative-adversarial-networks.en.txt

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03_applications-of-adversarial-approach.en.txt

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01_generative-models-101.en.srt

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02_generative-adversarial-networks.en.srt

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03_applications-of-adversarial-approach.en.srt

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01_generative-models-101.mp4

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02_generative-adversarial-networks.mp4

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03_applications-of-adversarial-approach.mp4

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/.../02_cheet-sheets/

01__resources.html

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/.../02_modern-cnns/

03_your-first-cnn-on-cifar-10_instructions.html

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02_overview-of-modern-cnn-architectures.en.txt

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02_overview-of-modern-cnn-architectures.en.srt

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01_training-tips-and-tricks-for-deep-cnns.en.txt

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01_training-tips-and-tricks-for-deep-cnns.en.srt

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01_training-tips-and-tricks-for-deep-cnns_w3_3_tricks_final.pdf

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02_overview-of-modern-cnn-architectures_w3_4_modern_arch_final.pdf

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02_overview-of-modern-cnn-architectures.mp4

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01_training-tips-and-tricks-for-deep-cnns.mp4

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/.../01_final-project/

01_image-captioning-final-project_instructions.html

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/.../03_applications-of-cnns/

03_fine-tuning-inceptionv3-for-flowers-classification_instructions.html

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01_learning-new-tasks-with-pre-trained-cnns.en.txt

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02_a-glimpse-of-other-computer-vision-tasks.en.txt

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01_learning-new-tasks-with-pre-trained-cnns.en.srt

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02_a-glimpse-of-other-computer-vision-tasks.en.srt

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01_learning-new-tasks-with-pre-trained-cnns_w3_5_transfer_learning_final.pdf

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02_a-glimpse-of-other-computer-vision-tasks_w3_6_other_problems_final.pdf

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01_learning-new-tasks-with-pre-trained-cnns.mp4

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02_a-glimpse-of-other-computer-vision-tasks.mp4

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/.../04_stochastic-methods-for-optimization/

03_linear-models-and-optimization_instructions.html

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01_stochastic-gradient-descent.en.txt

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01_stochastic-gradient-descent.en.srt

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02_gradient-descent-extensions.en.txt

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02_gradient-descent-extensions.en.srt

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01_stochastic-gradient-descent_w1_4_1_sgd.pdf

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02_gradient-descent-extensions_w1_4_2_sgd.pdf

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01_stochastic-gradient-descent.mp4

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02_gradient-descent-extensions.mp4

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/.../01_variational-autoencoders/

09_vae-paper_instructions.html

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08_variational-autoencoder_instructions.html

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05_gradient-of-decoder.en.txt

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06_log-derivative-trick.en.txt

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01_scaling-variational-inference-unbiased-estimates.en.txt

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07_reparameterization-trick.en.txt

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03_using-cnns-with-a-mixture-of-gaussians.en.txt

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05_gradient-of-decoder.en.srt

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06_log-derivative-trick.en.srt

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01_scaling-variational-inference-unbiased-estimates.en.srt

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02_modeling-a-distribution-of-images.en.txt

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07_reparameterization-trick.en.srt

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03_using-cnns-with-a-mixture-of-gaussians.en.srt

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09_vae-paper_1312.6114

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04_scaling-variational-em.en.txt

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02_modeling-a-distribution-of-images.en.srt

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04_scaling-variational-em.en.srt

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08_variational-autoencoder_assignment_5.zip

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06_log-derivative-trick_w5a5_alex.pdf

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03_using-cnns-with-a-mixture-of-gaussians_w5a2_alex.pdf

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07_reparameterization-trick_w5a6_alex.pdf

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01_scaling-variational-inference-unbiased-estimates_w5a1_alex.pdf

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02_modeling-a-distribution-of-images_w5a1.5_alex.pdf

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05_gradient-of-decoder_w5a4_alex.pdf

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04_scaling-variational-em_w5a3_alex.pdf

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05_gradient-of-decoder.mp4

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01_scaling-variational-inference-unbiased-estimates.mp4

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06_log-derivative-trick.mp4

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03_using-cnns-with-a-mixture-of-gaussians.mp4

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07_reparameterization-trick.mp4

