Экзамены Cambridge English в СПбГУ 2017Aleksey KonovalenkovСдай Кембриджские экзамены в СПбГУ!
Экзамены Cambrdige English Language Assessment (KET, PET, FCE, CAE, CPE, BEC) теперь можно сдать в СПбГУ на самых выгодных условиях!
Spark + H20 = Machine Learning at scaleMateusz Dymczykݺߣs for a presentation I gave for the Machine Learning with Spark Tokyo meetup.
Introduction to Spark, H2O, SparklingWater and live demos of GBM and DL.
H20: A platform for big math DataWorks Summit/Hadoop SummitThis document provides an overview of machine learning and artificial intelligence presented by Arno Candel, Chief Architect at H2O.ai. It discusses the history and evolution of AI from early concepts in the 1950s to recent advances in deep learning. It also describes H2O.ai's platform for scalable machine learning and how it works, allowing users to easily build and deploy models on big data using APIs for R, Python, and other languages.
Sparkling Water 2.0 - Michal MalohlavaSri AmbatiMichal Malohlava from H2O.ai talks about the new features in Sparkling Water 2.0 and the future roadmap.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Fighting Cybercrime: A Joint Task Force of Real-Time Data and Human Analytics...Spark SummitCybercrime is big business. Gartner reports worldwide security spending at $80B, with annual losses totalling more than $1.2T (in 2015). Small to medium sized businesses now account for more than half of the attacks targeting enterprises today. The threat actors behind these attacks are continually shifting their techniques and toolkits to evade the security defenses that businesses commonly use. Thanks to the growing frequency and complexity of attacks, the task of identifying and mitigating security-related events has become increasingly difficult.
At eSentire, we use a combination of data and human analytics to identify, respond to and mitigate cyber threats in real-time. We capture all network traffic on our customers’ networks, hence ingesting a large amount of time-series data. We process the data as it is being streamed into our system to extract relevant threat insights and block attacks in real-time. Furthermore, we enable our cybersecurity analysts to perform in-depth investigations to: i) confirm attacks and ii) identify threats that analytical models miss. Having security experts in the loop provides feedback to our analytics engine, thereby improving the overall threat detection effectiveness.
So how exactly can you build an analytics pipeline to handle a large amount of time-series/event-driven data? How do you build the tools that allow people to query this data with the expectation of mission-critical response times?
In this presentation, William Callaghan will focus on the challenges faced and lessons learned in building a human-in-the loop cyber threat analytics pipeline. They will discuss the topic of analytics in cybersecurity and highlight the use of technologies such as Spark Streaming/SQL, Cassandra, Kafka and Alluxio in creating an analytics architecture with missions-critical response times.
Spark-Streaming-as-a-Service with Kafka and YARN: Spark Summit East talk by J...Spark SummitSince April 2016, Spark-as-a-service has been available to researchers in Sweden from the Swedish ICT SICS Data Center at www.hops.site. Researchers work in an entirely UI-driven environment on a platform built with only open-source software.
Spark applications can be either deployed as jobs (batch or streaming) or written and run directly from Apache Zeppelin. Spark applications are run within a project on a YARN cluster with the novel property that Spark applications are metered and charged to projects. Projects are also securely isolated from each other and include support for project-specific Kafka topics. That is, Kafka topics are protected from access by users that are not members of the project. In this talk we will discuss the challenges in building multi-tenant Spark streaming applications on YARN that are metered and easy-to-debug. We show how we use the ELK stack (Elasticsearch, Logstash, and Kibana) for logging and debugging running Spark streaming applications, how we use Graphana and Graphite for monitoring Spark streaming applications, and how users can debug and optimize terminated Spark Streaming jobs using Dr Elephant. We will also discuss the experiences of our users (over 120 users as of Sept 2016): how they manage their Kafka topics and quotas, patterns for how users share topics between projects, and our novel solutions for helping researchers debug and optimize Spark applications.
