Thoughtful Machine Learning with Python

Index

A

• activation functions, Neurons-Activation Functions
• Adaboost, Boosting
• Adzic, Gojko, Writing the Right Software
• Agile Manifesto, Testing or TDD
• algorithms summary, What Can Machine Learning Accomplish?-What Can Machine Learning Accomplish?, Machine Learning Algorithms Revisited-Machine Learning Algorithms Revisited
• AmazonFresh, Writing the Right Software
• artificial neural networks, History of Neural Nets

B

• back propagation algorithm, Training Algorithms, The Delta Rule
• bagging, Bagging-Bagging, Bagging
• Bain, Alexander, History of Neural Nets
• Bayes' theorem, Inverse Conditional Probability (aka Bayes’ Theorem)
  • (see also Naive Bayesian Classification)
• BeautifulSoup, EmailObject
• Beck, Kent, Testing or TDD
• Boolean logic, Boolean Logic
• boosting, Boosting-Boosting
• bootstrap aggregation (see bagging)
• Brown Corpus, Part-of-Speech Tagging with the Brown Corpus-How to Make This Model Better
  • (see also part-of-speech tagging with the Brown Corpus)

C

• chain rule, The Chain Rule
• Changing Anything Changes Everything (CACE), SRP
• clustering, Clustering-Conclusion, How to Use This Information to Solve Problems
  • consistency in, The Impossibility Theorem
  • data gathering, Gathering the Data
  • EM algorithm, EM Clustering-Maximization, EM Clustering Our Data-The Results from the EM Jazz Clustering
  • example with music categorization, Example: Categorizing Music-The Results from the EM Jazz Clustering
  • fitness functions, Fitness of a Cluster
  • ground truth testing, Comparing Results to Ground Truth
  • and the impossibility theorem, The Impossibility ...