This document discusses machine learning and deep learning concepts like convolutional neural networks. It provides an overview of ML.NET, an open source machine learning framework, and shows how to build and train models with ML.NET including training a deep learning model to classify images into categories like rock, paper, or scissors. Examples of loading data, defining the model architecture, training the model, exporting it and using it for predictions are provided.
Slides for my session at Virtual ML.NET Conference about developing an image recognition machine learning model for a rock-paper-scissors mobile game with ML.NET and Xamarin
Elasticsearch : petit déjeuner du 13 mars 2014ALTER WAY
Elasticsearch est un moteur de recherche Open Source très puissant basé sur
Apache Lucene. Il permet l'indexation de millions de données, leur recherche et leur
analyse en temps réel. Les outils Elascticsearch sont déjà utilisés par des acteurs de
référence tels que FourSquare, GitHub, OpenDataSoft ou encore Dailymotion.
Alter Way et Elasticsearch vous convient à venir découvrir la suite Elasticsearch
enfin disponible en version 1.0 et prête pour la production !
Funnel Analysis with Apache Spark and DruidDatabricks
Every day, millions of advertising campaigns are happening around the world.
As campaign owners, measuring the ongoing campaign effectiveness (e.g “how many distinct users saw my online ad VS how many distinct users saw my online ad, clicked it and purchased my product?”) is super important.
However, this task (often referred to as “funnel analysis”) is not an easy task, especially if the chronological order of events matters.
One way to mitigate this challenge is combining Apache Druid and Apache DataSketches, to provide fast analytics on large volumes of data.
However, while that combination can answer some of these questions, it still can’t answer the question “how many distinct users viewed the brand’s homepage FIRST and THEN viewed product X page?”
In this talk, we will discuss how we combine Spark, Druid and DataSketches to answer such questions at scale.
Slides for my session at Virtual ML.NET Conference about developing an image recognition machine learning model for a rock-paper-scissors mobile game with ML.NET and Xamarin
Elasticsearch : petit déjeuner du 13 mars 2014ALTER WAY
Elasticsearch est un moteur de recherche Open Source très puissant basé sur
Apache Lucene. Il permet l'indexation de millions de données, leur recherche et leur
analyse en temps réel. Les outils Elascticsearch sont déjà utilisés par des acteurs de
référence tels que FourSquare, GitHub, OpenDataSoft ou encore Dailymotion.
Alter Way et Elasticsearch vous convient à venir découvrir la suite Elasticsearch
enfin disponible en version 1.0 et prête pour la production !
Funnel Analysis with Apache Spark and DruidDatabricks
Every day, millions of advertising campaigns are happening around the world.
As campaign owners, measuring the ongoing campaign effectiveness (e.g “how many distinct users saw my online ad VS how many distinct users saw my online ad, clicked it and purchased my product?”) is super important.
However, this task (often referred to as “funnel analysis”) is not an easy task, especially if the chronological order of events matters.
One way to mitigate this challenge is combining Apache Druid and Apache DataSketches, to provide fast analytics on large volumes of data.
However, while that combination can answer some of these questions, it still can’t answer the question “how many distinct users viewed the brand’s homepage FIRST and THEN viewed product X page?”
In this talk, we will discuss how we combine Spark, Druid and DataSketches to answer such questions at scale.
Quantum computing takes a giant leap forward from today’s technology—one that will forever alter our economic, industrial, academic, and societal landscape. This has massive implications for your customers in any industry including healthcare, energy, environmental systems, smart materials, and more. Learn how Microsoft is taking a unique revolutionary approach to quantum and how your customers can get started developing quantum solutions with the Quantum Development Kit.
Slides de mi Conferencia: We Are Digital Puppets Actualizada (Inglés) que dicté en San Francisco CA. Hablo sobre el Tracking y el profiling de personas.
Creating a custom Machine Learning Model for your applications - Java Dev Day...Isabel Palomar
Aprenderás como puede ser creado un modelo de Machine Learning que puedas implementar en tu aplicación móvil o Java. Iré mostrando cada uno de los pasos que se tienen que seguir, los tipos de problemas que se pueden resolver, los datos que necesitas para que funcione y por último, las opciones para realizar la implementación de nuestro modelo en nuestras aplicaciones.
