- Deep learning and machine learning techniques can be used for image pattern analysis, speech recognition, natural language processing, and autonomous vehicles.
- Amazon Web Services provides services and capabilities to help customers build, train, and deploy deep learning and AI models at scale.
This lecture talks about parsing. Briefly gives overview on lexicon, categorization, grammar rules, syntactic tree, word senses and various challenges of natural language processing
Slides for the hands on PyData workshop.
Cover three main topics:
- Current state of NLP models at Walmart
- Steps we took to optimize serving BERT
- how we serve models with Facebook’s TorchServe.
Corresponding repo for notebooks for handson:
https://bit.ly/pytorch-workshop-2021
Big Data and Natural Language ProcessingMichel Bruley
Natural Language Processing (NLP) is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language.
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
This lecture talks about parsing. Briefly gives overview on lexicon, categorization, grammar rules, syntactic tree, word senses and various challenges of natural language processing
Slides for the hands on PyData workshop.
Cover three main topics:
- Current state of NLP models at Walmart
- Steps we took to optimize serving BERT
- how we serve models with Facebook’s TorchServe.
Corresponding repo for notebooks for handson:
https://bit.ly/pytorch-workshop-2021
Big Data and Natural Language ProcessingMichel Bruley
Natural Language Processing (NLP) is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language.
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
Six month major project on text classification with twitter sentiment analysis of US airlines.
It tells the importance of data and reviews given by the users for different airlines and helps recommending options to improve user experience.
TensorFlow에 대한 분석 내용
- TensorFlow?
- 배경
- DistBelief
- Tutorial - Logistic regression
- TensorFlow - 내부적으로는
- Tutorial - CNN, RNN
- Benchmarks
- 다른 오픈 소스들
- TensorFlow를 고려한다면
- 설치
- 참고 자료
Search algorithms are fundamental to artificial intelligence (AI) because they play a crucial role in solving complex problems, making decisions, and finding optimal solutions in various AI applications.
Applied Deep Learning for Text Classification - Examples from the HR IndustryAlexander Chukovski
Text Classification is a major part of our data processing stack. We have successfully managed to automate a large part of complex document classification processes with traditional linear classifiers, based on n-grams, bad of words and linguistic rules and extensive feature engineering. In this presentation we decided to benchmark couple of deep learning algorithms like Very Deep Convolutional Neural Networks (VDCNN) and Hierarchical Deep Learning for Text Classification (HDLTex) on the classification of career levels for jobs. In parallel, we compare the Deep Learning results to an innovative text classification framework, developed and open-sourced by Facebook (FastText), which yields supreme results.
AI-04 Production System - Search Problem.pptxPankaj Debbarma
Production Systems
A simple string rewriting production system example
Search Problem
Basic searching process
Algorithm’s performance and complexity
Computational complexity
‘Big - O’ notation
Tower of Hanoi
8 Puzzle
Water Jug Problem
Can Solution Steps be Ignored
Is Good Solution Absolute or Relative
Issues in the Design of Search Programs
NLTK: Natural Language Processing made easyoutsider2
Natural Language Toolkit(NLTK), an open source library which simplifies the implementation of Natural Language Processing(NLP) in Python is introduced. It is useful for getting started with NLP and also for research/teaching.
Neural Language Generation Head to Toe Hady Elsahar
This is a gentle introduction to Natural language Generation (NLG) using deep learning. If you are a computer science practitioner with basic knowledge about Machine learning. This is a gentle intuitive introduction to Language Generation using Neural Networks. It takes you in a journey from the basic intuitions behind modeling language and how to model probabilities of sequences to recurrent neural networks to large Transformers models that you have seen in the news like GPT2/GPT3. The tutorial wraps up with a summary on the ethical implications of training such large language models on uncurated text from the internet.
Las aplicaciones de Inteligencia Artificial como Machine Learning y Deep Learning se han convertido en parte importante en nuestras vidas. Los productos que compramos, si somos o no aptos para un préstamo bancario, las películas o series que Netflix nos recomienda, coches autoconducidos, reconocimiento de objetos, etc; toda esa información es dirigida hacia nosotros por estos algoritmos.
