Advanced Machine Learning Specialization

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9A31F0C4690810429C38E93EF0B80AE51A3B6840
Number of Files:
709
Content Size:
3.07GB
Convert On:
2022-05-07
Magnet Link:
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1. intro-to-deep-learning/01_introduction-to-optimization/04_stochastic-methods-for-optimization/01_stochastic-gradient-descent.mp4
11.4MB
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3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm/04_m-step-details.mp4
11.43MB
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3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/01_scaling-variational-inference-unbiased-estimates.mp4
11.49MB
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2. competitive-data-science/10_advanced-feature-engineering-ii/01_advanced-features-ii/01_statistics-and-distance-based-features.mp4
11.6MB
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2. competitive-data-science/10_advanced-feature-engineering-ii/01_advanced-features-ii/04_t-sne.mp4
11.62MB
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2. competitive-data-science/01_introduction-recap/04_software-hardware-requirements/01_software-hardware-requirements.mp4
11.75MB
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3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/02_dirichlet-distribution.mp4
11.88MB
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1. intro-to-deep-learning/04_unsupervised-representation-learning/01_intro-to-unsupervised-learning/02_autoencoders-101.mp4
11.9MB
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2. competitive-data-science/11_ensembling/01_ensembling/02_bagging.mp4
11.94MB
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3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/02_expectation-maximization-algorithm/07_summary-of-expectation-maximization.mp4
12.1MB
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2. competitive-data-science/04_exploratory-data-analysis/02_eda-examples/03_numerai-competition-eda.mp4
12.21MB
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3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/06_log-derivative-trick.mp4
12.24MB
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2. competitive-data-science/06_data-leakages/01_data-leakages/01_basic-data-leaks.mp4
12.28MB
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3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/01_latent-variable-models/02_probabilistic-clustering.mp4
12.4MB
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2. competitive-data-science/04_exploratory-data-analysis/01_exploratory-data-analysis/03_building-intuition-about-the-data.mp4
12.7MB
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1. intro-to-deep-learning/04_unsupervised-representation-learning/01_intro-to-unsupervised-learning/01_unsupervised-learning-what-it-is-and-why-bother.mp4
12.74MB
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3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/03_applications-and-examples/05_em-for-probabilistic-pca.mp4
12.98MB
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2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/06_general-approaches-for-metrics-optimization.mp4
13.12MB
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13.18MB
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13.29MB
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1. intro-to-deep-learning/02_introduction-to-neural-networks/01_multilayer-perceptron-or-the-basic-principles-of-deep-learning/01_multilayer-perceptron.mp4
13.4MB
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2. competitive-data-science/04_exploratory-data-analysis/01_exploratory-data-analysis/02_exploratory-data-analysis.mp4
13.51MB
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13.55MB
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1. intro-to-deep-learning/01_introduction-to-optimization/04_stochastic-methods-for-optimization/02_gradient-descent-extensions.mp4
13.63MB
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1. intro-to-deep-learning/02_introduction-to-neural-networks/04_philosophy-of-deep-learning/02_deep-learning-as-a-language.mp4
13.66MB
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2. competitive-data-science/01_introduction-recap/02_competition-mechanics/01_competition-mechanics.mp4
13.69MB
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13.73MB
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13.76MB
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13.81MB
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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.mp4
13.95MB
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13.96MB
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14.08MB
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3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/02_gaussian-processes.mp4
14.14MB
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2. competitive-data-science/05_validation/01_validation/02_validation-strategies.mp4
14.19MB
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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
14.22MB
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1. intro-to-deep-learning/05_deep-learning-for-sequences/02_modern-rnns/01_the-training-of-rnns-is-not-that-easy.mp4
14.51MB
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3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/03_using-cnns-with-a-mixture-of-gaussians.mp4
14.61MB
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1. intro-to-deep-learning/04_unsupervised-representation-learning/04_generative-adversarial-networks/01_generative-models-101.mp4
14.62MB
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2. competitive-data-science/04_exploratory-data-analysis/01_exploratory-data-analysis/06_dataset-cleaning-and-other-things-to-check.mp4
14.7MB
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2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/08_classification-metrics-optimization-i.mp4
14.71MB
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3. bayesian-methods-in-machine-learning/05_variational-autoencoder/01_variational-autoencoders/07_reparameterization-trick.mp4
14.74MB
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1. intro-to-deep-learning/02_introduction-to-neural-networks/01_multilayer-perceptron-or-the-basic-principles-of-deep-learning/03_backpropagation-primer.mp4
14.96MB
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3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/03_applications-and-examples/03_k-means-m-step.mp4
15.26MB
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1. intro-to-deep-learning/04_unsupervised-representation-learning/02_more-autoencoders/02_autoencoder-applications-image-generation-data-visualization-more.mp4
15.33MB
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3. bayesian-methods-in-machine-learning/05_variational-autoencoder/02_variational-dropout/02_dropout-as-bayesian-procedure.mp4
15.47MB
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3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/05_example-of-gibbs-sampling.mp4
15.54MB
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2. competitive-data-science/08_advanced-feature-engineering-i/01_mean-encodings/02_regularization.mp4
15.63MB
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1. intro-to-deep-learning/02_introduction-to-neural-networks/02_tensorflow/03_gradients-optimization-in-tensorflow.mp4
15.65MB
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2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/02_motivation.mp4
15.76MB
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3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/09_markov-chain-monte-carlo-summary.mp4
15.83MB
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1. intro-to-deep-learning/05_deep-learning-for-sequences/01_introduction-to-rnn/01_motivation-for-recurrent-layers.mp4