Boost your online e commerce with magnoliaMagnolia
This talk was given by Maurizio Sofo, Tinext, at Magnolia Conference 2015 in Basel, Switzerland
Tinext recently launched the new portal for Eolo, the largest independent Italian wireless network, owned by NGI, Italian Company, leader in broadband field. NGI chose Magnolia to address the new corporate identity strategy (from NGI to Eolo) and to enhance their websites e-commerce experience whilst simplifying the information access and optimizing the mobile navigation for both private customers (B2C commerce) and sales partners (B2B commerce).
Key points of project, developed on Magnolia, are: easy and secure purchase in a few click for private customers, complete self-management of the price plan for Magnolia publishers, strong integration with internal NGI systems for purchase, CRM, network, call center and self-care processes; tracking e-commerce and conversion rate by using Google Analytics; users profiling and home personalization of section both for contents and for functionalities (i.e. Customer Journey Map); Search Engine Optimization (SEO) for site (rebranding) and domain migration.
Ai Tech Summit Closing Keynote: How to Launch An Exponential Ai Tech Startup ...Christine Souffrant Ntim
To accelerate developments within the Ai tech industry, the Global Startup Ecosystem is to host the first annual Ai Tech Summit on November 28, 2018 featured at Galvanize, New York.
The Ai Tech Summit is the world’s most exclusive event featuring Ai tech investors, entrepreneurs and influencers coming together to address the world’s greatest challenges via Ai technology. The summit also serves to teach fundamentals of Ai technology, leading applications of Ai, and startup development strategies via exclusive workshop sessions with award winning experts. The program concludes with a high profile call to action VIP gathering speakers and partners.
The 2018 Ai Tech Summit will achieve this by covering three experience areas.
Part one is a fast track covering the latest tech developments and trends Ai .
Part two is a fast track of intense workshops and keynote sessions with the goal of teaching the basics of startup development, investment and ecosystem building within Ai networks.
Part Three is an exclusive VIP networking session with round tables with speakers, experts, investors and ecosystem leaders.
This keynote was presented by Christine Souffrant Ntim: Christine Souffrant Ntim is an award-winning Haitian-American & Ghanaian, entrepreneur & startup ecosystem expert for emerging markets. She was selected and featured in Forbes 30 Under 30, AdAge 40 Under 40, Haiti Changemakers 1804 List, Singularity NASA, Entrepreneur Magazine, Huffington Post, Inc Magazine and more. She speaks on digital entrepreneurship, startup hacking, exponential tech AI, and personal branding at over 20+ global conferences a year- which includes former appearances at the US State Department Tours, United Nations, TEDx, SXSW, Startup Grind Global, SeedStars World, European Union Forum and more. She started her career as the founder of Vendedy- a social network connecting people to street markets with the aim of digitizing a $10 trillion dollar black economy and centralizing the world’s 200,000 street markets. Today, Christine is the Director of Startup Grind Dubai Powered by Google For Entrepreneurs and a partner at the GlobalStartupEcosystem.com which hosts the largest digital online accelerator program in the world- graduating over 1000+ companies across 190+ countries a year.
🚀 Exciting New Business Opportunities in Emerging Technologies! 🌟
Are you ready to ride the wave of innovation? 💡 From AI to blockchain, and everything in between, emerging technologies are reshaping industries and creating endless possibilities for entrepreneurs. 🌐💼
🤖 AI and Machine Learning: Harness the power of data-driven insights and automation to revolutionize businesses and enhance customer experiences. #AI #MachineLearning
🔗 Blockchain and Cryptocurrency: Explore the decentralized world of blockchain technology, offering secure transactions and disrupting traditional finance models. #Blockchain #Cryptocurrency
📱 AR/VR and Immersive Experiences: Dive into the realm of augmented and virtual reality, transforming how we interact with content and engage with brands. #AR #VR
💻 Cybersecurity: Safeguard businesses and individuals against cyber threats, with a growing demand for innovative solutions in an increasingly digital world. #Cybersecurity
💡 IoT and Smart Devices: Connect the unconnected with the Internet of Things, enabling smarter cities, homes, and businesses through interconnected devices. #IoT #SmartDevices
💬 Emojis and Digital Communication: Explore the expressive world of emojis, shaping the way we communicate online and presenting unique branding and marketing opportunities. #Emojis #DigitalCommunication
🚀 Don't miss out on the chance to seize these exciting new opportunities and shape the future of technology-driven businesses! 💼💫 #TechTrends #Entrepreneurship #Innovation
Let's spark some conversations and dive into the possibilities! 💬✨
Machine Learning Tokyo - Deep Neural Networks for Video - NumberBoostAlex Conway
Slides from a talk I gave at the Machine Learning Tokyo meetup group on 20190318.