En la actualidad, estos campos de estudio son los más apasionantes y retadores en computación debido a su alto nivel de complejidad y gran demanda en el mercado. En esta presentación vamos a conocer y aprender a diferenciar estos conceptos, ya que son herramientas inevitables para el mejoramiento de la vida humana.
A continuación, te presentamos algunos de los temas específicos que se expondrán:
- Contexto de ML y DL en Inteligencia Artificial.
- Machine Learning.
- Supervised Learning.
- Unsupervised Learning.
- Deep Learning.
- Artificial Neural Network.
- Convolutional Neural Networks.
- Aplicaciones en ML y DL.
Given presentation tell us about string, string matching and the navie method of string matching. Well this method has O((n-m+1)*m) time complexicity. It also tells the problem with naive approach and gives list of approaches which can be applied to reduce the time complexicity
아마존닷컴은 쇼핑 상품 추천, 배송 및 물류 예측 등에 기계 학습 기술을 활용해 왔으며, 최근 프라임 서비스를 위한 음악, 이미지, 영상 인식, 무인 매장인 아마존고 및 음성 비서 서비스인 알렉사에 딥러닝 기술을 활용하고 있다. 본 세션에서는 이러한 주요 딥러닝 활용 기술 사례를 알아보고, AWS 클라우드를 통해 제공하는 이미지/영상 인식, 음성 인식 및 합성, 기계 번역, 자연어 처리 등 다양한 딥러닝 기반 서비스 구현 방법을 살펴본다. 개발자들이 직접 딥러닝 기반 데이터 처리, 모델 학습 및 서비스 배포까지 손쉽게 구성할 수 있는 Amazon SageMaker와 Deep Lens를 통해 어떻게 IoT 기반 서비스로 활용할 수 있는지 시연을 통해 알아본다.
Six month major project on text classification with twitter sentiment analysis of US airlines.
It tells the importance of data and reviews given by the users for different airlines and helps recommending options to improve user experience.
TensorFlow에 대한 분석 내용
- TensorFlow?
- 배경
- DistBelief
- Tutorial - Logistic regression
- TensorFlow - 내부적으로는
- Tutorial - CNN, RNN
- Benchmarks
- 다른 오픈 소스들
- TensorFlow를 고려한다면
- 설치
- 참고 자료
Search algorithms are fundamental to artificial intelligence (AI) because they play a crucial role in solving complex problems, making decisions, and finding optimal solutions in various AI applications.
Applied Deep Learning for Text Classification - Examples from the HR IndustryAlexander Chukovski
Text Classification is a major part of our data processing stack. We have successfully managed to automate a large part of complex document classification processes with traditional linear classifiers, based on n-grams, bad of words and linguistic rules and extensive feature engineering. In this presentation we decided to benchmark couple of deep learning algorithms like Very Deep Convolutional Neural Networks (VDCNN) and Hierarchical Deep Learning for Text Classification (HDLTex) on the classification of career levels for jobs. In parallel, we compare the Deep Learning results to an innovative text classification framework, developed and open-sourced by Facebook (FastText), which yields supreme results.
AI-04 Production System - Search Problem.pptxPankaj Debbarma
Production Systems
A simple string rewriting production system example
Search Problem
Basic searching process
Algorithm’s performance and complexity
Computational complexity
‘Big - O’ notation
Tower of Hanoi
8 Puzzle
Water Jug Problem
Can Solution Steps be Ignored
Is Good Solution Absolute or Relative
Issues in the Design of Search Programs
NLTK: Natural Language Processing made easyoutsider2
Natural Language Toolkit(NLTK), an open source library which simplifies the implementation of Natural Language Processing(NLP) in Python is introduced. It is useful for getting started with NLP and also for research/teaching.
Neural Language Generation Head to Toe Hady Elsahar
This is a gentle introduction to Natural language Generation (NLG) using deep learning. If you are a computer science practitioner with basic knowledge about Machine learning. This is a gentle intuitive introduction to Language Generation using Neural Networks. It takes you in a journey from the basic intuitions behind modeling language and how to model probabilities of sequences to recurrent neural networks to large Transformers models that you have seen in the news like GPT2/GPT3. The tutorial wraps up with a summary on the ethical implications of training such large language models on uncurated text from the internet.