To conclude, we will also give an overview on our course ID2223 on Large Scale Learning and Deep Learning, in which 60 students designed and ran SparkML applications on the platform.
Using SparkR to Scale Data Science Applications in Production. Lessons from t...Spark SummitR is a hugely popular platform for Data Scientists to create analytic models in many different domains. But when these applications should move from the science lab to the production environment of large enterprises a new set of challenges arises. Independently of R, Spark has been very successful as a powerful general-purpose computing platform. With the introduction of SparkR an exciting new option to productionize Data Science applications has been made available. This talk will give insight into two real-life projects at major enterprises where Data Science applications in R have been migrated to SparkR.
• Dealing with platform challenges: R was not installed on the cluster. We show how to execute SparkR on a Yarn cluster with a dynamic deployment of R.
• Integrating Data Engineering and Data Science: we highlight the technical and cultural challenges that arise from closely integrating these two different areas.
• Separation of concerns: we describe how to disentangle ETL and data preparation from analytic computing and statistical methods.
• Scaling R with SparkR: we present what options SparkR offers to scale R applications and how we applied them to different areas such as time series forecasting and web analytics.
• Performance Improvements: we will show benchmarks for an R applications that took over 20 hours on a single server/single-threaded setup. With moderate effort we have been able to reduce that number to 15 minutes with SparkR. And we will show how we plan to further reduces this to less than a minute in the future.
• Mixing SparkR, SparkSQL and MLlib: we show how we combined the three different libraries to maximize efficiency.
• Summary and Outlook: we describe what we have learnt so far, what the biggest gaps currently are and what challenges we expect to solve in the short- to mid-term.
Sparking up Data Engineering: Spark Summit East talk by Rohan SharmaSpark SummitLearn about the Big Data Processing ecosystem at Netflix and how Apache Spark sits in this platform. I talk about typical data flows and data pipeline architectures that are used in Netflix and address how Spark is helping us gain efficiency in our processes. As a bonus – i’ll touch on some unconventional use-cases contrary to typical warehousing / analytics solutions that are being served by Apache Spark.
Apache Spark for Machine Learning with High Dimensional Labels: Spark Summit ...Spark SummitThis talk will cover the tools we used, the hurdles we faced and the work arounds we developed with the help from Databricks support in our attempt to build a custom machine learning model and use it to predict the TV ratings for different networks and demographics.
The Apache Spark machine learning and dataframe APIs make it incredibly easy to produce a machine learning pipeline to solve an archetypal supervised learning problem. In our applications at Cadent, we face a challenge with high dimensional labels and relatively low dimensional features; at first pass such a problem is all but intractable but thanks to a large number of historical records and the tools available in Apache Spark, we were able to construct a multi-stage model capable of forecasting with sufficient accuracy to drive the business application.
Over the course of our work we have come across many tools that made our lives easier, and others that forced work around. In this talk we will review our custom multi-stage methodology, review the challenges we faced and walk through the key steps that made our project successful.
Scaling Apache Spark MLlib to Billions of Parameters: Spark Summit East talk ...Spark SummitApache Spark MLlib provides scalable implementation of popular machine learning algorithms, which lets users train models from big dataset and iterate fast. The existing implementations assume that the number of parameters is small enough to fit in the memory of a single machine. However, many applications require solving problems with billions of parameters on a huge amount of data such as Ads CTR prediction and deep neural network. This requirement far exceeds the capacity of exisiting MLlib algorithms many of who use L-BFGS as the underlying solver. In order to fill this gap, we developed Vector-free L-BFGS for MLlib. It can solve optimization problems with billions of parameters in the Spark SQL framework where the training data are often generated. The algorithm scales very well and enables a variety of MLlib algorithms to handle a massive number of parameters over large datasets. In this talk, we will illustrate the power of Vector-free L-BFGS via logistic regression with real-world dataset and requirement. We will also discuss how this approach could be applied to other ML algorithms.
