Coursera - Advanced Learning Algorithms

1.39 GB | 00:47:27 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English

Files Included :
01 welcome (10.93 MB)
02 neurons-and-the-brain (26.62 MB)
03 demand-prediction (24.6 MB)
04 example-recognizing-images (13.67 MB)
01 neural-network-layer (19.1 MB)
02 more-complex-neural-networks (15.9 MB)
03 inference-making-predictions-forward-propagation (11.81 MB)
01 inference-in-code (16.26 MB)
02 data-in-tensorflow (23.74 MB)
03 building-a-neural-network (23.44 MB)
01 forward-prop-in-a-single-layer (11.88 MB)
02 general-implementation-of-forward-propagation (20.72 MB)
01 is-there-a-path-to-agi (27.5 MB)
01 how-neural-networks-are-implemented-efficiently (12.53 MB)
02 matrix-multiplication (15.11 MB)
03 matrix-multiplication-rules (16.51 MB)
04 matrix-multiplication-code (12.78 MB)
01 tensorflow-implementation (11.27 MB)
02 training-details (22.4 MB)
01 alternatives-to-the-sigmoid-activation (11.5 MB)
02 choosing-activation-functions (23.17 MB)
03 why-do-we-need-activation-functions (12.39 MB)
01 multiclass (8.45 MB)
02 softmax (19.14 MB)
03 neural-network-with-softmax-output (13.91 MB)
04 improved-implementation-of-softmax (15.2 MB)
05 classification-with-multiple-outputs-optional (11.72 MB)
01 advanced-optimization (15.24 MB)
02 additional-layer-types (20.32 MB)
01 what-is-a-derivative-optional (39.22 MB)
02 computation-graph-optional (31.27 MB)
03 larger-neural-network-example-optional (26.68 MB)
01 deciding-what-to-try-next (11.81 MB)
02 evaluating-a-model (19.62 MB)
03 model-selection-and-training-cross-validation-test-sets (27.27 MB)
01 diagnosing-bias-and-variance (20.54 MB)
02 regularization-and-bias-variance (21.46 MB)
03 establishing-a-baseline-level-of-performance (19.73 MB)
04 learning-curves (23.83 MB)
05 deciding-what-to-try-next-revisited (27.14 MB)
06 bias-variance-and-neural-networks (25.86 MB)
01 iterative-loop-of-ml-development (13.79 MB)
02 error-analysis (16.39 MB)
03 adding-data (31.05 MB)
04 transfer-learning-using-data-from-a-different-task (19.32 MB)
05 full-cycle-of-a-machine-learning-project (15.44 MB)
06 fairness-bias-and-ethics (24.97 MB)
01 error-metrics-for-skewed-datasets (19.15 MB)
02 trading-off-precision-and-recall (22.56 MB)
01 decision-tree-model (13.86 MB)
02 learning-process (28.06 MB)
01 measuring-purity (16.27 MB)
02 choosing-a-split-information-gain (21.51 MB)
03 putting-it-together (17.34 MB)
04 using-one-hot-encoding-of-categorical-features (13.49 MB)
05 continuous-valued-features (15.01 MB)
06 regression-trees-optional (17.7 MB)
01 using-multiple-decision-trees (12.05 MB)
02 sampling-with-replacement (14.1 MB)
03 random-forest-algorithm (12.95 MB)
04 xgboost (20.49 MB)
05 when-to-use-decision-trees (16.88 MB)
01 andrew-ng-and-chris-manning-on-natural-language-processing (260.16 MB)
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