Las aplicaciones de Inteligencia Artificial como Machine Learning y Deep Learning se han convertido en parte importante en nuestras vidas. Los productos que compramos, si somos o no aptos para un préstamo bancario, las películas o series que Netflix nos recomienda, coches autoconducidos, reconocimiento de objetos, etc; toda esa información es dirigida hacia nosotros por estos algoritmos.
En la actualidad, estos campos de estudio son los más apasionantes y retadores en computación debido a su alto nivel de complejidad y gran demanda en el mercado. En esta presentación vamos a conocer y aprender a diferenciar estos conceptos, ya que son herramientas inevitables para el mejoramiento de la vida humana.
A continuación, te presentamos algunos de los temas específicos que se expondrán:
- Contexto de ML y DL en Inteligencia Artificial.
- Machine Learning.
- Supervised Learning.
- Unsupervised Learning.
- Deep Learning.
- Artificial Neural Network.
- Convolutional Neural Networks.
- Aplicaciones en ML y DL.
Given presentation tell us about string, string matching and the navie method of string matching. Well this method has O((n-m+1)*m) time complexicity. It also tells the problem with naive approach and gives list of approaches which can be applied to reduce the time complexicity
아마존닷컴은 쇼핑 상품 추천, 배송 및 물류 예측 등에 기계 학습 기술을 활용해 왔으며, 최근 프라임 서비스를 위한 음악, 이미지, 영상 인식, 무인 매장인 아마존고 및 음성 비서 서비스인 알렉사에 딥러닝 기술을 활용하고 있다. 본 세션에서는 이러한 주요 딥러닝 활용 기술 사례를 알아보고, AWS 클라우드를 통해 제공하는 이미지/영상 인식, 음성 인식 및 합성, 기계 번역, 자연어 처리 등 다양한 딥러닝 기반 서비스 구현 방법을 살펴본다. 개발자들이 직접 딥러닝 기반 데이터 처리, 모델 학습 및 서비스 배포까지 손쉽게 구성할 수 있는 Amazon SageMaker와 Deep Lens를 통해 어떻게 IoT 기반 서비스로 활용할 수 있는지 시연을 통해 알아본다.
최근 데이터의 폭증과 이를 기반한 빅데이터 분석이 기업 비지니스 성패에 큰 영향을 끼치고 있습니다. 다양한 기업의 데이터 기반 의사 결정을 위한 요구를 수용하는 분석 플랫폼과 인공 지능 기술의 도입은 큰 화두입니다. 본 세션에서는 기업의 비지니스 전략 및 기획을 담당하시는 분들을 위해 클라우드 기반 데이터 분석 플랫폼을 쉽게 접근하고 사용할 수 있는 방법을 사례 위주로 소개합니다.국내외 주요 기업들이 어떻게 AWS기반 데이터 분석 및 기계 학습 서비스로 비지니스 혁신에 활용하고 있는지 알아보시기 바랍니다.
AWS Summit Singapore - Artificial Intelligence to Delight Your CustomersAmazon Web Services
Andrew Watts-Curnow, Senior Cloud Architect – Professional Services, APAC, AWS
Learn how advances in AI are enabling improvements in customer experience. This is a deep dive using machine learning frameworks for people who are familiar with building their own models. In this session, we will detail a facial recognition solution that can detect known customers and alert customer service staff.
AWS re:Invent is an annual global conference of the Amazon Web Services community held in Las Vegas. In 2017, we held 1000+ breakout sessions and attracted over 40,000 attendees. The event offers expanded opportunities to learn about the latest AWS releases, use cases and business benefits, not to mention diving deep into hot topics and meeting with our subject matter experts.
Missed it? Don’t worry, we are bringing AWS re:Invent to Hong Kong on Jan 18, 2018. Packed in a day, AWS re:Invent 2017 Recap Hong Kong will showcase new releases announced at re:Invent 2017 on Serverless & Container, DevOps & Mobile, Artificial Intelligence & Machine Learning and more. Local customers will also be invited to share their re:Invent experience and success stories with AWS.