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02_modeling-a-distribution-of-images.mp4

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04_scaling-variational-em.mp4

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/.../01_final-project/

03_final-project-advice-1_instructions.html

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01_final-project_instructions.html

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02_final-project-overview.en.txt

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02_final-project-overview.en.srt

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02_final-project-overview.mp4

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/.../02_tips-and-tricks/

02_additional-materials-and-links_instructions.html

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01_practical-guide.en.txt

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01_practical-guide.en.srt

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01_practical-guide_practical_guide.pdf

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01_practical-guide.mp4

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/.../01_final-project/

01_final-project_instructions.html

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/.../01_gaussian-processes-and-bayesian-optimization/

08_gpy-and-gpyopt_instructions.html

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08_gpy-and-gpyopt_grader.py

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07_application-of-bayesian-optimization.en.txt

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03_gp-for-machine-learning.en.txt

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01_nonparametric-methods.en.txt

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04_derivation-of-main-formula.en.txt

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02_gaussian-processes.en.txt

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07_application-of-bayesian-optimization.en.srt

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03_gp-for-machine-learning.en.srt

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01_nonparametric-methods.en.srt

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06_bayesian-optimization.en.txt

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05_nuances-of-gp.en.txt

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04_derivation-of-main-formula.en.srt

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02_gaussian-processes.en.srt

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06_bayesian-optimization.en.srt

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05_nuances-of-gp.en.srt

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08_gpy-and-gpyopt_Coursera_BMML_week_6.ipynb

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03_gp-for-machine-learning_w6a3.pdf

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01_nonparametric-methods_w6a1.pdf

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02_gaussian-processes_w6a2.pdf

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05_nuances-of-gp_w6a4.pdf

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06_bayesian-optimization_w6a5.pdf

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07_application-of-bayesian-optimization_w6a6.pdf

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07_application-of-bayesian-optimization.mp4

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03_gp-for-machine-learning.mp4

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01_nonparametric-methods.mp4

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02_gaussian-processes.mp4

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06_bayesian-optimization.mp4

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05_nuances-of-gp.mp4

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04_derivation-of-main-formula.mp4

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/.../01_data-leakages/

06_additional-material-and-links_instructions.html

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05_data-leakages_instructions.html

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07_final-project-advice-2_instructions.html

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04_comments-on-quiz_instructions.html

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01_basic-data-leaks.en.txt

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03_expedia-challenge.en.txt

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02_leaderboard-probing-and-examples-of-rare-data-leaks.en.txt

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01_basic-data-leaks.en.srt

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03_expedia-challenge.en.srt

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02_leaderboard-probing-and-examples-of-rare-data-leaks.en.srt

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01_basic-data-leaks_leaks_basics.pdf

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01_basic-data-leaks_w3_leaks_1.pptx

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02_leaderboard-probing-and-examples-of-rare-data-leaks_w3_leaks_2.pptx

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02_leaderboard-probing-and-examples-of-rare-data-leaks_leaks_probing.pdf

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03_expedia-challenge_leaks_expedia.pdf

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03_expedia-challenge_w3_expedia.pptx

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01_basic-data-leaks.mp4

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02_leaderboard-probing-and-examples-of-rare-data-leaks.mp4

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03_expedia-challenge.mp4

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/05_validation/01_validation/

07_additional-material-and-links_instructions.html

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03_validation-strategies_instructions.html

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02_validation-strategies.en.txt

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06_comments-on-quiz_instructions.html

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01_validation-and-overfitting.en.txt

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02_validation-strategies.en.srt

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04_data-splitting-strategies.en.txt

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01_validation-and-overfitting.en.srt

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05_problems-occurring-during-validation.en.txt

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04_data-splitting-strategies.en.srt

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05_problems-occurring-during-validation.en.srt

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07_additional-material-and-links_cross_validation.html

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02_validation-strategies_Validation_strategies.pdf

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01_validation-and-overfitting_Validation_and_overfitting.pdf

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05_problems-occurring-during-validation_Common_validation_problems.pdf

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04_data-splitting-strategies_Data_splitting_strategies.pdf

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02_validation-strategies.mp4

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01_validation-and-overfitting.mp4

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04_data-splitting-strategies.mp4

