After being snowed under for much of February and March, we've finally got rid of the last of the snow and there's new buds all over the plants and trees. Maybe we'll get a 'normal' spring this year. That's normal as in 'usual', not as in 'Normal Distribution', but I guess you probably figured that out...
Now that the clocks have changed and the nights are getting lighter, there might be just a little more light left in the evenings to read by without turning on the lights, so let's curl up with a good book, get the brain cells going and learn some new data skills.
I hope the 3 free books in this blog post prove to be a valuable resource to you and that you will visit regularly (and share with your friends in social media too).
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This month we highlight 3 books:
- Theory and Applications for Advanced Text Mining
- Modeling with Data
- Learning Deep Architectures of AI
They're all FREE, so help yourselves...
Disclosure: the three books highlighted here do not have affiliate links.
However, links to other resources on this page may be affiliate links, and we may earn an affiliate commission for purchases you make when using these links.
by Shigeaki Sakurai
Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge.
Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields
This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language.
I believe that this book will give new knowledge in the text mining field and help many readers open their new research fields.
by Ben Klemens
Modeling with Data fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results.
Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures.
He then demonstrates how to easily apply these tools to the many threads of statistical technique, including classical, Bayesian, maximum likelihood, and Monte Carlo methods.
Klemens's accessible survey describes these models in a unified and non-traditional manner, providing alternative ways of looking at statistical concepts that often befuddle students. The book includes nearly one hundred sample programs of all kinds.
by Yoshua Bengio
Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g., in vision, language, and other AI-level tasks), one may need deep architectures.
Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae.
Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas.
This monograph discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.