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| 01_acknowledgements_instructions.html |
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| 01_acknowledgments_instructions.html |
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| 01_acknowledgments_instructions.html |
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| 01_advanced-optimization.en.srt |
10.54Кб |
| 01_advanced-optimization.en.txt |
5.56Кб |
| 01_advanced-optimization.mp4 |
15.57Мб |
| 01_alternatives-to-the-sigmoid-activation.en.srt |
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| 01_alternatives-to-the-sigmoid-activation.en.txt |
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| 01_alternatives-to-the-sigmoid-activation.mp4 |
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| 01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.en.srt |
50.57Кб |
| 01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.en.txt |
31.74Кб |
| 01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.mp4 |
230.66Мб |
| 01_andrew-ng-and-chris-manning-on-natural-language-processing.en.srt |
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| 01_andrew-ng-and-chris-manning-on-natural-language-processing.en.txt |
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| 01_andrew-ng-and-chris-manning-on-natural-language-processing.mp4 |
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| 01_andrew-ng-and-fei-fei-li-on-human-centered-ai.en.srt |
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| 01_andrew-ng-and-fei-fei-li-on-human-centered-ai.en.txt |
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| 01_andrew-ng-and-fei-fei-li-on-human-centered-ai.mp4 |
214.72Мб |
| 01_collaborative-filtering-vs-content-based-filtering.en.srt |
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| 01_collaborative-filtering-vs-content-based-filtering.en.txt |
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| 01_collaborative-filtering-vs-content-based-filtering.mp4 |
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| 01_cost-function-for-logistic-regression.en.srt |
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| 01_cost-function-for-logistic-regression.en.txt |
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| 01_cost-function-for-logistic-regression.mp4 |
24.61Мб |
| 01_deciding-what-to-try-next.en.srt |
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| 01_deciding-what-to-try-next.en.txt |
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| 01_deciding-what-to-try-next.mp4 |
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| 01_decision-tree-model.en.srt |
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| 01_decision-tree-model.en.txt |
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| 01_decision-tree-model.mp4 |
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| 01_diagnosing-bias-and-variance.en.srt |
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| 01_diagnosing-bias-and-variance.en.txt |
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| 01_diagnosing-bias-and-variance.mp4 |
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| 01_error-metrics-for-skewed-datasets.en.srt |
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| 01_error-metrics-for-skewed-datasets.en.txt |
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| 01_error-metrics-for-skewed-datasets.mp4 |
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| 01_example-of-continuous-state-space-applications.en.srt |
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| 01_example-of-continuous-state-space-applications.en.txt |
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| 01_example-of-continuous-state-space-applications.mp4 |
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| 01_feature-scaling-part-1.en.srt |
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| 01_feature-scaling-part-1.en.txt |
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| 01_feature-scaling-part-1.mp4 |
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| 01_finding-unusual-events.en.srt |
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| 01_finding-unusual-events.en.txt |
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| 01_finding-unusual-events.mp4 |
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| 01_forward-prop-in-a-single-layer.en.srt |
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| 01_forward-prop-in-a-single-layer.en.txt |
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| 01_forward-prop-in-a-single-layer.mp4 |
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| 01_gradient-descent.en.srt |
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| 01_gradient-descent.en.txt |
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| 01_gradient-descent.mp4 |
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| 01_gradient-descent-implementation.en.srt |
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| 01_gradient-descent-implementation.en.txt |
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| 01_gradient-descent-implementation.mp4 |
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| 01_how-neural-networks-are-implemented-efficiently.en.srt |
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| 01_how-neural-networks-are-implemented-efficiently.en.txt |
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| 01_how-neural-networks-are-implemented-efficiently.mp4 |
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| 01_inference-in-code.en.srt |
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| 01_inference-in-code.en.txt |
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| 01_inference-in-code.mp4 |
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| 01_is-there-a-path-to-agi.en.srt |
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| 01_is-there-a-path-to-agi.en.txt |
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| 01_is-there-a-path-to-agi.mp4 |
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| 01_iterative-loop-of-ml-development.en.srt |
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| 01_iterative-loop-of-ml-development.en.txt |
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| 01_iterative-loop-of-ml-development.mp4 |
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| 01_linear-regression-model-part-1.en.srt |
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| 01_linear-regression-model-part-1.en.txt |
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| 01_linear-regression-model-part-1.mp4 |