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05_problems-occurring-during-validation.mp4

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/.../01_mean-encodings/

06_final-project-advice-3_instructions.html

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05_mean-encoding-implementation_instructions.html

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04_comments-on-quiz_instructions.html

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02_regularization.en.txt

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01_concept-of-mean-encoding.en.txt

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03_extensions-and-generalizations.en.txt

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02_regularization.en.srt

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01_concept-of-mean-encoding.en.srt

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03_extensions-and-generalizations.en.srt

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03_extensions-and-generalizations_mean_encodings_part3.pdf

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03_extensions-and-generalizations_w3_mean_encs_p3.pptx

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01_concept-of-mean-encoding_w3_mean_encs_p1.pptx

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01_concept-of-mean-encoding_mean_encodings_part1.pdf

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02_regularization_w3_mean_encs_p2.pptx

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02_regularization_mean_encodings_part2.pdf

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02_regularization.mp4

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01_concept-of-mean-encoding.mp4

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03_extensions-and-generalizations.mp4

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/.../04_software-hardware-requirements/

02_pandas-basics_instructions.html

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04_additional-material-and-links_instructions.html

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03_explanation-for-quiz-questions_instructions.html

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01_software-hardware-requirements.en.txt

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01_software-hardware-requirements.en.srt

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04_additional-material-and-links_using-spot-instances.html

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04_additional-material-and-links_1109.0887.pdf

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01_software-hardware-requirements_SoftHard.pdf

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01_software-hardware-requirements.mp4

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/.../02_advanced-features-ii-programming-assignment/

01_knn-features-implementation_instructions.html

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/.../04_markov-chain-monte-carlo/01_mcmc/

12_pymc_instructions.html

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12_pymc_grader.py

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07_metropolis-hastings-choosing-the-critic.en.txt

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05_example-of-gibbs-sampling.en.txt

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06_metropolis-hastings.en.txt

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12_pymc_Week4._Practical_Assignment._MCMC.ipynb

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09_markov-chain-monte-carlo-summary.en.txt

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04_gibbs-sampling.en.txt

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08_example-of-metropolis-hastings.en.txt

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11_bayesian-neural-networks.en.txt

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07_metropolis-hastings-choosing-the-critic.en.srt

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05_example-of-gibbs-sampling.en.srt

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03_markov-chains.en.txt

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06_metropolis-hastings.en.srt

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02_sampling-from-1-d-distributions.en.txt

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01_monte-carlo-estimation.en.txt

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09_markov-chain-monte-carlo-summary.en.srt

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10_mcmc-for-lda.en.txt

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08_example-of-metropolis-hastings.en.srt

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11_bayesian-neural-networks.en.srt

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03_markov-chains.en.srt

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02_sampling-from-1-d-distributions.en.srt

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01_monte-carlo-estimation.en.srt

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10_mcmc-for-lda.en.srt

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07_metropolis-hastings-choosing-the-critic_w4b3_after_board_alex.pdf

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04_gibbs-sampling_w4b2.1_alex.pdf

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09_markov-chain-monte-carlo-summary_w4b5_alex.pdf

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06_metropolis-hastings_w4b3_alex.pdf

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11_bayesian-neural-networks_w4c2_alex.pdf

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02_sampling-from-1-d-distributions_w4a2_alex.pdf

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01_monte-carlo-estimation_w4a1_alex.pdf

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05_example-of-gibbs-sampling_w4b2.2_alex.pdf

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03_markov-chains_w4b1_alex.pdf

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10_mcmc-for-lda_w4c1_alex.pdf

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08_example-of-metropolis-hastings_w4b4_alex.pdf

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05_example-of-gibbs-sampling.mp4

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09_markov-chain-monte-carlo-summary.mp4

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06_metropolis-hastings.mp4

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11_bayesian-neural-networks.mp4

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07_metropolis-hastings-choosing-the-critic.mp4

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08_example-of-metropolis-hastings.mp4

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01_monte-carlo-estimation.mp4

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03_markov-chains.mp4

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02_sampling-from-1-d-distributions.mp4

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10_mcmc-for-lda.mp4

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04_gibbs-sampling.mp4

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/11_ensembling/01_ensembling/