More info here: https://www.meetup.com/Machine-Learning-Tokyo/events/259467268/
Feel free to reach out if you ever need to build a computer vision system or need data labelled to train machine learning models :)
www.numberboost.com
Do you understand the differences between pattern recognition, artificial intelligence and machine learning? And most important, what they separately bring to the table? In this week’s webinar we will tackle the terminology and discuss its recent explosion of popularity, and also look at how the Ogilvy analytics team has applied machine learning methods to effectively answer client challenges and drive value.
Applied Data Science: Building a Beer Recommender | Data Science MD - Oct 2014Austin Ogilvie
Applied Data Science: Building a Beer Recommender | Data Science MD - Oct 2014
-----------
Slides from a talk by Greg Lamp, CTO of Yhat, about building recommendation systems using Python and deploying them to production.
81819, 957 PMPrintPage 1 of 43httpscontent.ashford.e.docxblondellchancy
8/18/19, 9'57 PMPrint
Page 1 of 43https://content.ashford.edu/print/Valacich.3917.17.1?sections=ch0…&clientToken=a3e9dfb8-8e7d-6865-d886-be0e64bd158d&np=ch01lev1sec1
9 Developing and Acquiring Information Systems
After reading this chapter, you will be able to do the following:
1. Describe how to formulate and present the business case for technology investments.
2. Describe the systems development life cycle and its various phases.
3. Explain how organizations acquire systems via external acquisition and outsourcing.
Preview
As you have read throughout this book and have experienced in your own life, information systems and technologies are of many different types, including high-speed
Web servers to rapidly process customer requests, business intelligence systems to aid managerial decision making, and customer relationship management systems to
provide improved customer service. Given this variety, when we refer to “systems” in this chapter, we are talking about a broad range of technologies, including
hardware, software, and services. Just as there are different types of systems, there are different approaches for developing and acquiring them. If you are a business
student majoring in areas such as marketing, finance, accounting, or management, you might be wondering why we have a discussion about developing and acquiring
information systems. The answer is simple: No matter what area of an organization you are in, you will be involved in systems development or technology acquisition
processes. In fact, research indicates that spending on systems in many organizations is controlled by the specific business functions rather than by the information
systems (IS) department. What this means is that even if your career interests are in something other than information systems, it is very likely that you will be
involved in the development and acquisition of systems, technologies, or services. Understanding this process is important to your future success.
Managing in the Digital World: Microsoft Is “Kinecting” Its Ecosystem
How useful would an iPhone or an Android smartphone be without the apps? How useful would a Blu-ray player be without a large selection of movies available in that
format? The value of many devices or systems grows with the size of their ecosystems, including the users, application or content developers, sellers, and marketplaces. Like
a tree standing still in a world without rain, birds, or flowers—a tree that would likely not be able to survive—the iPhone sans the “apps” would be much less useful, less
exciting, and much less successful in the marketplace. Similarly, Google, Microsoft, and, not surprisingly, Amazon.com (http://Amazon.com) are trying to build large
ecosystems around their products and services (Figure 9.1 (http://content.thuzelearning.com/books/Valacich.3917.17.1/sections/ch09#ch09fig1) ).
FIGURE 9.1 All parts of an ecosystem are interrelated.
http://amazon.com/
https://content.ashford.edu/books/Va ...
An indepth look at Google BigQuery Architecture by Felipe Hoffa of GoogleData Con LA
Abstract:- Come learn about Google BigQuery and its underlying architecture. Felipe will go over the evolution of BigQuery and explain some of the underlying principles of BigQuery and Dremel. Felipe will also go over some of the latest use cases and will demo a use case of Google BigQuery
Bio:-
Felipe Hoffa moved from Chile to San Francisco to join Google as a Software Engineer. Since 2013 he's been a Developer Advocate on big data - to inspire developers around the world to leverage the Google Cloud Platform tools to analyze and understand their data in ways they could never before. You can find him in several YouTube videos, blog posts, and conferences around the world.