Building Real-Time BI Systems with Kafka, Spark, and Kudu: Spark Summit East ...Spark SummitOne of the key challenges in working with real-time and streaming data is that the data format for capturing data is not necessarily the optimal format for ad hoc analytic queries. For example, Avro is a convenient and popular serialization service that is great for initially bringing data into HDFS. Avro has native integration with Flume and other tools that make it a good choice for landing data in Hadoop. But columnar file formats, such as Parquet and ORC, are much better optimized for ad hoc queries that aggregate over large number of similar rows.
Scalable Data Science with SparkR: Spark Summit East talk by Felix CheungSpark SummitR is a very popular platform for Data Science. Apache Spark is a highly scalable data platform. How could we have the best of both worlds? How could a Data Scientist leverage the rich 9000+ packages on CRAN, and integrate Spark into their existing Data Science toolset?
In this talk we will walkthrough many examples how several new features in Apache Spark 2.x will enable this. We will also look at exciting changes in and coming next in Apache Spark 2.x releases.
Real-time Platform for Second Look Business Use Case Using Spark and Kafka: S...Spark SummitIn this talk we will introduce the business use case of how we create a real-time platform for our Second Look project using Spark and Kafka.
Second Look is a feature created by Capital One to detect and notify cardholders of these potential mistakes and unexpected charges. We bring them to the attention of the customers automatically through email alerts and push notifications to ensure customers can take timely action. The situations can be resolved through a conversation with the merchant, or a dispute on your charge directly to Capital One. We help to guide the user through this resolution path through our user experiences.
We use Spark extensively to build the infrastructure for this project. Before we use Spark and Kafka, the alerts were not sent in real-time and there were delays in days between when the customers transact and when customers receive the alerts. With the power of Spark and Kafka, we are able to send the alert in a more timely manner. We will share how we connect each parts together from data ingestion to processing, alert generation, and alert delivery. We will demonstrate how Spark plays critical role in the whole infrastructure.
What’s next? We will leverage more power of machine learning using Spark to generate various types of alerts.
Building a Real-Time Fraud Prevention Engine Using Open Source (Big Data) Sof...Spark SummitFraudsters attempt to pay for goods, flights, hotels – you name it – using stolen credit cards. This hurts both the trust of card holders and the business of vendors around the world. We built a Real-Time Fraud Prevention Engine using Open Source (Big Data) Software: Spark, Spark ML, H2O, Hive, Esper. In my talk I will highlight both the business and the technical challenges that we’ve faced and dealt with.
Ielts в «ЭКСПЕРТЕ»Expert KaliningradВ языковой школе "ЭКСПЕРТ" (г. Калининград) Вы можете не только сдать экзамен IELTS, но и пройти подготовку, которая поможет Вам успешно сдать экзамен и получить желаемый балл. http://www.expertlanguage.ru/exams/Ielts.php
Apache Spark for Machine Learning with High Dimensional Labels: Spark Summit ...Spark SummitThis talk will cover the tools we used, the hurdles we faced and the work arounds we developed with the help from Databricks support in our attempt to build a custom machine learning model and use it to predict the TV ratings for different networks and demographics.
The Apache Spark machine learning and dataframe APIs make it incredibly easy to produce a machine learning pipeline to solve an archetypal supervised learning problem. In our applications at Cadent, we face a challenge with high dimensional labels and relatively low dimensional features; at first pass such a problem is all but intractable but thanks to a large number of historical records and the tools available in Apache Spark, we were able to construct a multi-stage model capable of forecasting with sufficient accuracy to drive the business application.
Over the course of our work we have come across many tools that made our lives easier, and others that forced work around. In this talk we will review our custom multi-stage methodology, review the challenges we faced and walk through the key steps that made our project successful.
