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RBM (Restricted Boltzmann Machine) with DL4J (Deep Learning for Java) , code for mathematics and physics
RBM with DL4J for Deep Learning
RBM with DL4J for Deep Learning
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Applications of Recurrent Neural Networks on timeseries data in the Enterprise with DL4J
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Building Production Class Deep Learning Workflows for the Enterprise
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Building Production Class Deep Learning Workflows for the Enterprise
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Enterprise Deep Learning with DL4J - Hadoop Summit 2015 presentation by Josh Patterson
Deep learning with DL4J - Hadoop Summit 2015
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Introduction to deep learning and DL4J - http://deeplearning4j.org/ - a guest lecture by Josh Patterson at Georgia Tech for the cse6242 graduate class.
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Deep Learning Intro - Georgia Tech - CSE6242 - March 2015
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RBM (Restricted Boltzmann Machine) with DL4J (Deep Learning for Java) , code for mathematics and physics
RBM with DL4J for Deep Learning
RBM with DL4J for Deep Learning
신동 강
Applications of Recurrent Neural Networks on timeseries data in the Enterprise with DL4J
Deep Learning and Recurrent Neural Networks in the Enterprise
Deep Learning and Recurrent Neural Networks in the Enterprise
Josh Patterson
Building Production Class Deep Learning Workflows for the Enterprise
Deep Learning: DL4J and DataVec
Deep Learning: DL4J and DataVec
Josh Patterson
Building Production Class Deep Learning Workflows for the Enterprise
Smart Data Conference: DL4J and DataVec
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Josh Patterson
Enterprise Deep Learning with DL4J - Hadoop Summit 2015 presentation by Josh Patterson
Deep learning with DL4J - Hadoop Summit 2015
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Josh Patterson
Introduction to deep learning and DL4J - http://deeplearning4j.org/ - a guest lecture by Josh Patterson at Georgia Tech for the cse6242 graduate class.
Georgia Tech cse6242 - Intro to Deep Learning and DL4J
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Josh Patterson
What is Deep Learning? Types of Deep Networks Tools, Resources, and DL4J
Deep Learning Intro - Georgia Tech - CSE6242 - March 2015
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Slides for my talk about Object Calisthenics, presented at Code Europe 2017 in Cracow, Wroclaw and Warsaw.
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by Joe Spisak, Product Manager for Amazon Deep Learning, AWS
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Cinchcast (aka BlogTalkRadio) is a startup in New York City. Using only a phone, you can broadcast your message globally to millions of listeners. Thousands of broadcasts are happening every day on topics ranging from technology to battling cancer. In this talk, we will discuss how we accomplished this, the technology behind it, and the challenges ahead. We will talk about what it's like building a startup in .NET and the techniques we have used to scale, such as HTML and donut caching, lazy loading of data, elastic search, as well as marrying telephony to the web stack.
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The term "machine learning" is increasingly bandied about in corporate settings and cocktail parties, but what is it, really? In this session we'll answer that question, providing an approachable overview of machine learning concepts, technologies, and use cases. We'll then take a deeper dive into machine learning topics such as supervised learning, unsupervised learning, and deep learning. We'll also survey various machine learning APIs and platforms. Technologies including Spring and Cloud Foundry will be leveraged in the demos. You'll be the hit of your next party when you're able to express the near-magical inner-workings of artificial neural networks!