Follow Felipe at https://twitter.com/felipehoffa.
Scott Brinker - Democratizing Martech - The Rise of Citizen Developers & Data...Boye & Co
In the past marketers had to go through IT for the marketing tools they need. Today marketing technology is widespread, easy to adopt and use. It’s been taken out of the hands of IT, and placed into the hands of practically everyone in the organization.
This keynote explores the possibilities and challenges that new MarTech - with the introduction of artificial intelligence - is giving marketers.
Quantum computing takes a giant leap forward from today’s technology—one that will forever alter our economic, industrial, academic, and societal landscape. This has massive implications for your customers in any industry including healthcare, energy, environmental systems, smart materials, and more. Learn how Microsoft is taking a unique revolutionary approach to quantum and how your customers can get started developing quantum solutions with the Quantum Development Kit.
Slides de mi Conferencia: We Are Digital Puppets Actualizada (Inglés) que dicté en San Francisco CA. Hablo sobre el Tracking y el profiling de personas.
Creating a custom Machine Learning Model for your applications - Java Dev Day...Isabel Palomar
Aprenderás como puede ser creado un modelo de Machine Learning que puedas implementar en tu aplicación móvil o Java. Iré mostrando cada uno de los pasos que se tienen que seguir, los tipos de problemas que se pueden resolver, los datos que necesitas para que funcione y por último, las opciones para realizar la implementación de nuestro modelo en nuestras aplicaciones.
Boost your online e commerce with magnoliaMagnolia
This talk was given by Maurizio Sofo, Tinext, at Magnolia Conference 2015 in Basel, Switzerland
Tinext recently launched the new portal for Eolo, the largest independent Italian wireless network, owned by NGI, Italian Company, leader in broadband field. NGI chose Magnolia to address the new corporate identity strategy (from NGI to Eolo) and to enhance their websites e-commerce experience whilst simplifying the information access and optimizing the mobile navigation for both private customers (B2C commerce) and sales partners (B2B commerce).
Key points of project, developed on Magnolia, are: easy and secure purchase in a few click for private customers, complete self-management of the price plan for Magnolia publishers, strong integration with internal NGI systems for purchase, CRM, network, call center and self-care processes; tracking e-commerce and conversion rate by using Google Analytics; users profiling and home personalization of section both for contents and for functionalities (i.e. Customer Journey Map); Search Engine Optimization (SEO) for site (rebranding) and domain migration.
Ai Tech Summit Closing Keynote: How to Launch An Exponential Ai Tech Startup ...Christine Souffrant Ntim
To accelerate developments within the Ai tech industry, the Global Startup Ecosystem is to host the first annual Ai Tech Summit on November 28, 2018 featured at Galvanize, New York.
The Ai Tech Summit is the world’s most exclusive event featuring Ai tech investors, entrepreneurs and influencers coming together to address the world’s greatest challenges via Ai technology. The summit also serves to teach fundamentals of Ai technology, leading applications of Ai, and startup development strategies via exclusive workshop sessions with award winning experts. The program concludes with a high profile call to action VIP gathering speakers and partners.
The 2018 Ai Tech Summit will achieve this by covering three experience areas.
Part one is a fast track covering the latest tech developments and trends Ai .
Part two is a fast track of intense workshops and keynote sessions with the goal of teaching the basics of startup development, investment and ecosystem building within Ai networks.
Part Three is an exclusive VIP networking session with round tables with speakers, experts, investors and ecosystem leaders.
This keynote was presented by Christine Souffrant Ntim: Christine Souffrant Ntim is an award-winning Haitian-American & Ghanaian, entrepreneur & startup ecosystem expert for emerging markets. She was selected and featured in Forbes 30 Under 30, AdAge 40 Under 40, Haiti Changemakers 1804 List, Singularity NASA, Entrepreneur Magazine, Huffington Post, Inc Magazine and more. She speaks on digital entrepreneurship, startup hacking, exponential tech AI, and personal branding at over 20+ global conferences a year- which includes former appearances at the US State Department Tours, United Nations, TEDx, SXSW, Startup Grind Global, SeedStars World, European Union Forum and more. She started her career as the founder of Vendedy- a social network connecting people to street markets with the aim of digitizing a $10 trillion dollar black economy and centralizing the world’s 200,000 street markets. Today, Christine is the Director of Startup Grind Dubai Powered by Google For Entrepreneurs and a partner at the GlobalStartupEcosystem.com which hosts the largest digital online accelerator program in the world- graduating over 1000+ companies across 190+ countries a year.