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| 01_making-recommendations.en.srt |
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| 01_making-recommendations.en.txt |
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| 01_making-recommendations.mp4 |
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| 01_mean-normalization.en.srt |
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| 01_mean-normalization.en.txt |
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| 01_mean-normalization.mp4 |
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| 01_measuring-purity.en.srt |
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| 01_motivations.en.srt |
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| 01_multiclass.en.srt |
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| 01_multiclass.mp4 |
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| 01_multiple-features.en.srt |
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| 01_multiple-features.mp4 |
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| 01_neural-network-layer.en.srt |
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| 01_neural-network-layer.mp4 |
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| 01_practice-quiz-classification-with-logistic-regression_exam.html |
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| 01_practice-quiz-cost-function-for-logistic-regression_exam.html |
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| 01_practice-quiz-gradient-descent-for-logistic-regression_exam.html |
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| 01_practice-quiz-gradient-descent-in-practice_exam.html |
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| 01_practice-quiz-multiple-linear-regression_exam.html |
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| 01_practice-quiz-regression_exam.html |
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| 01_practice-quiz-supervised-vs-unsupervised-learning_exam.html |
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| 01_practice-quiz-the-problem-of-overfitting_exam.html |
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| 01_practice-quiz-train-the-model-with-gradient-descent_exam.html |
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| 01_reducing-the-number-of-features-optional.en.srt |
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| 01_reducing-the-number-of-features-optional.en.txt |
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| 01_reducing-the-number-of-features-optional.mp4 |
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| 01_state-action-value-function-definition.en.srt |
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| 01_state-action-value-function-definition.en.txt |
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| 01_state-action-value-function-definition.mp4 |
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| 01_summary-and-thank-you.en.srt |
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| 01_summary-and-thank-you.mp4 |
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| 01_tensorflow-implementation.en.srt |
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| 01_tensorflow-implementation.en.txt |
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| 01_tensorflow-implementation.mp4 |
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| 01_the-problem-of-overfitting.en.srt |
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| 01_the-problem-of-overfitting.en.txt |
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| 01_the-problem-of-overfitting.mp4 |
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| 01_using-multiple-decision-trees.en.srt |
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| 01_using-multiple-decision-trees.en.txt |
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| 01_using-multiple-decision-trees.mp4 |
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| 01_week-2-practice-lab-linear-regression_instructions.html |
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| 01_week-3-practice-lab-logistic-regression_instructions.html |
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| 01_welcome.en.srt |
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| 01_welcome.en.txt |
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| 01_welcome.mp4 |
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| 01_welcome-to-machine-learning.en.srt |
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| 01_welcome-to-machine-learning.en.txt |
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| 01_welcome-to-machine-learning.mp4 |
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| 01_what-is-a-derivative-optional.en.srt |
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| 01_what-is-a-derivative-optional.en.txt |
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| 01_what-is-a-derivative-optional.mp4 |
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| 01_what-is-clustering.en.srt |
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| 01_what-is-clustering.en.txt |
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| 01_what-is-clustering.mp4 |
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| 01_what-is-machine-learning.en.srt |
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| 01_what-is-machine-learning.en.txt |
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| 01_what-is-machine-learning.mp4 |
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| 01_what-is-reinforcement-learning.en.srt |
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| 01_what-is-reinforcement-learning.en.txt |
8.00Кб |
| 01_what-is-reinforcement-learning.mp4 |
30.97Мб |
| 02_additional-layer-types.en.srt |
11.31Кб |
| 02_additional-layer-types.en.txt |
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| 02_additional-layer-types.mp4 |
19.53Мб |
| 02_addressing-overfitting.en.srt |
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| 02_addressing-overfitting.en.txt |
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| 02_addressing-overfitting.mp4 |
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| 02_applications-of-machine-learning.en.srt |
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| 02_applications-of-machine-learning.en.txt |
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| 02_applications-of-machine-learning.mp4 |
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| 02_choosing-activation-functions.en.srt |
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| 02_choosing-activation-functions.mp4 |