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Slides for a talk given at the Spark Barcelona Meetup on Dec 9th
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François Garillot
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A tutorial given at NAACL HLT 2013. Richard Socher and Christopher Manning http://nlp.stanford.edu/courses/NAACL2013/ Machine learning is everywhere in today's NLP, but by and large machine learning amounts to numerical optimization of weights for human designed representations and features. The goal of deep learning is to explore how computers can take advantage of data to develop features and representations appropriate for complex interpretation tasks. This tutorial aims to cover the basic motivation, ideas, models and learning algorithms in deep learning for natural language processing. Recently, these methods have been shown to perform very well on various NLP tasks such as language modeling, POS tagging, named entity recognition, sentiment analysis and paraphrase detection, among others. The most attractive quality of these techniques is that they can perform well without any external hand-designed resources or time-intensive feature engineering. Despite these advantages, many researchers in NLP are not familiar with these methods. Our focus is on insight and understanding, using graphical illustrations and simple, intuitive derivations. The goal of the tutorial is to make the inner workings of these techniques transparent, intuitive and their results interpretable, rather than black boxes labeled "magic here". The first part of the tutorial presents the basics of neural networks, neural word vectors, several simple models based on local windows and the math and algorithms of training via backpropagation. In this section applications include language modeling and POS tagging. In the second section we present recursive neural networks which can learn structured tree outputs as well as vector representations for phrases and sentences. We cover both equations as well as applications. We show how training can be achieved by a modified version of the backpropagation algorithm introduced before. These modifications allow the algorithm to work on tree structures. Applications include sentiment analysis and paraphrase detection. We also draw connections to recent work in semantic compositionality in vector spaces. The principle goal, again, is to make these methods appear intuitive and interpretable rather than mathematically confusing. By this point in the tutorial, the audience members should have a clear understanding of how to build a deep learning system for word-, sentence- and document-level tasks. The last part of the tutorial gives a general overview of the different applications of deep learning in NLP, including bag of words models. We will provide a discussion of NLP-oriented issues in modeling, interpretation, representational power, and optimization.
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Contenu connexe
Tendances
Deep Learning on hadoop at galvanize for next ml
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Slides for my talk about Object Calisthenics, presented at Code Europe 2017 in Cracow, Wroclaw and Warsaw.
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by Joe Spisak, Product Manager for Amazon Deep Learning, AWS
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Amazon Deep Learning
Amanda Mackay (she/her)
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David Pilato
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Slides for the CI Tutorial delivered at STPcon 2009.
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Presentation slides about the architecture of “Dragon” A distributed object storage at Yahoo! JAPAN.
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Slides from Elasticsearch workshop conducted at The Fifth Elephant 2013.
Workshop: Learning Elasticsearch
Workshop: Learning Elasticsearch
Anurag Patel
The term "machine learning" is increasingly bandied about in corporate settings and cocktail parties, but what is it, really? In this session we'll answer that question, providing an approachable overview of machine learning concepts, technologies, and use cases. We'll then take a deeper dive into machine learning topics such as supervised learning, unsupervised learning, and deep learning. We'll also survey various machine learning APIs and platforms. Technologies including Spring and Cloud Foundry will be leveraged in the demos. You'll be the hit of your next party when you're able to express the near-magical inner-workings of artificial neural networks!
Machine Learning Exposed!
Machine Learning Exposed!
javafxpert
Tendances
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Deep learning on Hadoop/Spark -NextML
Deep learning on Hadoop/Spark -NextML
DSC NTUE Info Session
DSC NTUE Info Session
Object Calisthenics (Code Europe 2017)
Object Calisthenics (Code Europe 2017)
Amazon Deep Learning
Amazon Deep Learning
You Too Can Be a Radio Host Or How We Scaled a .NET Startup And Had Fun Doing It
You Too Can Be a Radio Host Or How We Scaled a .NET Startup And Had Fun Doing It
Elasticsearch - Devoxx France 2012 - English version
Elasticsearch - Devoxx France 2012 - English version
Even images and videos are BigData, analyze them. Daniele Madama, XPeppers
Even images and videos are BigData, analyze them. Daniele Madama, XPeppers
Core Principles Of Ci
Core Principles Of Ci
Dragon: A Distributed Object Storage at Yahoo! JAPAN (WebDB Forum 2017 / E...
Dragon: A Distributed Object Storage at Yahoo! JAPAN (WebDB Forum 2017 / E...
Workshop: Learning Elasticsearch
Workshop: Learning Elasticsearch
Machine Learning Exposed!