🚀 Exciting New Business Opportunities in Emerging Technologies! 🌟
Are you ready to ride the wave of innovation? 💡 From AI to blockchain, and everything in between, emerging technologies are reshaping industries and creating endless possibilities for entrepreneurs. 🌐💼
🤖 AI and Machine Learning: Harness the power of data-driven insights and automation to revolutionize businesses and enhance customer experiences. #AI #MachineLearning
🔗 Blockchain and Cryptocurrency: Explore the decentralized world of blockchain technology, offering secure transactions and disrupting traditional finance models. #Blockchain #Cryptocurrency
📱 AR/VR and Immersive Experiences: Dive into the realm of augmented and virtual reality, transforming how we interact with content and engage with brands. #AR #VR
💻 Cybersecurity: Safeguard businesses and individuals against cyber threats, with a growing demand for innovative solutions in an increasingly digital world. #Cybersecurity
💡 IoT and Smart Devices: Connect the unconnected with the Internet of Things, enabling smarter cities, homes, and businesses through interconnected devices. #IoT #SmartDevices
💬 Emojis and Digital Communication: Explore the expressive world of emojis, shaping the way we communicate online and presenting unique branding and marketing opportunities. #Emojis #DigitalCommunication
🚀 Don't miss out on the chance to seize these exciting new opportunities and shape the future of technology-driven businesses! 💼💫 #TechTrends #Entrepreneurship #Innovation
Let's spark some conversations and dive into the possibilities! 💬✨
Machine Learning Tokyo - Deep Neural Networks for Video - NumberBoostAlex Conway
Slides from a talk I gave at the Machine Learning Tokyo meetup group on 20190318.
More info here: https://www.meetup.com/Machine-Learning-Tokyo/events/259467268/
Feel free to reach out if you ever need to build a computer vision system or need data labelled to train machine learning models :)
www.numberboost.com
Do you understand the differences between pattern recognition, artificial intelligence and machine learning? And most important, what they separately bring to the table? In this week’s webinar we will tackle the terminology and discuss its recent explosion of popularity, and also look at how the Ogilvy analytics team has applied machine learning methods to effectively answer client challenges and drive value.
Applied Data Science: Building a Beer Recommender | Data Science MD - Oct 2014Austin Ogilvie
Applied Data Science: Building a Beer Recommender | Data Science MD - Oct 2014
-----------
Slides from a talk by Greg Lamp, CTO of Yhat, about building recommendation systems using Python and deploying them to production.
81819, 957 PMPrintPage 1 of 43httpscontent.ashford.e.docxblondellchancy
8/18/19, 9'57 PMPrint
Page 1 of 43https://content.ashford.edu/print/Valacich.3917.17.1?sections=ch0…&clientToken=a3e9dfb8-8e7d-6865-d886-be0e64bd158d&np=ch01lev1sec1
9 Developing and Acquiring Information Systems
After reading this chapter, you will be able to do the following:
1. Describe how to formulate and present the business case for technology investments.
2. Describe the systems development life cycle and its various phases.
3. Explain how organizations acquire systems via external acquisition and outsourcing.
Preview
As you have read throughout this book and have experienced in your own life, information systems and technologies are of many different types, including high-speed
Web servers to rapidly process customer requests, business intelligence systems to aid managerial decision making, and customer relationship management systems to
provide improved customer service. Given this variety, when we refer to “systems” in this chapter, we are talking about a broad range of technologies, including
hardware, software, and services. Just as there are different types of systems, there are different approaches for developing and acquiring them. If you are a business
student majoring in areas such as marketing, finance, accounting, or management, you might be wondering why we have a discussion about developing and acquiring
information systems. The answer is simple: No matter what area of an organization you are in, you will be involved in systems development or technology acquisition
processes. In fact, research indicates that spending on systems in many organizations is controlled by the specific business functions rather than by the information
systems (IS) department. What this means is that even if your career interests are in something other than information systems, it is very likely that you will be
involved in the development and acquisition of systems, technologies, or services. Understanding this process is important to your future success.