Scaling Apache Spark MLlib to Billions of Parameters: Spark Summit East talk ...Spark SummitApache Spark MLlib provides scalable implementation of popular machine learning algorithms, which lets users train models from big dataset and iterate fast. The existing implementations assume that the number of parameters is small enough to fit in the memory of a single machine. However, many applications require solving problems with billions of parameters on a huge amount of data such as Ads CTR prediction and deep neural network. This requirement far exceeds the capacity of exisiting MLlib algorithms many of who use L-BFGS as the underlying solver. In order to fill this gap, we developed Vector-free L-BFGS for MLlib. It can solve optimization problems with billions of parameters in the Spark SQL framework where the training data are often generated. The algorithm scales very well and enables a variety of MLlib algorithms to handle a massive number of parameters over large datasets. In this talk, we will illustrate the power of Vector-free L-BFGS via logistic regression with real-world dataset and requirement. We will also discuss how this approach could be applied to other ML algorithms.
Building Real-Time BI Systems with Kafka, Spark, and Kudu: Spark Summit East ...Spark SummitOne of the key challenges in working with real-time and streaming data is that the data format for capturing data is not necessarily the optimal format for ad hoc analytic queries. For example, Avro is a convenient and popular serialization service that is great for initially bringing data into HDFS. Avro has native integration with Flume and other tools that make it a good choice for landing data in Hadoop. But columnar file formats, such as Parquet and ORC, are much better optimized for ad hoc queries that aggregate over large number of similar rows.
Scalable Data Science with SparkR: Spark Summit East talk by Felix CheungSpark SummitR is a very popular platform for Data Science. Apache Spark is a highly scalable data platform. How could we have the best of both worlds? How could a Data Scientist leverage the rich 9000+ packages on CRAN, and integrate Spark into their existing Data Science toolset?
In this talk we will walkthrough many examples how several new features in Apache Spark 2.x will enable this. We will also look at exciting changes in and coming next in Apache Spark 2.x releases.
Real-time Platform for Second Look Business Use Case Using Spark and Kafka: S...Spark SummitIn this talk we will introduce the business use case of how we create a real-time platform for our Second Look project using Spark and Kafka.
Second Look is a feature created by Capital One to detect and notify cardholders of these potential mistakes and unexpected charges. We bring them to the attention of the customers automatically through email alerts and push notifications to ensure customers can take timely action. The situations can be resolved through a conversation with the merchant, or a dispute on your charge directly to Capital One. We help to guide the user through this resolution path through our user experiences.
We use Spark extensively to build the infrastructure for this project. Before we use Spark and Kafka, the alerts were not sent in real-time and there were delays in days between when the customers transact and when customers receive the alerts. With the power of Spark and Kafka, we are able to send the alert in a more timely manner. We will share how we connect each parts together from data ingestion to processing, alert generation, and alert delivery. We will demonstrate how Spark plays critical role in the whole infrastructure.
What’s next? We will leverage more power of machine learning using Spark to generate various types of alerts.
Building a Real-Time Fraud Prevention Engine Using Open Source (Big Data) Sof...Spark SummitFraudsters attempt to pay for goods, flights, hotels – you name it – using stolen credit cards. This hurts both the trust of card holders and the business of vendors around the world. We built a Real-Time Fraud Prevention Engine using Open Source (Big Data) Software: Spark, Spark ML, H2O, Hive, Esper. In my talk I will highlight both the business and the technical challenges that we’ve faced and dealt with.
Ielts в «ЭКСПЕРТЕ»Expert KaliningradВ языковой школе "ЭКСПЕРТ" (г. Калининград) Вы можете не только сдать экзамен IELTS, но и пройти подготовку, которая поможет Вам успешно сдать экзамен и получить желаемый балл. http://www.expertlanguage.ru/exams/Ielts.php
2. C 2016 г. СПбГУ является авторизованным центром Cambridge English
Language Assessment
spbu.ru2
2,700+
Авторизованных экзаменационных
центров в мире
30,000+
Сертифицированных экзаменаторов
Сеть экзаменационных центров Cambridge English
расположена в 130 странах мира
Центр языкового тестирования
СПбГУ проводит экзамены
Cambridge English для студентов
университета на специальных
условиях
3. Зачем сдавать экзамены Cambridge English?