Discover the latest services and features from Amazon Web Services and learn how to integrate them into your applications
Microsoft & Machine Learning / Artificial Intelligenceİbrahim KIVANÇ
In this presentation you'll find Machine Learning / Deep Learning tools and services from Microsoft. Including Azure Machine Learning Workbench, Azure Notebooks, Azure Data Science Virtual Machines and more.
Here are the demos & resources
https://github.com/ikivanc/Azure-ML-Workbench-Iris-Dataset-Classification
https://github.com/ikivanc/Azure-ML-Resources
최근 데이터의 폭증과 이를 기반한 빅데이터 분석이 기업 비지니스 성패에 큰 영향을 끼치고 있습니다. 다양한 기업의 데이터 기반 의사 결정을 위한 요구를 수용하는 분석 플랫폼과 인공 지능 기술의 도입은 큰 화두입니다. 본 세션에서는 기업의 비지니스 전략 및 기획을 담당하시는 분들을 위해 클라우드 기반 데이터 분석 플랫폼을 쉽게 접근하고 사용할 수 있는 방법을 사례 위주로 소개합니다.국내외 주요 기업들이 어떻게 AWS기반 데이터 분석 및 기계 학습 서비스로 비지니스 혁신에 활용하고 있는지 알아보시기 바랍니다.
다시보기 링크: https://youtu.be/24YgdrJ9r-A
Amazon SageMaker is a fully-managed platform that lets developers and data scientists build and scale machine learning solutions. First, we'll show you how SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we'll show you how to build, train and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc). Finally, we'll show you how to use 3rd-party models from the AWS marketplace.
Amazon SageMaker는 기계 학습을 위한 데이터와 알고리즘, 프레임워크를 빠르게 연결하에 손쉽게 ML 구축이 가능한 신규 클라우드 서비스입니다. 이번 시간에는 Amazon S3에 저장된 학습 데이터를 이용하여 가장 일반적으로 사용하는 알고리즘 몇 가지를 직접 실행해 보는 실습을 진행합니다. 이를 위해 유명한 오픈 소스 프레임워크인 TensorFlow와 Keras 그리고 Apache MXNet과 Gluon 등을 사용해 봅니다.
Il Machine Learning può sembrare più difficile di quanto non lo sia perché il processo di sviluppo, training e deployment dei modelli in produzione è troppo complicato e lento. Amazon SageMaker è un servizio completamente gestito che consente a sviluppatori e data scientist di progettare, implementare e distribuire modelli di Machine Learning in qualsiasi scala. Amazon SageMaker offre una scelta di algoritmi di machine learning altamente performanti e framework preconfigurati come Apache MXNet, TensorFlow, PyTorch e Chainer; inoltre, è possibile utilizzare framework o algoritmi alternativi attraverso container Docker. In questa sessione approfondiremo l’utilizzo di Amazon SageMaker, anche attraverso alcuni pratici esempi.
In this session, we discuss how scientists worldwide – from CERN, the National Health Service in the UK, and the Communication Research Centre (CRC) Canada – are using AWS to accelerate the pace of research. Enjoy a range of case studies and overview of tools, such as high-performance computing, machine learning, and deep learning, using AWS research. Discover how to seamlessly extend on-premises clusters into Amazon EC2; and how researchers from PHAC use this method daily at the National Microbiology Lab (NML) in Winnipeg to sequence genome data, rapidly detect disease outbreaks, and inform public health responses. Finally, learn about CRC’s advanced, secure scientific-computing environment in the cloud, plus its role in democratizing high-performance computing for various scientific needs.
Build Deep Learning Applications Using Apache MXNet - Featuring Chick-fil-A (...Amazon Web Services
The Apache MXNet deep learning framework is used for developing, training, and deploying diverse AI applications, including computer vision, speech recognition, natural language processing, and more at scale. In this session, learn how to get started with Apache MXNet on the Amazon SageMaker machine learning platform. Chick-fil-A share how they got started with MXNet on Amazon SageMaker to measure waffle fry freshness and how they leverage AWS services to improve the Chick-fil-A guest experience.
Build Deep Learning Applications Using Apache MXNet, Featuring Workday (AIM40...Amazon Web Services
The Apache MXNet deep learning framework is used for developing, training, and deploying diverse AI applications, including computer vision, speech recognition, and natural language processing at scale. In this session, learn how to get started with MXNet on the Amazon SageMaker machine learning platform. Hear from Workday about how they built computer vision and natural language processing (NLP) models using MXNet to automatically extract information from paper documents, such as expense receipts and populate data records. Workday also shares its experience using Sockeye, an MXNet toolkit for quickly prototyping sequence-to-sequence NLP models.