Managing in the Digital World: Microsoft Is “Kinecting” Its Ecosystem
How useful would an iPhone or an Android smartphone be without the apps? How useful would a Blu-ray player be without a large selection of movies available in that
format? The value of many devices or systems grows with the size of their ecosystems, including the users, application or content developers, sellers, and marketplaces. Like
a tree standing still in a world without rain, birds, or flowers—a tree that would likely not be able to survive—the iPhone sans the “apps” would be much less useful, less
exciting, and much less successful in the marketplace. Similarly, Google, Microsoft, and, not surprisingly, Amazon.com (http://Amazon.com) are trying to build large
ecosystems around their products and services (Figure 9.1 (http://content.thuzelearning.com/books/Valacich.3917.17.1/sections/ch09#ch09fig1) ).
FIGURE 9.1 All parts of an ecosystem are interrelated.
http://amazon.com/
https://content.ashford.edu/books/Va ...
An indepth look at Google BigQuery Architecture by Felipe Hoffa of GoogleData Con LA
Abstract:- Come learn about Google BigQuery and its underlying architecture. Felipe will go over the evolution of BigQuery and explain some of the underlying principles of BigQuery and Dremel. Felipe will also go over some of the latest use cases and will demo a use case of Google BigQuery
Bio:-
Felipe Hoffa moved from Chile to San Francisco to join Google as a Software Engineer. Since 2013 he's been a Developer Advocate on big data - to inspire developers around the world to leverage the Google Cloud Platform tools to analyze and understand their data in ways they could never before. You can find him in several YouTube videos, blog posts, and conferences around the world.
Follow Felipe at https://twitter.com/felipehoffa.
Scott Brinker - Democratizing Martech - The Rise of Citizen Developers & Data...Boye & Co
In the past marketers had to go through IT for the marketing tools they need. Today marketing technology is widespread, easy to adopt and use. It’s been taken out of the hands of IT, and placed into the hands of practically everyone in the organization.
This keynote explores the possibilities and challenges that new MarTech - with the introduction of artificial intelligence - is giving marketers.
Similar to 03 GlobalAIBootcamp2020Lisboa-Rock, Paper, Scissors.pptx (20)
Real NET Docs Show - Serverless Machine Learning v3.pptxLuis Beltran
Slides of my presentation about Serverless Machine Learning using Azure Functions, Twilio APIs, and Cognitive Services for text and image processing of WhatsApp messages at .NET Docs Show weekly community event organized by Microsoft
Latam Space Week - Clasificación de rocas espaciales por medio de IA.pptxLuis Beltran
Slides of my presentation about Space rocks image classification using Machine Learning and Artificial Intelligence with Python at Latam Space Week event
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.
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.
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
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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.
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
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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/
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.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
5. #GlobalAILisboa #GlobalAIBootcamp #GlobalAICommunity
DeepLearning
• Deeplearning is a subfield of Machine Learning concernedwith
algorithms inspired by the structure and function of the brain
called artificial neuralnetworks.
• It is exceptionally effective in discovering patterns.
• Algorithms learn through a multi-layered hierarchy.
• If you supply the system with tons of information, it will begin to
understand and respond in helpful ways.
6. #GlobalAILisboa #GlobalAIBootcamp #GlobalAICommunity
Deep learning hasan inbuilt automatic multi stage feature learning process that learns rich
hierarchicalrepresentations (i.e. features).
Low-level
features
Mid-level
features
Output (e.g. exterior,
interior)
High-level
features
Trainable
Classifier
7. #GlobalAILisboa #GlobalAIBootcamp #GlobalAICommunity
Image
Pixel EdgeTexture Motif Part Object
Text
Character Word Word-group Clause Sentence Story
Each module in DeepLearning transforms its input representation into a higher-level one, in a way similar to human
cortex.
Low Level
Features
Mid Level
Features Output
High
Level
Features
Trainable
Classifier
Input
11. #GlobalAILisboa #GlobalAIBootcamp #GlobalAICommunity
Pooling
Max pooling: reports the maximum output within a rectangular neighborhood.
