Visualizing machine learning algorithms in the Jupyter notebook
summary
Shows how to create visual, interactive explanations of machine learning algorithms inside Jupyter notebooks, using notebook-friendly tools and libraries to illustrate algorithm behavior and decisions. Focuses on concrete visualization techniques and implementations that run within the Jupyter environment.
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chapters (16)
- 0:00Start
- 0:45OUTLINE
- 1:30INTERACTIVE WIDGETS
- 3:11GAUSSIAN DENSITY
- 5:03LOCAL REGRESSION
- 9:24TWITTER SENTIMENT ANALYSIS
- 9:45METHODOLOGY
- 10:14ONE-VS-REST LOGISTIC REGRESSION
- 11:30CLASSIFIER PERFORMANCE ANALYSIS
- 12:39ANALYZE MISCLASSIFICATIONS
- 14:25USE LASSO TO FIND DATA ISSUES
- 15:43NEURAL NETWORK WIZARD
- 18:10NETWORK PARAMETERS
- 19:06LOSS AND ACCURACY CURVES
- 20:08DISTRIBUTIONS OF WEIGHTS/BIASESIACTIVATIONS
- 20:53DIAGNOSTIC PLOTS
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