Coursera Unsupervised Learning Recommenders Reinforcement Learning

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1.12 GB | 00:33:18 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English

Files Included :
01 welcome (8.3 MB)
01 what-is-clustering (8.39 MB)
02 k-means-intuition (11.92 MB)
03 k-means-algorithm (18.5 MB)
04 optimization-objective (29.24 MB)
05 initializing-k-means (17.13 MB)
06 choosing-the-number-of-clusters (16.19 MB)
01 finding-unusual-events (25.09 MB)
02 gaussian-normal-distribution (20.08 MB)
03 anomaly-detection-algorithm (18.84 MB)
04 developing-and-evaluating-an-anomaly-detection-system (22.63 MB)
05 anomaly-detection-vs-supervised-learning (19.39 MB)
06 choosing-what-features-to-use (29.79 MB)
01 making-recommendations (20.25 MB)
02 using-per-item-features (21.62 MB)
03 collaborative-filtering-algorithm (29.03 MB)
04 binary-labels-favs-likes-and-clicks (19.08 MB)
01 mean-normalization (17.82 MB)
02 tensorflow-implementation-of-collaborative-filtering (34.31 MB)
03 finding-related-items (16.23 MB)
01 collaborative-filtering-vs-content-based-filtering (18.83 MB)
02 deep-learning-for-content-based-filtering (23.82 MB)
03 recommending-from-a-large-catalogue (17.02 MB)
04 ethical-use-of-recommender-systems (23.84 MB)
05 tensorflow-implementation-of-content-based-filtering (12 MB)
01 reducing-the-number-of-features-optional (27.05 MB)
02 pca-algorithm-optional (28.56 MB)
03 pca-in-code-optional (18.23 MB)
01 what-is-reinforcement-learning (31.4 MB)
02 mars-rover-example (12.47 MB)
03 the-return-in-reinforcement-learning (29.24 MB)
04 making-decisions-policies-in-reinforcement-learning (5.86 MB)
05 review-of-key-concepts (11.32 MB)
01 state-action-value-function-definition (19.22 MB)
02 state-action-value-function-example (14.74 MB)
03 bellman-equation (25.61 MB)
04 random-stochastic-environment-optional (19.5 MB)
01 example-of-continuous-state-space-applications (27.47 MB)
02 lunar-lander (10.34 MB)
03 learning-the-state-value-function (29.97 MB)
04 algorithm-refinement-improved-neural-network-architecture (7.66 MB)
05 algorithm-refinement-greedy-policy (25.22 MB)
06 algorithm-refinement-mini-batch-and-soft-updates-optional (25.19 MB)
07 the-state-of-reinforcement-learning (7.99 MB)
01 summary-and-thank-you (14.15 MB)
01 andrew-ng-and-chelsea-finn-on-ai-and-robotics (252.7 MB)

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