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En vedette
Slides for a talk given at the Spark Barcelona Meetup on Dec 9th
Deep learning on a mixed cluster with deeplearning4j and spark
Deep learning on a mixed cluster with deeplearning4j and spark
François Garillot
Deep Learning on Hadoop
Deep Learning on Hadoop
DataWorks Summit
A tutorial given at NAACL HLT 2013. Richard Socher and Christopher Manning http://nlp.stanford.edu/courses/NAACL2013/ Machine learning is everywhere in today's NLP, but by and large machine learning amounts to numerical optimization of weights for human designed representations and features. The goal of deep learning is to explore how computers can take advantage of data to develop features and representations appropriate for complex interpretation tasks. This tutorial aims to cover the basic motivation, ideas, models and learning algorithms in deep learning for natural language processing. Recently, these methods have been shown to perform very well on various NLP tasks such as language modeling, POS tagging, named entity recognition, sentiment analysis and paraphrase detection, among others. The most attractive quality of these techniques is that they can perform well without any external hand-designed resources or time-intensive feature engineering. Despite these advantages, many researchers in NLP are not familiar with these methods. Our focus is on insight and understanding, using graphical illustrations and simple, intuitive derivations. The goal of the tutorial is to make the inner workings of these techniques transparent, intuitive and their results interpretable, rather than black boxes labeled "magic here". The first part of the tutorial presents the basics of neural networks, neural word vectors, several simple models based on local windows and the math and algorithms of training via backpropagation. In this section applications include language modeling and POS tagging. In the second section we present recursive neural networks which can learn structured tree outputs as well as vector representations for phrases and sentences. We cover both equations as well as applications. We show how training can be achieved by a modified version of the backpropagation algorithm introduced before. These modifications allow the algorithm to work on tree structures. Applications include sentiment analysis and paraphrase detection. We also draw connections to recent work in semantic compositionality in vector spaces. The principle goal, again, is to make these methods appear intuitive and interpretable rather than mathematically confusing. By this point in the tutorial, the audience members should have a clear understanding of how to build a deep learning system for word-, sentence- and document-level tasks. The last part of the tutorial gives a general overview of the different applications of deep learning in NLP, including bag of words models. We will provide a discussion of NLP-oriented issues in modeling, interpretation, representational power, and optimization.
Deep Learning for NLP (without Magic) - Richard Socher and Christopher Manning
Deep Learning for NLP (without Magic) - Richard Socher and Christopher Manning
BigDataCloud
Introduction to Neural Networks, Deep Learning, TensorFlow, and Keras. For code see https://github.com/asimjalis/tensorflow-quickstart
Neural Networks and Deep Learning
Neural Networks and Deep Learning
Asim Jalis
Distributed Deep Learning on Hadoop Clusters
Distributed Deep Learning on Hadoop Clusters
Distributed Deep Learning on Hadoop Clusters
DataWorks Summit/Hadoop Summit
Slide deck presented for a tutorial at KDD2017. https://engineering.linkedin.com/data/publications/kdd-2017/deep-learning-tutorial
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En vedette
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Deep learning on a mixed cluster with deeplearning4j and spark
Deep learning on a mixed cluster with deeplearning4j and spark
Deep Learning on Hadoop
Deep Learning on Hadoop
Deep Learning for NLP (without Magic) - Richard Socher and Christopher Manning
Deep Learning for NLP (without Magic) - Richard Socher and Christopher Manning
Neural Networks and Deep Learning
Neural Networks and Deep Learning
Distributed Deep Learning on Hadoop Clusters
Distributed Deep Learning on Hadoop Clusters
Deep Learning for Personalized Search and Recommender Systems
Deep Learning for Personalized Search and Recommender Systems
Similaire à Deep Learning for Java (DL4J)
Building Large Java Projects Faster. Multicore javac and Makefile integration. The new build system for OpenJDK and sjavac.
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Fredrik Öhrström
В этом докладе мы рассмотрим мотивацию создания и текущее состояние следующих Java проектов: - Проект Valhalla для внедрения встроенных типов (Inline Types) в Java - Проект Loom для реализации облегченных потоков (Lightweight Threads) в Java - GraalVM - полиглотная виртуальная машина для Java. Мы также поговорим о том, как GraalVM делает приложения на основе Java более доступными для внедрения Serverless парадигмы.
JavaFest. Вадим Казулькин. Projects Valhalla, Loom and GraalVM
JavaFest. Вадим Казулькин. Projects Valhalla, Loom and GraalVM
FestGroup
In this talk I will cover the current state of the Projects Valhalla, Loom and GraalVM, also talking about challenges adopting Java with Serverless
Projects Valhalla, Loom and GraalVM at virtual JavaFest 2020 in Kiev, Ukraine...
Projects Valhalla, Loom and GraalVM at virtual JavaFest 2020 in Kiev, Ukraine...
