This is a one slide presentation for the Haskell for Minsk conference that provides a link to view the full presentation on Google Presentations. The presentation discusses the Haskell for Minsk conference which is an event focused on the Haskell programming language and community in Minsk, Belarus.
The document discusses the present and future of metaprogramming in Scala. It describes scala.reflect, the current metaprogramming API, and its limitations. The future scala.meta API aims to have an independent language model, use hygienic quasiquotes for syntax trees, and make compiler syntax trees persistent. The new API design is being validated and will likely be available as a compiler plugin next year.
This is a one slide presentation for the Haskell for Minsk conference that provides a link to view the full presentation on Google Presentations. The presentation discusses the Haskell for Minsk conference which is an event focused on the Haskell programming language and community in Minsk, Belarus.
The document discusses the present and future of metaprogramming in Scala. It describes scala.reflect, the current metaprogramming API, and its limitations. The future scala.meta API aims to have an independent language model, use hygienic quasiquotes for syntax trees, and make compiler syntax trees persistent. The new API design is being validated and will likely be available as a compiler plugin next year.
This document discusses a function-passing programming model for distributed systems. The key concepts are:
1. Immutable, stationary data stored in "silos".
2. Portable "spore" functions that are serialized and sent to remote silos to perform work on the data.
Silos contain the data and run functions on it. Silo references allow applying functions asynchronously across machines by serializing the functions and data references. The model aims to simplify distributed programming by keeping data stationary and moving functions to the data.
Trillhaas Goetz. Innovations in Google and Global Digital TrendsVolha Banadyseva
Goetz Trillhaas discussed Google's 8 principles of innovation which focus on hiring great people, sharing ideas and information openly, having a clear shared vision while adapting, iterating products quickly, relying on data to drive decisions, and putting users first rather than money. He provided examples of Google's innovations like Google Search, Google Now, Project Loon for balloon-powered internet access, driverless cars, Google Glass, and Android Wear as well as their goal of giving users control.
Сергей Сверчков и Виталий Руденя. Choosing a NoSQL databaseVolha Banadyseva
This document discusses choosing a NoSQL database for a project. It evaluated Couchbase, MongoDB, Riak, Cassandra, and MySQL based on performance benchmarks across various workloads and stability during node failures. Couchbase and Cassandra demonstrated the best performance and stability in most tests. MongoDB performance was impacted by availability of configuration servers. Riak and MySQL NDB Cluster were more complex to configure for geographic replication between data centers.
- Logistic regression is a classic machine learning algorithm used for classification tasks like predicting customer churn or click behavior. However, training logistic regression on large datasets ("big data") using the traditional batch approach is very slow.
- Online learning is an alternative approach that trains logistic regression on one data point at a time, allowing for faster real-time updates. Popular libraries for online learning include Sofia-ml, Vowpal Wabbit, and scikit-learn which can train models on data in batches.
- Expedia uses logistic regression for tasks like predicting hotel bookings and detecting credit card fraud, where billions of predictions are made daily. Online learning allows training these models faster to keep up with this scale
1. Adform has been researching technologies for Big Data analytics since 2012 as their data volumes grew to 1.4 TB per day.
2. They tested several technologies over 3 months each including IBM Netezza, HP Vertica, SAP Sybase IQ, Amazon Redshift, and Calpont InfiniDB to handle their needs for real-time queries, unlimited scaling, high performance, and no downtime.
3. While each technology had strengths, no single solution met all their requirements for queries, load speed, compression and more. Adform concluded the best approach is to adopt a technology based on your unique needs rather than forcing your needs to fit a technology.
This document discusses a function-passing programming model for distributed systems. The key concepts are:
1. Immutable, stationary data stored in "silos".
2. Portable "spore" functions that are serialized and sent to remote silos to perform work on the data.
Silos contain the data and run functions on it. Silo references allow applying functions asynchronously across machines by serializing the functions and data references. The model aims to simplify distributed programming by keeping data stationary and moving functions to the data.
Trillhaas Goetz. Innovations in Google and Global Digital TrendsVolha Banadyseva
Goetz Trillhaas discussed Google's 8 principles of innovation which focus on hiring great people, sharing ideas and information openly, having a clear shared vision while adapting, iterating products quickly, relying on data to drive decisions, and putting users first rather than money. He provided examples of Google's innovations like Google Search, Google Now, Project Loon for balloon-powered internet access, driverless cars, Google Glass, and Android Wear as well as their goal of giving users control.
Сергей Сверчков и Виталий Руденя. Choosing a NoSQL databaseVolha Banadyseva
This document discusses choosing a NoSQL database for a project. It evaluated Couchbase, MongoDB, Riak, Cassandra, and MySQL based on performance benchmarks across various workloads and stability during node failures. Couchbase and Cassandra demonstrated the best performance and stability in most tests. MongoDB performance was impacted by availability of configuration servers. Riak and MySQL NDB Cluster were more complex to configure for geographic replication between data centers.
- Logistic regression is a classic machine learning algorithm used for classification tasks like predicting customer churn or click behavior. However, training logistic regression on large datasets ("big data") using the traditional batch approach is very slow.
- Online learning is an alternative approach that trains logistic regression on one data point at a time, allowing for faster real-time updates. Popular libraries for online learning include Sofia-ml, Vowpal Wabbit, and scikit-learn which can train models on data in batches.
- Expedia uses logistic regression for tasks like predicting hotel bookings and detecting credit card fraud, where billions of predictions are made daily. Online learning allows training these models faster to keep up with this scale
1. Adform has been researching technologies for Big Data analytics since 2012 as their data volumes grew to 1.4 TB per day.
2. They tested several technologies over 3 months each including IBM Netezza, HP Vertica, SAP Sybase IQ, Amazon Redshift, and Calpont InfiniDB to handle their needs for real-time queries, unlimited scaling, high performance, and no downtime.
3. While each technology had strengths, no single solution met all their requirements for queries, load speed, compression and more. Adform concluded the best approach is to adopt a technology based on your unique needs rather than forcing your needs to fit a technology.
8. Применяемые способы
1. Консультирование «on-site»
2. Консультирование «on-line»
- по телефону
- в сети с задержкой времени
- в сети в режиме реального времени
3. Информационно-рекламная продукция
9.
10. Возможности образования
• Создание информационной среды (в том
числе обеспечение системности и полноты)
• Выработка практических навыков
• Доступ к информации заблаговременно
• Предоставление информации
«опционально», дополнительно к другим
программам
• Более эффективное преодоление
психологических и социальных
несоответствий