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| 02_choosing-a-split-information-gain.en.srt |
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| 02_choosing-a-split-information-gain.en.txt |
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| 02_choosing-a-split-information-gain.mp4 |
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| 02_computation-graph-optional.en.srt |
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| 02_computation-graph-optional.mp4 |
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| 02_data-in-tensorflow.en.srt |
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| 02_data-in-tensorflow.mp4 |
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| 02_deep-learning-for-content-based-filtering.en.srt |
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| 02_deep-learning-for-content-based-filtering.en.txt |
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| 02_deep-learning-for-content-based-filtering.mp4 |
24.34Мб |
| 02_error-analysis.en.srt |
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| 02_error-analysis.en.txt |
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| 02_error-analysis.mp4 |
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| 02_evaluating-a-model.en.srt |
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| 02_evaluating-a-model.mp4 |
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| 02_feature-scaling-part-2.en.srt |
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| 02_feature-scaling-part-2.mp4 |
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| 02_gaussian-normal-distribution.en.srt |
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7.86Кб |
| 02_gaussian-normal-distribution.mp4 |
20.88Мб |
| 02_general-implementation-of-forward-propagation.en.srt |
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| 02_general-implementation-of-forward-propagation.mp4 |
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| 02_implementing-gradient-descent.en.srt |
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| 02_k-means-intuition.en.srt |
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| 02_k-means-intuition.mp4 |
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| 02_learning-process.en.srt |
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| 02_linear-regression-model-part-2.mp4 |
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| 02_logistic-regression.en.srt |
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| 02_logistic-regression.mp4 |
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| 02_lunar-lander.en.srt |
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| 02_mars-rover-example.en.srt |
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| 02_matrix-multiplication.en.srt |
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| 02_neurons-and-the-brain.en.srt |
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| 02_optional-opportunity-to-mentor-other-learners_instructions.html |
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| 02_regularization-and-bias-variance.en.srt |
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| 02_sampling-with-replacement.en.srt |
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| 02_simplified-cost-function-for-logistic-regression.en.srt |
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| 02_softmax.en.srt |
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| 02_supervised-learning-part-1.en.srt |
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| 02_tensorflow-implementation-of-collaborative-filtering.en.srt |
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| 02_trading-off-precision-and-recall.en.srt |
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| 02_training-details.en.srt |
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| 03_adding-data.en.srt |
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| 03_anomaly-detection-algorithm.en.srt |
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| 03_bellman-equation.en.srt |
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| 03_building-a-neural-network.en.srt |
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| 03_checking-gradient-descent-for-convergence.en.srt |
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| 03_checking-gradient-descent-for-convergence.mp4 |
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| 03_collaborative-filtering-algorithm.en.srt |
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| 03_collaborative-filtering-algorithm.mp4 |
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| 03_cost-function-formula.en.srt |
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| 03_decision-boundary.en.srt |
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| 03_demand-prediction.en.srt |
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| 03_recommending-from-a-large-catalogue.en.srt |
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| 03_supervised-learning-part-2.en.srt |
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| 03_the-return-in-reinforcement-learning.en.srt |
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| 03_the-return-in-reinforcement-learning.en.txt |
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| 03_the-return-in-reinforcement-learning.mp4 |
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| 03_vectorization-part-2.en.srt |
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| 03_why-do-we-need-activation-functions.en.srt |
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| 04_algorithm-refinement-improved-neural-network-architecture.en.srt |
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| 04_gradient-descent-for-multiple-linear-regression.en.srt |
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| 04_optimization-objective.en.srt |
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| 04_regularized-linear-regression.en.srt |
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| 04_transfer-learning-using-data-from-a-different-task.en.srt |
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| 04_using-one-hot-encoding-of-categorical-features.en.srt |
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| 05_unsupervised-learning-part-2.en.srt |
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| 06_running-gradient-descent.en.srt |
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| Bonus Resources.txt |
386б |
| Get Bonus Downloads Here.url |
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