Machine learning and big data with kdb+/q [online] / Jan Novotny, Paul A. Bilokon, Aris Galiotos, Frédéric Déléze.

Index

Include referințe bibliografice.

PART One : Language Fundamentals Fundamentals of the q Programming Language Dictionaries and Tables: The q Fundamentals Functions Editors and Other Tools Debugging q Code

PART Two : Data Operations Splayed and Partitioned Tables Joins Parallelisation Data Cleaning and Filtering Parse Trees A Few Use Cases

PART Three : Data Science Basic Overview of Statistics Linear Regression Time Series Econometrics Fourier Transform Eigensystem and PCA Outlier Detection Simulating Asset Prices

PART Four : Machine Learning PART Four : Machine Learning Linear Regression with Regularisation Nearest Neighbours Neural Networks AdaBoost with Stumps Trees Forests Unsupervised Machine Learning: The Apriori Algorithm Processing Information Towards AI – Monte Carlo Tree Search Econophysics: The Agent‐Based Computational Models Epilogue: Art

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