spbu.ru3
Для студентов СПбГУ экзамен Cambridge English дает возможность
Получить положительный
результат по
входному/итоговому
тестированию в СПбГУ*
•Сертификаты Cambridge English Language
Assessment FCE, CAE, CPE, BEC Vantage, BEC
Higher, ILEC, ICFE признаются в качестве
положительного результата входного и
итогового тестирования по английскому
языку как подтверждающего уровень
владения обучающимся английским языком
не ниже уровня B2 Общеевропейской
шкалы иноязычной коммуникативной
компетенции в Санкт-Петербургском
государственном университете.
•Сертификаты экзаменов Cambridge English
действуют бессрочно.
Подать сертификат для целей
включенного обучения
•Сертификаты Cambridge English
признаются для подтверждения
языковых способностей, необходимых
для обучения в зарубежных вузах-
партнерах
Получить сертификат о
владении английским языком,
признаваемый во всем мире
•Сертификаты Cambridge English
признаются 20,000 ведущими вузами,
работодателями и государственными
органами во всем мире
•Сертификат может быть использован при
поступлении на англоязычные
образовательные программы
(бакалавриат, магистратура, PhD)
•Великобритания: 100% вузов
•Германия: >120 вузов
•Франция: >100 вузов
•Италия: >100 вузов
•Финляндия: 100% вузов
•Чехия: >15 вузов
*Приказ первого проректора по учебной и научной
работе от 08.10.2013 №3534/1 «О признании
результатов международных экзаменов»
4. Структура и уровни экзаменов Cambridge English
spbu.ru4
Экзамен Cambridge English тестирует 4
вида речевой деятельности
Чтение и
грамматика
(Reading &
Use of English)
1 час 15 минут (First)
1 час 30 минут (Advanced)
Письмо
(Writing)
1 час 20 минут (First)
1 час 30 минут (Advanced)
Аудирование
(Listening)
40 минут
Говорение
(Speaking)
14-15 минут
5. Сколько стоит экзамен Cambridge English в СПбГУ?
spbu.ru5
СПбГУ
Другие
центры
тестирования
Студенты СПбГУ имеют уникальную
возможность сдать экзамены
Cambridge English на специальных
условиях
6. Как записаться на экзамены Cambridge English в СПбГУ?
spbu.ru6
Экзамен Cambridge English в СПбГУ проводится в два дня.
10 декабря 2016 г. сдаются чтение и грамматика, письмо и
аудирование.
11 декабря 2016 г. сдается говорение.
Совокупное время проведения экзамена Cambridge English – около
3,5 часов.
Экзамен проводится по адресу: наб. Лейтенанта Шмидта, д.11/2,
Центр языкового тестирования СПбГУ
Для прохождения экзамена Cambridge English в СПбГУ необходимо
направить на адрес test.language@spbu.ru письмо со следующими
данными (на английском языке):
• имя, фамилия
• пол
• телефон
• email
• дата рождения
• номер паспорта
Или возможно заполнить онлайн-форму на сайте СПбГУ в разделе
Центр языкового тестирования – Cambridge English Language
Assessment:
http://spbu.ru/science/expert/lang-centre/27312-cambridge-english-
language-assessment.html
Для кандидатов, оплативших экзамен до 31.10.2016 г., проводится бесплатная консультация по
подготовке к экзамену в формате Cambridge English
1
2
3
7. Преимущества экзаменов Cambridge English для студентов СПбГУ
spbu.ru7
Выгодно для студента
Проводится в
СПбГУ
Самая низкая
в России цена
Междуна-
родный
сертификат
Возможность сдачи экзаменов
Cambridge English
Независимое научно обоснованное подтверждение
уровня владения английским языком в соответствии
с CEFR
Получение международного сертификата Cambridge
English, признаваемого по всему миру
Возможность использования одного сертификата
для целей включенного обучения и поступления в
англоязычную магистратуру