My, doesn't time fly when you're having fun?
It might only be October, and we've still got Halloween, Bonfire Night and Thanksgiving to get through, but since I brought you the last lot of data science books a month ago, the shops have filled up with Christmas stuff and all the stores smells like, well, like Christmas!
And here's me thinking we've got lots of time to do the shopping. Apparently not...
Anyway, it's time for another set of 3 FREE data science books to tickle your data fancy.
I hope these hit the spot and whet your appetite for another month.
This month we highlight 3 books:
- Understanding the Chief Data Officer
- Getting Started with TensorFlow
- KB Neural Data Mining with Python Sources
They're all FREE, so help yourselves...
To manage today's flood of available data, a number of high-profile corporations have adopted a new position in addition to existing CTOs and CIOs: the Chief Data Officer, or CDO. In this report, Julie Steele of Silicon Valley Data Science provides a clear, concise look at how CDOs view their nascent role in high-profile organizations such as Wells Fargo, Samsung, the Republican National Committee, Allstate, and the Federal Reserve Board.
Although there are as many CDO implementations as there are organizations that employ them, some distinct patterns have emerged. This report presents a picture of the current landscape, as well as some guidelines and best practices for those considering adding a CDO role to their own company.
Google's TensorFlow engine is a robust, user-friendly, and customizable software library of machine learning code for deep learning and neural networks.
Start off by understanding the fundamentals of Go, followed by a detailed description of the Go data types, program structures and Maps. Learn how to use Go concurrency idioms to avoid pitfalls and create programs that are exact in expected behavior. Get to grips with the tools and libraries that are available in Go for writing and exercising tests, benchmarking, and code coverage.
Install and adopt TensorFlow in your Python environment to solve mathematical problems, get to know basic machine and deep learning concepts, and more.
The aim of this work is to present and describe in detail the algorithms to extract the knowledge hidden inside data using Python language, which allows us to read and easily understand the nature and the characteristics of the rules of the computing utilized, as opposed to what happens in commercial applications, which are available only in the form of running codes, which remain impossible to modify. The algorithms of computing contained within the work, are minutely described, documented and available in the Python source format, and serve to extract the hidden knowledge within the data whether they are textual or numerical kinds. There are also various examples of usage, underlining the characteristics, method of execution and providing comments on the obtained results.
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