2018 11 14 Artificial Intelligence and Machine Learning in AzureBruno Capuano
Slides used during my session "Artificial Intelligence and Machine Learning in Azure" for The Azure Group (Canada's Azure User Community) on November 14 2018.
Public group
비행기 설계를 왜 통일 해야 할까?
디자인 시스템을 하는 이유
비행기들이 다 용도가 다르다...어떻게 설계하지?
맥락이 다른 페이지와 패턴
경유지까지 아직 멀었다... 언제 수리하지?
디자인 시스템을 적용하는 시점
엔지니어랑 얘기해서 정비해야하는데...어떻게 수리하지?
디자인 시스템을 적용하는 프로세스
비행기 설계가 바뀐걸 어떻게 알리지?
디자인 시스템의 전파
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
28. • A Kumar, et al, Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding,
https://arxiv.org/abs/1711.00549
• R Maas, et al, Domain-Specific Utterance End-Point Detection for Speech Recognition - Proc. Interspeech 2017,
http://www.isca-speech.org/archive/Interspeech_2017/pdfs/1673.PDF
• B King et al, Robust Speech Recognition Via Anchor Word Representations - Proc. Interspeech 2017,
http://www.isca-speech.org/archive/Interspeech_2017/pdfs/1570.PDF
• A Kumar et al, Zero-shot learning across heterogeneous overlapping domains - Proc. Interspeech 2017,
http://www.isca-speech.org/archive/Interspeech_2017/pdfs/0516.PDF
• M Sun et al, Max-pooling loss training of long short-term memory networks for small-footprint keyword spotting,
Spoken Language Technology Workshop (SLT), 2016 IEEE
• F Ladhak et al, LatticeRnn: Recurrent Neural Networks Over Lattices - Proc. Interspeech 2016, http://www.isca-
speech.org/archive/Interspeech_2016/pdfs/1583.PDF
• S Panchapagesan et al, Multi-Task Learning and Weighted Cross-Entropy for DNN-Based Keyword Spotting -
Proc. Interspeech 2016, http://www.isca-speech.org/archive/Interspeech_2016/pdfs/1485.PDF
• R Maas et al, Anchored Speech Detection - Proc. Interspeech 2016, http://www.isca-
speech.org/archive/Interspeech_2016/pdfs/1346.PDF
• M Sun et al, Model Shrinking for Embedded Keyword Spotting, 2015 IEEE 14th International Conference on
Machine Learning and Applications (ICMLA)
• N Strom, Scalable distributed DNN training using commodity GPU cloud computing, Annual Conference of the
International Speech Communication Association 2015, http://www.isca-
speech.org/archive/interspeech_2015/papers/i15_1488.pdf
29. NEW!
“Alexa, start the meeting.”
“Alexa, dial 555-8000.”
“Alexa, lower the blinds.”
“Alexa, ask Salesforce which
big deals closed today.”
