GraalVM を普通の Java VM として使う ～クラウドベンチマークなどでの比較～Shinji Takao
クラウドや多言語の環境に対応できる 新しいJava実行環境 GraalVM は、ネイティブビルドだけでなく、通常の Java VM として使うこともできます。
このたび、クラウド環境用ベンチマーク「BluePerf」などを使い、GraalVM と OpenJDK を比較したので、結果を報告します。
Japan Java User Group (JJUG) Cross Community Conference (CCC) 2021 Fall 発表資料
https://www.youtube.com/watch?v=5MtjfQfdC_g
Deep Learning for Data Scientists - Data Science ATL Meetup Presentation, 201...Andrew Gardner
Note: these are the slides from a presentation at Lexis Nexis in Alpharetta, GA, on 2014-01-08 as part of the DataScienceATL Meetup. A video of this talk from Dec 2013 is available on vimeo at http://bit.ly/1aJ6xlt
Note: Slideshare mis-converted the images in slides 16-17. Expect a fix in the next couple of days.
---
Deep learning is a hot area of machine learning named one of the "Breakthrough Technologies of 2013" by MIT Technology Review. The basic ideas extend neural network research from past decades and incorporate new discoveries in statistical machine learning and neuroscience. The results are new learning architectures and algorithms that promise disruptive advances in automatic feature engineering, pattern discovery, data modeling and artificial intelligence. Empirical results from real world applications and benchmarking routinely demonstrate state-of-the-art performance across diverse problems including: speech recognition, object detection, image understanding and machine translation. The technology is employed commercially today, notably in many popular Google products such as Street View, Google+ Image Search and Android Voice Recognition.
In this talk, we will present an overview of deep learning for data scientists: what it is, how it works, what it can do, and why it is important. We will review several real world applications and discuss some of the key hurdles to mainstream adoption. We will conclude by discussing our experiences implementing and running deep learning experiments on our own hardware data science appliance.
GraalVM を普通の Java VM として使う ～クラウドベンチマークなどでの比較～Shinji Takao
クラウドや多言語の環境に対応できる 新しいJava実行環境 GraalVM は、ネイティブビルドだけでなく、通常の Java VM として使うこともできます。
このたび、クラウド環境用ベンチマーク「BluePerf」などを使い、GraalVM と OpenJDK を比較したので、結果を報告します。
Japan Java User Group (JJUG) Cross Community Conference (CCC) 2021 Fall 発表資料
https://www.youtube.com/watch?v=5MtjfQfdC_g
Deep Learning for Data Scientists - Data Science ATL Meetup Presentation, 201...Andrew Gardner
Note: these are the slides from a presentation at Lexis Nexis in Alpharetta, GA, on 2014-01-08 as part of the DataScienceATL Meetup. A video of this talk from Dec 2013 is available on vimeo at http://bit.ly/1aJ6xlt
Note: Slideshare mis-converted the images in slides 16-17. Expect a fix in the next couple of days.
---
Deep learning is a hot area of machine learning named one of the "Breakthrough Technologies of 2013" by MIT Technology Review. The basic ideas extend neural network research from past decades and incorporate new discoveries in statistical machine learning and neuroscience. The results are new learning architectures and algorithms that promise disruptive advances in automatic feature engineering, pattern discovery, data modeling and artificial intelligence. Empirical results from real world applications and benchmarking routinely demonstrate state-of-the-art performance across diverse problems including: speech recognition, object detection, image understanding and machine translation. The technology is employed commercially today, notably in many popular Google products such as Street View, Google+ Image Search and Android Voice Recognition.
In this talk, we will present an overview of deep learning for data scientists: what it is, how it works, what it can do, and why it is important. We will review several real world applications and discuss some of the key hurdles to mainstream adoption. We will conclude by discussing our experiences implementing and running deep learning experiments on our own hardware data science appliance.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help boost feelings of calmness, happiness and focus.
Jeff Dean at AI Frontiers: Trends and Developments in Deep Learning ResearchAI Frontiers
In this talk at AI Frontiers conference, Jeff Dean discusses recent trends and developments in deep learning research. Jeff touches on the significant progress that this research has produced in a number of areas, including computer vision, language understanding, translation, healthcare, and robotics. These advances are driven by both new algorithmic approaches to some of these problems, and by the ability to scale computation for training ever large models on larger datasets. Finally, one of the reasons for the rapid spread of the ideas and techniques of deep learning has been the availability of open source libraries such as TensorFlow. He gives an overview of why these software libraries have an important role in making the benefits of machine learning available throughout the world.
Transform your Business with AI, Deep Learning and Machine LearningSri Ambati
Video: https://www.youtube.com/watch?v=R3IXd1iwqjc
Meetup: http://www.meetup.com/SF-Bay-ACM/events/231709894/
In this talk, Arno Candel presents a brief history of AI and how Deep Learning and Machine Learning techniques are transforming our everyday lives. Arno will introduce H2O, a scalable open-source machine learning platform, and show live demos on how to train sophisticated machine learning models on large distributed datasets. He will show how data scientists and application developers can use the Flow GUI, R, Python, Java, Scala, JavaScript and JSON to build smarter applications, and how to take them to production. He will present customer use cases from verticals including insurance, fraud, churn, fintech, and marketing.