15.99MB
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2. competitive-data-science/12_competitions-go-through/01_competitions-go-through/05_walmart-trip-type-classification.mp4
16.29MB
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1. intro-to-deep-learning/02_introduction-to-neural-networks/04_philosophy-of-deep-learning/01_what-deep-learning-is-and-is-not.mp4
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16.87MB
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1. intro-to-deep-learning/03_deep-learning-for-images/02_modern-cnns/02_overview-of-modern-cnn-architectures.mp4
17.7MB
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17.73MB
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18.32MB
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18.32MB
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1. intro-to-deep-learning/05_deep-learning-for-sequences/01_introduction-to-rnn/02_simple-rnn-and-backpropagation.mp4
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1. intro-to-deep-learning/02_introduction-to-neural-networks/03_keras/01_keras-introduction.mp4
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19.18MB
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19.49MB
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1. intro-to-deep-learning/04_unsupervised-representation-learning/04_generative-adversarial-networks/02_generative-adversarial-networks.mp4
19.79MB
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19.95MB
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20.14MB
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1. intro-to-deep-learning/04_unsupervised-representation-learning/03_word-embeddings/01_natural-language-processing-primer.mp4
20.21MB
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3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/07_metropolis-hastings-choosing-the-critic.mp4
20.27MB
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3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/08_example-of-metropolis-hastings.mp4
20.49MB
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1. intro-to-deep-learning/04_unsupervised-representation-learning/02_more-autoencoders/01_autoencoder-applications.mp4
20.53MB
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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
20.93MB
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3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/05_nuances-of-gp.mp4
21.26MB
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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
21.3MB
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3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/01_latent-variable-models/01_latent-variable-models.mp4
21.31MB
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2. competitive-data-science/11_ensembling/01_ensembling/05_stacknet.mp4
21.4MB
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2. competitive-data-science/11_ensembling/01_ensembling/03_boosting.mp4
21.56MB
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2. competitive-data-science/08_advanced-feature-engineering-i/01_mean-encodings/03_extensions-and-generalizations.mp4
21.68MB
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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
22.28MB
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1. intro-to-deep-learning/04_unsupervised-representation-learning/04_generative-adversarial-networks/03_applications-of-adversarial-approach.mp4
22.79MB
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1. intro-to-deep-learning/03_deep-learning-for-images/01_introduction-to-cnn/01_motivation-for-convolutional-layers.mp4
22.79MB
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1. intro-to-deep-learning/01_introduction-to-optimization/02_linear-model-as-the-simplest-neural-network/02_linear-classification.mp4
22.82MB
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3. bayesian-methods-in-machine-learning/02_expectation-maximization-algorithm/03_applications-and-examples/04_probabilistic-pca.mp4
23.12MB
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1. intro-to-deep-learning/03_deep-learning-for-images/01_introduction-to-cnn/02_our-first-cnn-architecture.mp4
23.28MB
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2. competitive-data-science/11_ensembling/01_ensembling/04_stacking.mp4
23.3MB
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2. competitive-data-science/09_hyperparameter-optimization/01_hyperparameter-tuning/03_hyperparameter-tuning-ii.mp4
23.79MB
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2. competitive-data-science/04_exploratory-data-analysis/01_exploratory-data-analysis/05_visualizations.mp4
23.89MB
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3. bayesian-methods-in-machine-learning/01_introduction-to-bayesian-methods-conjugate-priors/01_introduction-to-bayesian-methods/05_linear-regression.mp4
24.35MB
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24.55MB
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26.31MB
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3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/03_markov-chains.mp4
26.45MB
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1. intro-to-deep-learning/04_unsupervised-representation-learning/03_word-embeddings/02_word-embeddings.mp4
26.45MB
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2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/03_regression-metrics-review-i.mp4
26.45MB
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3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/02_sampling-from-1-d-distributions.mp4
26.59MB
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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
26.85MB
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3. bayesian-methods-in-machine-learning/04_markov-chain-monte-carlo/01_mcmc/10_mcmc-for-lda.mp4
27.63MB
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1. intro-to-deep-learning/05_deep-learning-for-sequences/03_applications-of-rnns/01_practical-use-cases-for-rnns.mp4
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29.32MB
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30.4MB
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3. bayesian-methods-in-machine-learning/06_gaussian-processes-bayesian-optimization/01_gaussian-processes-and-bayesian-optimization/04_derivation-of-main-formula.mp4
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1. intro-to-deep-learning/03_deep-learning-for-images/02_modern-cnns/01_training-tips-and-tricks-for-deep-cnns.mp4
31.33MB
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2. competitive-data-science/09_hyperparameter-optimization/02_tips-and-tricks/01_practical-guide.mp4
32.82MB
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3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/04_lda-e-step-theta.mp4
33.36MB
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3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/01_variational-inference/03_example-ising-model.mp4
33.5MB
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3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/01_variational-inference/02_mean-field-approximation.mp4
35.44MB
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2. competitive-data-science/12_competitions-go-through/01_competitions-go-through/04_microsoft-malware-classification-challenge.mp4
37.84MB
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39.49MB
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2. competitive-data-science/07_metrics-optimization/01_metrics-optimization/05_classification-metrics-review.mp4
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3. bayesian-methods-in-machine-learning/03_variational-inference-latent-dirichlet-allocation/02_latent-dirichlet-allocation/06_lda-m-step-prediction.mp4
40.57MB
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