35. - -
FRAMEWORKS AND INTERFACES
AWS DEEP LEARNING AMI
Apache MXNet TensorFlowCaffe2 Torch KerasCNTK PyTorch GluonTheano
PLATFORM SERVICES
VISION
AWS DeepLensAmazon SageMaker
LANGUAGE
Amazon Rekognition Amazon Polly Amazon Lex
Amazon Rekognition Video Amazon Transcribe Amazon Comprehend
Alexa for Business
VR/AR
Amazon Sumerian
APPLICATION SERVICES
Amazon Machine Learning Amazon EMR & SparkMechanical Turk
INSTANCES
GPU (G2/P2/P3) CPU (C5) FPGA (F1)
Amazon Translate
36. F R A M E W O R K S A N D I N T E R FA C E S
NVIDIA
Tesla V100 GPUs
P3 1 Petaflop of compute
NVLink 2.0
5,120 Tensor cores
128GB of memory
~14X faster than P2
P3 Instance Deep Learning AMI Frameworks
PLATFORM SERVICES
VISION LANGUAGE VR/IR
APPLICATION SERVICE
AWS DeepLensAmazon SageMaker Amazon Machine Learning Amazon EMR & SparkMechanical Turk
AWS DEEP LEARNING AMI
Apache MXNet TensorFlowCaffe2 Torch KerasCNTK PyTorch GluonTheano
INSTANCES
GPU (G2/P2/P3) CPU (C5) FPGA (F1)
37. 2 0 3
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43. FRAMEWORKS AND INTERFACES
AWS DEEP LEARNING AMI
Apache MXNet TensorFlowCaffe2 Torch KerasCNTK PyTorch GluonTheano
PLATFORM SERVICES
VISION
AWS DeepLensAmazon SageMaker
LANGUAGE
Amazon Rekognition Amazon Polly Amazon Lex
Amazon Rekognition Video Amazon Transcribe Amazon Comprehend
Alexa for Business
VR/AR
Amazon Sumerian
APPLICATION SERVICES
Amazon Machine Learning Amazon EMR & SparkMechanical Turk
INSTANCES
GPU (G2/P2/P3) CPU (C5) FPGA (F1)
Amazon Translate
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Linear Learner Supervised
XGBoost Algorithm Supervised
Discrete Recommendations Factorization Machines Supervised
Image Classification Image Classification Algorithm Supervised, CNN
Neural Machine Translation Sequence to Sequence Supervised, seq2seq
Time-series Prediction DeepAR Supervised, RNN
Discrete Groupings K-Means Algorithm Unsupervised
Dimensionality Reduction PCA (Principal Component Analysis) Unsupervised
Topic Determination Latent Dirichlet Allocation (LDA) Unsupervised
Neural Topic Model (NTM) Unsupervised,
Neural Network Based
45. CA
“With Amazon SageMaker, we can accelerate our Artificial Intelligence
initiatives at scale by building and deploying our algorithms on the
platform. We will create novel large-scale machine learning and AI
algorithms and deploy them on this platform to solve complex problems
that can power prosperity for our customers."
- Ashok Srivastava, Chief Data Officer, Intuit
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Micro-SD
Mini-HDMI
USB
USB
Reset
Audio out
Power
• Intel Atom Processor
• Intel Gen9 graphics
• Ubuntu OS- 16.04 LTS
• 100 GFLOPS performance
• Dual band Wi-Fi
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resolution
• 2 USB ports
• Micro HDMI
• Audio out
• AWS Greengrass
• clDNN Optimized for MXNet
47. FRAMEWORKS AND INTERFACES
AWS DEEP LEARNING AMI
Apache MXNet TensorFlowCaffe2 Torch KerasCNTK PyTorch GluonTheano
PLATFORM SERVICES
AWS DeepLensAmazon SageMaker Amazon Machine Learning Amazon EMR & SparkMechanical Turk
INSTANCES
GPU (G2/P2/P3) CPU (C5) FPGA (F1)
VISION LANGUAGE
Amazon Rekognition
Image
Amazon
Polly
Amazon
Lex
Amazon Rekognition
Video
Amazon
Transcribe
Amazon
Comprehend
Alexa for
Business
VR/AR
Amazon
Sumerian
APPLICATION SERVICES
Amazon
Translate
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53. AWS ML Customers
APPLICATION SERVICES
Amazon Lex
Amazon Polly
Amazon Comprehend
Amazon Translate
Amazon Transcribe
Amazon Rekognition Image
Amazon Rekognition Video
PLATFORM SERVICES
Amazon SageMaker AWS DeepLens
FRAMEWORKS AND INTERFACES
AWS Deep Learning AMI
Apache MXNet
Caffe2
CNTK
PyTorch
TensorFlow
Theano
Torch
Gluon
Keras
AWS ML Platform
DATA LAKE STORAGE
Amazon S3
SECURITY
Access Control
Encryption
COMPUTE
Powerful GPU and CPU Instances
ANALYTICS
Amazon Athena
Amazon Redshift
and Redshift Spectrum
Amazon EMR
(Spark, Hive, Presto, Pig)
AWS Glue
Amazon Kinesis
Amazon QuickSight
Amazon Macie
AWS Organizations
AWS Cloud Platform
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