Vadym Kazulkin
for more information visit: http://salaboy.com
Java to Golang: An intro by Ryan Dawson Seldon.io
Java to Golang: An intro by Ryan Dawson Seldon.io
Mauricio (Salaboy) Salatino
It's now a long time Gradle is the official build system for Android. And as a very good developer you already switched to it, and you customize it depending on your needs. Most of the time, the cleaner way to manage all these customizations is to build Gradle plugins. During this talk you will discover best practices about building your plugin to make it a good citizen, more efficient, and more famous! This presentation will tell about: - Building a Gradly DSL - Interact with the Android Gradle Plugin - Test your project on the good way
Gradle plugins, take it to the next level
Gradle plugins, take it to the next level
Eyal Lezmy
Other dl4j in the wild meetup slides (Community updates): https://253bb1695cca2a388faddf6cd4.doorkeeper.jp/events/50918
Dl4j in the wild
Dl4j in the wild
Adam Gibson
You’re tasked with ‘doing AppSec’ for your company and you’ve got more apps and issues than you know how to deal with. How do you make sense of the different tools outputs for all your different apps? DefectDojo can be your one source of truth and become the heart of your AppSec automation program. DefectDojo grew out of a Product Security program 8 years ago and was created by AppSec people for AppSec people. In this talk, you’ll learn about DefectDojo and how to make the most of the many features it offers including its REST-based API. DefectDojo can be your one source of truth for discovered security vulnerabilities, report generation, aggregation of over 80 different security tools, inventory of applications, tracking testing efforts and metrics on the AppSec program. DefectDojo was the heart of an AppSec automation effort that saw an increase in assessments from 44 to 414 in two years. Don't you want 9.4 times more output from your AppSec program? It's time to ditch spreadsheets and get DefectDojo.
Intro to DefectDojo at OWASP Switzerland
Intro to DefectDojo at OWASP Switzerland
Matt Tesauro
spring
01 spring-intro
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hossein helali
A small slide stack to introduce an audience to DSpace, what it is, what makes it go, what you get out of the box, and how to start off working with it. Originally delivered to a group of developers at UCLA Library, so there might be some UCLA-specific links, that don't work for non-UCLA types. Sorry about that.
Introduction to DSpace
Introduction to DSpace
Hardy Pottinger
Talk at RubyKaigi 2015. Plugin architecture is known as a technique that brings extensibility to a program. Ruby has good language features for plugins. RubyGems.org is an excellent platform for plugin distribution. However, creating plugin architecture is not as easy as writing code without it: plugin loader, packaging, loosely-coupled API, and performance. Loading two versions of a gem is a unsolved challenge that is solved in Java on the other hand. I have designed some open-source software such as Fluentd and Embulk. They provide most of functions by plugins. I will talk about their plugin-based architecture.
Plugin-based software design with Ruby and RubyGems
Plugin-based software design with Ruby and RubyGems
Sadayuki Furuhashi
An Adopt OpenJDK presentation delivered at Javaland 2014 near (Phantasialand) Munich, Germany.
Adopt OpenJDK the past, the present & the future
Adopt OpenJDK the past, the present & the future
Mani Sarkar
How to build Neo4j Stored Procedures, Part 1
Neo4j Stored Procedure Training Part 1
Neo4j Stored Procedure Training Part 1
Max De Marzi
Read: issuu.com/shuweigoh/docs/skymind At Skymind, we’re tackling some of the most advanced problems in data analysis and machine intelligence. We offer state-of-the-art, flexible, scalable deep learning for industry. Deep learning is becoming an important tool set for natural-language processing (NLP), computer vision, database predictions, pattern recognition, image/video processing and fraud detection.
Skymind Company Profile
Skymind Company Profile
Shu Wei Goh
AdoptOpenJDK is rapidly becoming a leading provider of OpenJDK™ binaries. With over 125 million downloads in the last year, it is now a serious contender for your production usage of Java™. AdoptOpenJDK provides prebuilt OpenJDK™ binaries from a fully open-source set of build scripts and infrastructure. This talk will cover how we build on over 15 different platforms, execute over 60,000 tests and distribute OpenJDK™ binaries to millions of users. We will also cover how AdoptOpenJDK binaries compare against the Java™ binaries that you use today. If you’re curious to understand more about our future roadmap, security and supporting platforms like Lego® Mindstorms® then this is the talk for you!
