Data Science from Scratch, 2nd Edition

Index

A

• A/B tests, Example: Running an A/B Test
• accuracy, Correctness
• activation functions, Other Activation Functions
• AllenNLP, For Further Exploration
• Altair library, For Further Exploration
• Anaconda Python distribution, Getting Python
• args, args and kwargs
• argument unpacking, zip and Argument Unpacking
• arithmetic operations, Vectors
• arrays, Lists
• artificial neural networks, Neural Networks
• assert statements, Automated Testing and assert
• automated testing, Automated Testing and assert
• average (mean), Central Tendencies

B

• backpropagation, Backpropagation
• bagging, Random Forests
• bar charts, Bar Charts-Line Charts
• batch gradient descent, Minibatch and Stochastic Gradient Descent
• Bayesian inference, Bayesian Inference
• Bayes’s theorem, Bayes’s Theorem
• Beautiful Soup library, HTML and the Parsing Thereof
• bell-shaped curve, The Normal Distribution
• BernoulliNB model, For Further Exploration
• Beta distributions, Bayesian Inference
• betweenness centrality, Betweenness Centrality-Betweenness Centrality
• bias input, Feed-Forward Neural Networks
• bias-variance tradeoff, The Bias-Variance Tradeoff
• biased data, Biased Data
• bigram models, n-Gram Language Models
• binary judgments, Correctness
• Binomial distributions, Bayesian Inference
• binomial random variables, The Central Limit Theorem
• Bokeh library, For Further Exploration, Visualization
• Booleans, Truthiness
• bootstrap aggregating, Random Forests
• bootstrapping, Digression: The Bootstrap
• bottom-up hierarchical clustering, Bottom-Up Hierarchical Clustering-Bottom-Up Hierarchical Clustering ...