Average pooling: reports the average output of a rectangular neighborhood.
1 3 5 3
4 2 3 1
3 1 1 3
0 1 0 4
MaxPool with 2X2 filter with
stride of 2
Input Matrix Output Matrix
4 5
3 4
16. #GlobalAILisboa #GlobalAIBootcamp #GlobalAICommunity
MLContext
Starting point for all ML.NET operations which provides mechanisms to create components for:
• Data preparation
• Feature Engineering
• Training
• Prediction
• Model Validation
• Logging
• Execution control
• Initialization
26. #GlobalAILisboa #GlobalAIBootcamp #GlobalAICommunity
Main program
Loading data for
supervised learning
(images include tags) Training and Validation sets
Load pipeline:
Images loaded in memory
Training options:
ImageClassificationTrainer
chosen, based on the
InceptionV3 architecture
Training pipeline:
Trying to predict a
category
Both pipelines are combined
27. #GlobalAILisboa #GlobalAIBootcamp #GlobalAICommunity
Perform training
Model precision is validated
using validation dataset
Model Metrics calculated
Test the classification model using the new images
Prepare new images for prediction
Export the model
Consume the model
37. #GlobalAILisboa #GlobalAIBootcamp #GlobalAICommunity
Unable to find an entry point named 'TF_StringEncodedSize' in DLL 'tensorflow'
“I think ml.net support tensorflow 2.3.1 not yet support 2.4,
so you must download SciSharp.TensorFlow.Redist 2.3.1”
https://github.com/dotnet/machinelearning-samples/issues/880
Deep learning can be considered as a subset of machine learning. It is a field that is based on learning and improving on its own by examining computer algorithms. While machine learning uses simpler concepts, deep learning works with artificial neural networks, which are designed to imitate how humans think and learn. Until recently, neural networks were limited by computing power and thus were limited in complexity. However, advancements in Big Data analytics have permitted larger, sophisticated neural networks, allowing computers to observe, learn, and react to complex situations faster than humans. Deep learning has aided image classification, language translation, speech recognition. It can be used to solve any pattern recognition problem and without human intervention.
Artificial neural networks, comprising many layers, drive deep learning. Deep Neural Networks (DNNs) are such types of networks where each layer can perform complex operations such as representation and abstraction that make sense of images, sound, and text.
Low-level features are minor details of the image, like lines or dots, that can be picked up by, say, a convolutional filter (for really low-level things) or (for more abstract things like edges). High-level features are built on top of low-level features to detect objects and larger shapes in the image.
Convolutional neural networks use both types of features: the first couple convolutional layers will learn filters for finding lines, dots, curves etc. while the later layers will learn to recognize common objects and shapes.
Convolution is a general purpose filter effect for images
In Convolutional Neural Networks, Filters detect spatial patterns such as edges in an image by detecting the changes in intensity values of the image.
In terms of an image, a high-frequency image is the one where the intensity of the pixels changes by a large amount, whereas a low-frequency image is the one where the intensity is almost uniform. Usually, an image has both high and low frequency components. The high-frequency components correspond to the edges of an object because at the edges the rate of change of intensity of pixel values is high.
Convolution is a simple mathematical operation which is fundamental to many common image processing operators. Convolution provides a way of `multiplying together' two arrays of numbers, generally of different sizes, but of the same dimensionality, to produce a third array of numbers of the same dimensionality.
3. Now when you apply a set of filters on top of that (pass it through the 2nd conv. layer), the output will be activations that represent higher-level features. Types of these features could be semicircles (a combination of a curve and straight edge) or squares (a combination of several straight edges). As you go through the network and go through more conv. layers, you get activation maps that represent more and more complex features.
Es el conjunto de columnas, sus nombres, tipos y otras anotaciones.
Antes de cargar datos, debes definir cómo se verá el esquema de datos (nombres y tipos de columna)
Utiliza definiciones de clase para definir esquemas IDV
Pooling layers provide an approach to down sampling feature maps by summarizing the presence of features in patches of the feature map. Two common pooling methods are average pooling and max pooling that summarize the average presence of a feature and the most activated presence of a feature respectively.
A pooling layer is a new layer added after the convolutional layer. Specifically, after a nonlinearity (e.g. ReLU) h