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Learning Cloudera Impala
Learning Cloudera Impala

Perform interactive, real-time in-memory analytics on large amounts of data using the massive parallel processing engine Cloudera Impala

Overview

  • Step-by-step guidance to get you started with Impala on your Hadoop cluster
  • Manipulate your data rapidly by writing proper SQL statements
  • ...
Android Game Recipes: A Problem-Solution Approach
Android Game Recipes: A Problem-Solution Approach

Android game apps are typically the most popular type of Android apps in the various Google Play, Amazon Appstore and other Android app stores. So, beyond the Android game tutorials out there, what about a day-to-day handy and complete code reference for Android game developers?

Android Game Recipes is your first, reliable...

Survival Analysis Using SAS: A Practical Guide, Second Edition
Survival Analysis Using SAS: A Practical Guide, Second Edition

Easy to read and comprehensive, Survival Analysis Using SAS: A Practical Guide, Second Edition, by Paul D. Allison, is an accessible, data-based introduction to methods of survival analysis. Researchers who want to analyze survival data with SAS will find just what they need with this fully updated new edition that incorporates the many...

Visualforce Development Cookbook
Visualforce Development Cookbook

Over 75 recipes to help you create powerful custom pages, simplify data-entry, and enrich the Salesforce user interface

Overview

  • Provide an enhanced user experience with dynamically generated, reactive pages
  • Access data over additional channels via public web sites and mobile pages
  • ...
Circuit Complexity and Neural Networks (Foundations of Computing)
Circuit Complexity and Neural Networks (Foundations of Computing)

Neural networks usually work adequately on small problems but can run into trouble when they are scaled up to problems involving large amounts of input data. Circuit Complexity and Neural Networks addresses the important question of how well neural networks scale - that is, how fast the computation time and number of neurons grow as the...

Haskell Financial Data Modeling and Predictive Analytics
Haskell Financial Data Modeling and Predictive Analytics

Haskell is one of the three most influential functional programming languages available today along with Lisp and Standard ML. When used for financial analysis, you can achieve a much-improved level of prediction and clear problem descriptions.

Haskell Financial Data Modeling and Predictive Analytics is a hands-on guide that...

Building Interactive Queries with LINQPad
Building Interactive Queries with LINQPad

If you need to interact with databases, XML, in-memory collections, or remote services, LINQ can make your life simpler. The best way to discover LINQ is with the help of LINQPad, a free IDE whose first goal is to make sure that writing and interacting with your LINQ query is fun and easy. More generally, LINQPad is a C#/VB/F# scratchpad that...

Meta-Algorithmics: Patterns for Robust, Low Cost, High Quality Systems
Meta-Algorithmics: Patterns for Robust, Low Cost, High Quality Systems

The confluence of cloud computing, parallelism and advanced machine intelligence approaches has created a world in which the optimum knowledge system will usually be architected from the combination of two or more knowledge-generating systems. There is a need, then, to provide a reusable, broadly-applicable set of design patterns to empower...

Cost Optimal and Nearly Zero-Energy Buildings (nZEB): Definitions, Calculation Principles and Case Studies
Cost Optimal and Nearly Zero-Energy Buildings (nZEB): Definitions, Calculation Principles and Case Studies

Cost optimal and nearly zero energy performance levels are principles initiated by the European Union’s (EU) Energy Performance of Buildings Directive which was recast in 2010. These will be major drivers in the construction sector in the next few years, because all new buildings in the EU from 2021 onwards are expected to be nearly...

Synthesizable VHDL Design for FPGAs
Synthesizable VHDL Design for FPGAs

The methodology described in this book is the result of many years of research experience in the field of synthesizable VHDL design targeting FPGA based platforms. VHDL was first conceived as a documentation language for ASIC designs. Afterwards, the language was used for the behavioral simulation of ASICs, and also as a design input for...

Neural Networks for Pattern Recognition (Bradford Books)
Neural Networks for Pattern Recognition (Bradford Books)

Neural Networks for Pattern Recognition takes the pioneering work in artificial neural networks by Stephen Grossberg and his colleagues to a new level. In a simple and accessible way it extends embedding field theory into areas of machine intelligence that have not been clearly dealt with before. Following a tutorial of existing neural...

Approximation Methods for Polynomial Optimization: Models, Algorithms, and Applications (SpringerBriefs in Optimization)
Approximation Methods for Polynomial Optimization: Models, Algorithms, and Applications (SpringerBriefs in Optimization)
Polynomial optimization, as its name suggests, is used to optimize a generic multivariate polynomial function, subject to some suitable polynomial equality and/or inequality constraints. Such problem formulation dates back to the nineteenth century when the relationship between nonnegative polynomials and sum of squares (SOS) was...
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