Adopt openjdk and how it impacts you in 2020
Adopt openjdk and how it impacts you in 2020
George Adams
Yahoo! Hadoop User Group - May Meetup - Extraordinarily rapid and robust data...
Yahoo! Hadoop User Group - May Meetup - Extraordinarily rapid and robust data...
Hadoop User Group
Leo Z and I gave a talk this year at AWS Re:Invent on Continuous Integration at OFA, Mozilla Foundation, and other companies.
Continuous Deployment @ AWS Re:Invent
Continuous Deployment @ AWS Re:Invent
John Schneider
With AWS, companies now have the ability to develop and run their applications with speed and flexibility like never before. Working with an infrastructure that can be 100 percent API driven enables businesses to use lean methodologies and realize these benefits. This in turn leads to greater success for those who make use of these practices. In this session, we talk about some key concepts and design patterns for continuous deployment and continuous integration, two elements of lean development of applications and infrastructures.
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Continuous Integration and Deployment Best Practices on AWS (ARC307) | AWS re...
Amazon Web Services
What the Gradle team have shipped since Gradle 3.0, featuring performance features like compile avoidance, user experience features like the Kotlin DSL, and brand new tools like the Java 9 support
What's new in Gradle 4.0
What's new in Gradle 4.0
Eric Wendelin
Jim Helwig (University of Wisconsin-Madison) Aaron Grant (Oakland University) Lori Tirpak (Oakland University) Session presentation at the 2012 Jasig Sakai Conference uPortal is a highly powerful and flexible portal framework that institutions have used in a variety of innovative ways to solve very real campus problems. This presentation showcases two different uPortal implementations demonstrating the diverse ways campuses make use of a central portal. Oakland University (located in beautiful Oakland County Michigan) is a relative newcomer to uPortal: they first launched their uPortal-based campus portal, MySail, in 2009, using framework version 3.1. But on February 22nd 2012 they became the first school to run a portal based on uPortal4 in production. In this session we will showcase the new Oakland MySail portal and discuss the processes they used to migrate to uPortal 4. We will cover tips and tricks, best practices, and lessons learned. We will also highlight the use of Jasig portlets and talk about getting the most from those collaborative portlet projects. The University of Wisconsin-Madison has operated a campus portal, My UW-Madison, since 2001. In 2010 we rolled out virtual portals running on the same instance for our 13 sister campuses in the University of Wisconsin System. A migration to uPortal was completed in 2006 and the infrastructure was upgraded to uPortal 4 in April of this year. In this session we will highlight the personalized yet unified portal experience for our applicants, students, faculty and staff. We will showcase some of the innovate portlets we have implemented as well as our adoption of Jasig portlets. Finally we will highlight the benefits of developing Open Source portlets and engaging with the uPortal community.
uPortal 4 in Action
uPortal 4 in Action
Jim Helwig
This is the presentation from Meetup, that was held at Skyscanner, Sofia: https://www.meetup.com/Code-Voyagers-Sofia/events/256913292/ In the context of business application development, the dream of every Java developer is to have a quick turn-around time. On the other side, in the same context, the dream of every JavaScript developer is to build scalable applications, based on proven technology. See how these two worlds meet under the umbrella of Eclipse Dirigible – an Open Source RAD Low-Code/No-Code Platform.
Dirigible @ Skyscanner
Dirigible @ Skyscanner
Jordan Pavlov
Similaire à Deep Learning for Java (DL4J)
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Building Large Java Projects Faster: Multicore javac and Makefile integration
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JavaFest. Вадим Казулькин. Projects Valhalla, Loom and GraalVM
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Projects Valhalla, Loom and GraalVM at virtual JavaFest 2020 in Kiev, Ukraine...
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Java to Golang: An intro by Ryan Dawson Seldon.io
Gradle plugins, take it to the next level
Gradle plugins, take it to the next level
Dl4j in the wild
Dl4j in the wild
Intro to DefectDojo at OWASP Switzerland
Intro to DefectDojo at OWASP Switzerland
01 spring-intro
01 spring-intro
Introduction to DSpace
Introduction to DSpace
Plugin-based software design with Ruby and RubyGems
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