- 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
Presto is an interactive SQL query engine for big data that was originally developed at Facebook in 2012 and open sourced in 2013. It is 10x faster than Hive for interactive queries on large datasets. Presto is highly extensible, supports pluggable backends, ANSI SQL, and complex queries. It uses an in-memory parallel processing architecture with pipelined task execution, data locality, caching, JIT compilation, and SQL optimizations to achieve high performance on large datasets.
Presto is a distributed SQL query engine that allows for interactive analysis of large datasets across various data sources. It was created at Facebook to enable interactive querying of data in HDFS and Hive, which were too slow for interactive use. Presto addresses problems with existing solutions like Hive being too slow, the need to copy data for analysis, and high costs of commercial databases. It uses a distributed architecture with coordinators planning queries and workers executing tasks quickly in parallel.
JavaOne 2013: Effective Foreign Function Interfaces: From JNI to JNRRyan Sciampacone
Effective Foreign Function Interfaces: From JNI to JNR
JavaOne 2013 CON4767
Ryan A. Sciampacone, Senior Software Developer, IBM JTC
What do you do when your application needs access to platform features that aren’t available in the Java platform? You need a foreign function interface (FFI). The Java Native Interface (JNI) is the classic power tool for calling native code from your Java program. Using JNI means stepping out of the managed safety of the JVM into the wilds of native code. This session explains the most common JNI performance and correctness pitfalls and explains how to find and avoid them. As the JVM becomes the runtime of choice for more languages, the FFI landscape is also evolving. This session introduces alternative FFI approaches that minimizes effort (SWIG) and native code. It examines JNR in detail and shows how alternatives perform relative to handwritten JNI.
This document discusses the HiveServer2 project which aims to improve Hive by adding support for sessions, concurrency, ODBC/JDBC, authentication and authorization. It notes limitations in the current Thrift API and outlines milestones for the project including specifying a new Thrift API, adding driver support, fixing memory leaks, and extending authentication and authorization capabilities.
Lukasz Kaiser at AI Frontiers: How Deep Learning Quietly Revolutionized NLPAI Frontiers
While deep learning is very popular, it might not be well know how profoundly it has changed natural language processing (NLP). Lukasz gives an overview of the challenges unique to NLP that made it hard for neural networks, say how they were overcome, and how the new end-to-end deep learning methods managed to significantly improve over state-of-the-art in multiple NLP tasks, such as machine translation, parsing, and summarization.
Fisl15 Streaming de vídeo ao vivo na globo.comLeandro Moreira
O documento resume 10 lições aprendidas ao transmitir vídeos ao vivo na globo.com. A principal lição é que o protocolo HLS é muito melhor do que o RTMP para transmissão de vídeo, resultando em menos falhas de reprodução, melhor qualidade de vídeo e mais tempo assistido. Outra lição é a importância crucial de medição e métricas para otimizar o desempenho. Por fim, o documento defende iniciativas de código aberto para tornar o software mais genérico e atrair contribuições da comunidade.
HiveServer2 was reconstructed and reimplemented to address limitations in the original HiveServer1 such as lack of concurrency, incomplete security implementations, and instability. HiveServer2 uses a multithreaded architecture where each client connection creates a new execution context including a session and operations. This allows HiveServer2 to associate a Hive execution context like the session and Driver with the thread serving each client request. The new Thrift interface in HiveServer2 also enables better support for common database features around authentication, authorization, and auditing compared to the original Thrift API in HiveServer1.
This document provides guidance on building a global video platform for Telefonica to help them become a video company. It emphasizes the importance of not missing the target audience and ensuring the platform is ready for peak video traffic. It discusses strategies like centralized monitoring, automated testing, and understanding bottlenecks to prepare for high user loads. The document also provides an example using a soccer game as an analogy for video traffic spikes and recommends having indexes and queries optimized to avoid full table scans when traffic increases.
Nikko Ström at AI Frontiers: Deep Learning in AlexaAI Frontiers
Alexa is the service that understands spoken language in Amazon Echo and other voice enabled devices. Alexa relies heavily on machine learning and deep neural networks for speech recognition, text-to-speech, language understanding, skill selection, and more. In this talk Nikko presents an overview of deep learning in Alexa and gives a few illustrating examples.
This short document discusses getting the process ID (PID) in Panama. It mentions including the unistd.h header file and linking with the unistd.jar library to use the getpid function, as well as potentially needing the libc.so shared object for it to work properly.
The document repeatedly discusses the concept of "Japanthink" but provides no further context or details about what Japanthink refers to. It consists solely of the word "Japanthink" printed multiple times.
Project Jigsaw introduces a modular system to Java to address issues with non-modular code. It divides the Java platform API into named modules that clearly define dependencies and exported packages. This avoids issues with classes from sun.* and com.sun.* packages being inaccessible or the huge size of rt.jar. The module system allows modular and non-modular code to coexist through automatic modules and the unnamed module. Tools like jdeps and jlink are provided to help with adoption and management of modules.
El documento describe un proyecto llamado Valhalla que consiste en una lista de puntos con coordenadas x e y. La lista contiene cabeceras y puntos con valores numéricos para x e y.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