10_additional-materials-and-links_instructions.html

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08_ensembling-implementation_instructions.html

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11_final-project-advice-4_instructions.html

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01_introduction-into-ensemble-methods.en.txt

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02_bagging.en.txt

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01_introduction-into-ensemble-methods.en.srt

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09_comments-on-quiz_instructions.html

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07_validation-schemes-for-2-nd-level-models_instructions.html

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06_ensembling-tips-and-tricks.en.txt

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05_stacknet.en.txt

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02_bagging.en.srt

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03_boosting.en.txt

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04_stacking.en.srt

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03_boosting.en.srt

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01_introduction-into-ensemble-methods_Ensemble_methods.pdf

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02_bagging_Bagging.pdf

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03_boosting_Boosting.pdf

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04_stacking_Stacking.pdf

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05_stacknet_Stacknet.pdf

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01_introduction-into-ensemble-methods.mp4

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02_bagging.mp4

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06_ensembling-tips-and-tricks.mp4

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05_stacknet.mp4

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03_boosting.mp4

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04_stacking.mp4

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/.../03_applications-and-examples/

06_em-algorithm-for-gmm_instructions.html

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06_em-algorithm-for-gmm_grader.py

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03_k-means-m-step.en.txt

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06_em-algorithm-for-gmm_samples.npz

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05_em-for-probabilistic-pca.en.txt

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02_k-means-from-probabilistic-perspective.en.txt

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03_k-means-m-step.en.srt

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01_general-em-for-gmm.en.txt

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05_em-for-probabilistic-pca.en.srt

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04_probabilistic-pca.en.txt

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02_k-means-from-probabilistic-perspective.en.srt

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01_general-em-for-gmm.en.srt

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04_probabilistic-pca.en.srt

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06_em-algorithm-for-gmm_Coursera_BMML_week_2.ipynb

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03_k-means-m-step_w2c2.2_alex.pdf

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01_general-em-for-gmm_w2c1_alex.pdf

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05_em-for-probabilistic-pca_w2c5_alex.pdf

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02_k-means-from-probabilistic-perspective_w2c2.1_alex.pdf

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04_probabilistic-pca_w2c4_alex.pdf

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05_em-for-probabilistic-pca.mp4

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03_k-means-m-step.mp4

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02_k-means-from-probabilistic-perspective.mp4

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04_probabilistic-pca.mp4

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01_general-em-for-gmm.mp4

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/.../01_hyperparameter-tuning/

06_additional-material-and-links_instructions.html

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01_week-4-overview_instructions.html

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02_hyperparameter-tuning-i.en.txt

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05_comments-on-quiz_instructions.html

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03_hyperparameter-tuning-ii.en.txt

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04_hyperparameter-tuning-iii.en.txt

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01_what-deep-learning-is-and-is-not.en.txt

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02_deep-learning-as-a-language.en.srt

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01_what-deep-learning-is-and-is-not.en.srt

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02_deep-learning-as-a-language.mp4

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01_what-deep-learning-is-and-is-not.mp4

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/.../01_introduction-to-cnn/

02_our-first-cnn-architecture.en.txt

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01_motivation-for-convolutional-layers.en.txt

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02_our-first-cnn-architecture.en.srt

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01_motivation-for-convolutional-layers.en.srt

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01_motivation-for-convolutional-layers_w3_1_convolutions_final.pdf

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02_our-first-cnn-architecture_w3_2_pooling_lenet_final.pdf

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01_motivation-for-convolutional-layers.mp4

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02_our-first-cnn-architecture.mp4

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/.../03_word-embeddings/

01_natural-language-processing-primer.en.txt

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02_word-embeddings.en.txt

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01_natural-language-processing-primer.en.srt

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02_word-embeddings.en.srt

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01_natural-language-processing-primer.mp4

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02_word-embeddings.mp4

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/.../03_applications-of-rnns/

01_practical-use-cases-for-rnns.en.txt

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01_practical-use-cases-for-rnns.en.srt

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01_practical-use-cases-for-rnns.mp4

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/

bayesian-methods-in-machine-learning-syllabus-parsed.json

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competitive-data-science-syllabus-parsed.json

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Total files 709


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