Ruby On Rails Performance Tuning Guide.pdf

This document discusses concept learning, which involves inferring a Boolean-valued function from training examples of its input and output. It describes a concept learning task where each hypothesis is a vector of six constraints specifying values for six attributes. The most general and most specific hypotheses are provided. It also discusses the FIND-S algorithm for finding a maximally specific hypothesis consistent with positive examples, and its limitations in dealing with noise or multiple consistent hypotheses. Finally, it introduces the candidate-elimination algorithm and version spaces as an improvement over FIND-S that can represent all consistent hypotheses.

This Edureka Pig Tutorial ( Pig Tutorial Blog Series: https://goo.gl/KPE94k ) will help you understand the concepts of Apache Pig in depth. Check our complete Hadoop playlist here: https://goo.gl/ExJdZs Below are the topics covered in this Pig Tutorial: 1) Entry of Apache Pig 2) Pig vs MapReduce 3) Twitter Case Study on Apache Pig 4) Apache Pig Architecture 5) Pig Components 6) Pig Data Model 7) Running Pig Commands and Pig Scripts (Log Analysis)

This document discusses user defined functions (UDFs) in Apache Pig. It provides examples of different types of UDFs including EvalFunc, FilterFunc, and LoadFunc. For EvalFunc, it shows how to write a simple function to uppercase text and how to return complex types. For FilterFunc, it demonstrates an IsEmpty function. For LoadFunc, it outlines the key interfaces and methods needed to implement a custom loader using a regular expression example.

The document describes the backpropagation algorithm for training multilayer neural networks. It discusses how backpropagation uses gradient descent to minimize error between network outputs and targets by calculating error gradients with respect to weights. The algorithm iterates over examples, calculates error, computes gradients to update weights. Momentum can be added to help escape local minima. Backpropagation can learn representations in hidden layers and is prone to overfitting without validation data to select the best model.

Apache Pig is a high-level data flow platform for executing MapReduce programs on Hadoop. The language used for Pig is called Pig Latin. Pig scripts get converted into MapReduce jobs that are executed on data stored in HDFS. Pig can handle structured, semi-structured, or unstructured data and store results back in HDFS. Common Pig operations include joining, sorting, filtering, grouping, and using built-in and user-defined functions.

** PySpark Certification Training: https://www.edureka.co/pyspark-certification-training** This Edureka tutorial on PySpark Tutorial will provide you with a detailed and comprehensive knowledge of Pyspark, how it works, the reason why python works best with Apache Spark. You will also learn about RDDs, data frames and mllib.

The document discusses the Rabin-Karp algorithm for string matching. It defines Rabin-Karp as a string search algorithm that compares hash values of strings rather than the strings themselves. It explains that Rabin-Karp works by calculating a hash value for the pattern and text subsequences to compare, and only does a brute force comparison when hash values match. The worst-case complexity is O(n-m+1)m but the average case is O(n+m) plus processing spurious hits. Real-life applications include bioinformatics to find protein similarities.

Mapreduce examples starting from the basic WordCount to a more complex K-means algorithm. The code contained in these slides is available at https://github.com/andreaiacono/MapReduce

Hadoop is the popular open source like Facebook, Twitter, RFID readers, sensors, and implementation of MapReduce, a powerful tool so on.Your management wants to derive designed for deep analysis and transformation of information from both the relational data and thevery large data sets. Hadoop enables you to unstructuredexplore complex data, using custom analyses data, and wants this information as soon astailored to your information and questions. possible.Hadoop is the system that allows unstructured What should you do? Hadoop may be the answer!data to be distributed across hundreds or Hadoop is an open source project of the Apachethousands of machines forming shared nothing Foundation.clusters, and the execution of Map/Reduce It is a framework written in Java originallyroutines to run on the data in that cluster. Hadoop developed by Doug Cutting who named it after hishas its own filesystem which replicates data to sons toy elephant.multiple nodes to ensure if one node holding data Hadoop uses Google’s MapReduce and Google Filegoes down, there are at least 2 other nodes from System technologies as its foundation.which to retrieve that piece of information. This It is optimized to handle massive quantities of dataprotects the data availability from node failure, which could be structured, unstructured orsomething which is critical when there are many semi-structured, using commodity hardware, thatnodes in a cluster (aka RAID at a server level). is, relatively inexpensive computers. This massive parallel processing is done with greatWhat is Hadoop? performance. However, it is a batch operation handling massive quantities of data, so theThe data are stored in a relational database in your response time is not immediate.desktop computer and this desktop computer As of Hadoop version 0.20.2, updates are nothas no problem handling this load. possible, but appends will be possible starting inThen your company starts growing very quickly, version 0.21.and that data grows to 10GB. Hadoop replicates its data across differentAnd then 100GB. computers, so that if one goes down, the data areAnd you start to reach the limits of your current processed on one of the replicated computers.desktop computer. Hadoop is not suitable for OnLine Transaction So you scale-up by investing in a larger computer, Processing workloads where data are randomly and you are then OK for a few more months. accessed on structured data like a relational When your data grows to 10TB, and then 100TB. database.Hadoop is not suitable for OnLineAnd you are fast approaching the limits of that Analytical Processing or Decision Support Systemcomputer. workloads where data are sequentially accessed onMoreover, you are now asked to feed your structured data like a relational database, to application with unstructured data coming from generate reports that provide business sources intelligence. Hadoop is used for Big Data. It complements OnLine Transaction Processing and OnLine Analytical Pro

This PPt gives Information about: 1. Getting timestamp with time() 2. Converting TimeStamp with getdate() 3. PHP Date Function 4. PHP Time Function

This document provides an introduction to Apache Spark, including its architecture and programming model. Spark is a cluster computing framework that provides fast, in-memory processing of large datasets across multiple cores and nodes. It improves upon Hadoop MapReduce by allowing iterative algorithms and interactive querying of datasets through its use of resilient distributed datasets (RDDs) that can be cached in memory. RDDs act as immutable distributed collections that can be manipulated using transformations and actions to implement parallel operations.

This Edureka Big Data Analytics Tutorial will help you to understand the basics of Big Data domain. Learn how to analyze Big Data in this tutorial. Below are the topics covered in this tutorial: 1) Big Data Introduction 2) What is Big Data Analytics? 3) Why Big Data Analytics? 4) Stages in Big Data Analytics 5) Big Data Analytics Domains 6) Big Data Analytics Use Cases Subscribe to our channel to get updates. Check our complete Hadoop playlist here: https://goo.gl/4OyoTW

This chapter discusses limitations on algorithmic power and methods for establishing lower bounds on algorithms. It introduces lower bounds as estimates of the minimum amount of work needed to solve a problem. Several methods are presented for establishing lower bounds, including trivial lower bounds based on input/output sizes, decision trees to model comparisons, adversary arguments, and reducing one problem to another with a known lower bound. Examples are given for sorting, searching, and matrix multiplication.

Algorithm Analysis, Table method to calculate time complexity, Big-O Notation, Omega Notation, theta notation, Heap types, Operations on Heap

The document summarizes Apache Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes the key components of Hadoop including the Hadoop Distributed File System (HDFS) which stores data reliably across commodity hardware, and the MapReduce programming model which allows distributed processing of large datasets in parallel. The document provides an overview of HDFS architecture, data flow, fault tolerance, and other aspects to enable reliable storage and access of very large files across clusters.