Sukh osukh

Nov 6, 20120 likes182 views

The document discusses the benefits of exercise for both physical and mental health. It notes that regular exercise can reduce the risk of diseases like heart disease and diabetes, improve mood, and reduce feelings of stress and anxiety. Exercise is also said to boost brain health and function as we age by increasing blood flow and oxygen to the brain.

This document provides a behind-the-scenes tour of Google from both a user's and computer scientist's perspective. It discusses Google's hardware design philosophy of using many low-cost commodity servers to achieve high performance and reliability through parallelism. It also summarizes Google's core distributed systems like GFS, MapReduce, Bigtable, and how they are used to process large datasets and provide services across many Google products and clusters containing thousands of machines.

Software Engineering Advice from Google's Jeff Dean for Big, Distributed Systemsadrianionel

This document provides software engineering advice from Jeff Dean based on his experience building large-scale distributed systems at Google. Some key points include: - Design systems to be simple, scalable, performant, reliable, and general while balancing different goals. Get advice on designs before coding. - Distributed systems require careful data partitioning and high-capacity even within datacenters. Products are deployed across multiple datacenters worldwide. - Real hardware is unreliable so systems must be designed to handle many types of failures gracefully. - Prioritize low latency, consider data access patterns and encoding, use caching and parallelism where possible. - Favor eventual consistency over strong consistency for availability. - Add monitoring, debugging hooks,

Mit big data_sep2012Ratul Ray

The document discusses challenges and opportunities related to "Big Data" and Google's infrastructure. It describes how Google's data is distributed across many data centers worldwide and decomposed into various services. It also discusses issues like hardware failures, network variability, and the benefits and difficulties of shared computing environments at Google's massive scale. Techniques for improving reliability like replication and latency tolerance are presented, such as tying requests together to send to multiple servers to improve performance.

GOOGLE: Designs, Lessons and Advice from Building Large Distributed Systems xlight

Scala introductionvito jeng

This document provides an introduction to Scala. It discusses: - Who the author is and their background with Scala and Spark - Why Scala is a scalable language that runs on the JVM and supports object oriented and functional programming - How to install Scala and use the Scala interpreter - Basic Scala syntax like defining values and variables, type inference, strings, tuples, objects, importing classes - Common functions and operations like map, reduce, anonymous functions, pattern matching - Code samples for RDD relations and SparkPi - Tips for using Scala in practice including SBT and good IDEs like IntelliJ

Track A-2 基於 Spark 的數據分析Etu Solution

Spark is a general purpose computational framework that provides more flexibility than MapReduce. It leverages distributed memory and uses directed acyclic graphs for data parallel computations while retaining MapReduce properties like scalability, fault tolerance, and data locality. Cloudera has embraced Spark and is working to integrate it into their Hadoop ecosystem through projects like Hive on Spark and optimizations in Spark Core, MLlib, and Spark Streaming. Cloudera positions Spark as the future general purpose framework for Hadoop, while other specialized frameworks may still be needed for tasks like SQL, search, and graphs.

Large Scale Deep Learning with TensorFlow Jen Aman

Large-scale deep learning with TensorFlow allows storing and performing computation on large datasets to develop computer systems that can understand data. Deep learning models like neural networks are loosely based on what is known about the brain and become more powerful with more data, larger models, and more computation. At Google, deep learning is being applied across many products and areas, from speech recognition to image understanding to machine translation. TensorFlow provides an open-source software library for machine learning that has been widely adopted both internally at Google and externally.

Java onguice20070426Ratul Ray

The document discusses Google Guice, an open source framework for Java that supports dependency injection. It provides examples of how to use Guice to inject dependencies into classes through field injection, method injection, and constructor injection. It also covers how to define modules to configure dependency bindings and scopes like singletons. The document is intended to introduce developers to the basics of using Guice for dependency injection in Java applications.

Ruptan banglaeboi.comRatul Ray

Onno jiboner shaadRatul Ray

Moner manush-sunil-gangopadhayRatul Ray

The document discusses the challenges faced by farmers in India. It notes that farmers struggle with low crop yields and prices, as well as debt. While the government has programs to support farmers, issues remain such as lack of access to technology, markets, credit, and risk management tools. Overall, farmers experience hardship despite playing a vital role in the country. Solutions are needed to better support the agricultural sector and improve conditions for farmers.