Book picks similar to
Machine Learning: Fundamental Algorithms for Supervised and Unsupervised Learning With Real-World Applications by Joshua Chapmann
machine-learning
technology
technology-and-science
_check-this-out
Installing Linux on a Dead Badger
Lucy A. Snyder - 2007
This collection of thirteen short stories, articles and essays from Lucy A. Snyder will appeal to any fan of zombies, aliens or installation manuals. Here's what Wikipedia said about Lucy, last time we checked: "Lucy A. Snyder is an American science fiction, fantasy, humor, and nonfiction writer. She grew up in San Angelo, Texas but moved to Bloomington, Indiana for graduate studies at Indiana University and currently lives in Columbus, Ohio with her husband Gary A. Braunbeck. Snyder served as an editor for HMS Beagle, an online bioscience publication produced by Elsevier. She has also contributed technical articles to publications such as Electronic Products."
The Dream Machine: J.C.R. Licklider and the Revolution That Made Computing Personal
M. Mitchell Waldrop - 2001
C. R. Licklider, whose visionary dream of a human-computer symbiosis transformed the course of modern science and led to the development of the personal computer. Reprint.
Paradigms of Artificial Intelligence Programming: Case Studies in Common LISP
Peter Norvig - 1991
By reconstructing authentic, complex AI programs using state-of-the-art Common Lisp, the book teaches students and professionals how to build and debug robust practical programs, while demonstrating superior programming style and important AI concepts. The author strongly emphasizes the practical performance issues involved in writing real working programs of significant size. Chapters on troubleshooting and efficiency are included, along with a discussion of the fundamentals of object-oriented programming and a description of the main CLOS functions. This volume is an excellent text for a course on AI programming, a useful supplement for general AI courses and an indispensable reference for the professional programmer.
Numsense! Data Science for the Layman: No Math Added
Annalyn Ng - 2017
Sold in over 85 countries and translated into more than 5 languages.---------------Want to get started on data science?Our promise: no math added.This book has been written in layman's terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, we stick to intuitive explanations and visuals.Popular concepts covered include:- A/B Testing- Anomaly Detection- Association Rules- Clustering- Decision Trees and Random Forests- Regression Analysis- Social Network Analysis- Neural NetworksFeatures:- Intuitive explanations and visuals- Real-world applications to illustrate each algorithm- Point summaries at the end of each chapter- Reference sheets comparing the pros and cons of algorithms- Glossary list of commonly-used termsWith this book, we hope to give you a practical understanding of data science, so that you, too, can leverage its strengths in making better decisions.
Understanding Air France 447
Bill Palmer - 2013
Written by A330 Captain, Bill Palmer, this book opens to understanding the actions of the crew, how they failed to understand and control the problem, and how the airplane works and the part it played. All in easy to understand terms.Addressed are the many contributing aspects of weather, human factors, and airplane system operation and design that the crew could not recover from. How each contributed is covered in detail along with what has been done, and needs to be done in the future to prevent this from happening again.Also see the book's companion website: UnderstandingAF447.com for supplemental materials referred to in the book or to contact the author.