This document discusses foundational aspects of computer vision from a systems perspective. It contrasts computer vision with machine learning, theoretical analysis with experimental approaches, quality versus speed tradeoffs, automatic versus semi-automatic methods, the roles of algorithms and sensors, and how human input can be optimally utilized beyond traditional active learning. The goal is to provide a holistic view of computer vision that considers all aspects of designing and building complete vision systems.
Machine Learning without the Math: An overview of Machine LearningArshad Ahmed
A brief overview of Machine Learning and its associated tasks from a high level. This presentation discusses key concepts without the maths.The more mathematically inclined are referred to Bishops book on Pattern Recognition and Machine Learning.
The only way our model can perform at its best if it understands our data the best. Most algorithms only understand numeric data but in practical life that's impossible for us to have every feature in numeric form. This presentation will take you all through various techniques by which various types of features can be handled.
Machine Learning without the Math: An overview of Machine LearningArshad Ahmed
A brief overview of Machine Learning and its associated tasks from a high level. This presentation discusses key concepts without the maths.The more mathematically inclined are referred to Bishops book on Pattern Recognition and Machine Learning.
The only way our model can perform at its best if it understands our data the best. Most algorithms only understand numeric data but in practical life that's impossible for us to have every feature in numeric form. This presentation will take you all through various techniques by which various types of features can be handled.
الموعد الإثنين 03 يناير 2022
143
مبادرة
#تواصل_تطوير
المحاضرة ال 143 من المبادرة
المهندس / محمد الرافعي طرباي
نقيب المبرمجين بالدقهلية
بعنوان
"IT INDUSTRY"
How To Getting Into IT With Zero Experience
وذلك يوم الإثنين 03 يناير2022
السابعة مساء توقيت القاهرة
الثامنة مساء توقيت مكة المكرمة
و الحضور من تطبيق زووم
https://us02web.zoom.us/meeting/register/tZUpf-GsrD4jH9N9AxO39J013c1D4bqJNTcu
علما ان هناك بث مباشر للمحاضرة على القنوات الخاصة بجمعية المهندسين المصريين
ونأمل أن نوفق في تقديم ما ينفع المهندس ومهمة الهندسة في عالمنا العربي
والله الموفق
للتواصل مع إدارة المبادرة عبر قناة التليجرام
https://t.me/EEAKSA
ومتابعة المبادرة والبث المباشر عبر نوافذنا المختلفة
رابط اللينكدان والمكتبة الالكترونية
https://www.linkedin.com/company/eeaksa-egyptian-engineers-association/
رابط قناة التويتر
https://twitter.com/eeaksa
رابط قناة الفيسبوك
https://www.facebook.com/EEAKSA
رابط قناة اليوتيوب
https://www.youtube.com/user/EEAchannal
رابط التسجيل العام للمحاضرات
https://forms.gle/vVmw7L187tiATRPw9
ملحوظة : توجد شهادات حضور مجانية لمن يسجل فى رابط التقيم اخر المحاضرة
Machine Learning 2 deep Learning: An IntroSi Krishan
Provides a brief introduction to machine learning, reasons for its popularity, a simple walk through example and then a need for deep learning and some of its characteristics. This is an updated version of an earlier presentation.
A DEEP LEARNING APPROACH FOR SEMANTIC SEGMENTATION IN BRAIN TUMOR IMAGESPNandaSai
Digital image processing is vast fields which can be using various applications. Which include Detection of criminal face, fingerprint authentication system, in medical field, object recognition etc. Brain tumor detection plays an important role in medical field. Brain tumor detection is detection of tumor affected part in the brain along with its shape size and boundary, so it useful in medical field.
Segmentation and the subsequent quantitative assessment of lesions in medical images provide valuable information for the analysis of neuropathologist and are important for planning of treatment strategies, monitoring of disease progression and prediction of patient outcome. For a better understanding of the pathophysiology of diseases, quantitative imaging can reveal clues about the disease characteristics and effects on particular anatomical structures
Webinar: Machine Learning para MicrocontroladoresEmbarcados
Neste webinar, serão apresentados conceitos sobre inteligência artificial, assim como ferramentas disponíveis para o desenvolvimento integradas ao MPLAB X e ao Harmony 3 e demonstração de um sistema de detecção de anomalia utilizando um microcontrolador da família ATSAMD21 (ARM Cortex M0+).
Keynote presentation from ECBS conference. The talk is about how to use machine learning and AI in improving software engineering. Experiences from our project in Software Center (www.software-center.se).
Machine learning has become an important tool in the modern software toolbox, and high-performing organizations are increasingly coming to rely on data science and machine learning as a core part of their business. eBay introduced machine learning to its commerce search ranking and drove double-digit increases in revenue. Stitch Fix built a multibillion dollar clothing retail business in the US by combining the best of machines with the best of humans. And WeWork is bringing machine-learned approaches to the physical office environment all around the world. In all cases, algorithmic techniques started simple and slowly became more sophisticated over time. This talk will use these examples to derive an agile approach to machine learning, and will explore that approach across several different dimensions. We will set the stage by outlining the kinds of problems that are most amenable to machine-learned approaches as well as describing some important prerequisites, including investments in data quality, a robust data pipeline, and experimental discipline. Next, we will choose the right (algorithmic) tool for the right job, and suggest how to incrementally evolve the algorithmic approaches we bring to bear. Most fancy cutting-edge recommender systems in the real world, for example, started out with simple rules-based techniques or basic regression. Finally, we will integrate machine learning into the broader product development process, and see how it can help us to accelerate business results
Rise of the machines -- Owasp israel -- June 2014 meetupShlomo Yona
Rise of the machines -- Owasp israel -- June 2014 meetup
Shlomo Yona presents why it is a good idea to use Machine Learning in Security and explains some Machine Learning jargon and demonstraits with two fingerprinting examples: a wifi device (PHY) and a browser (L7)
AWS re:Invent 2016: Getting to Ground Truth with Amazon Mechanical Turk (MAC201)Amazon Web Services
Jump-start your machine learning project by using the crowd to build your training set. Before you can train your machine learning algorithm, you need to take your raw inputs and label, annotate, or tag them to build your ground truth. Learn how to use the Amazon Mechanical Turk marketplace to perform these tasks. We share Amazon's best practices, developed while training our own machine learning algorithms, and walk you through quickly getting affordable and high-quality training data.
The Fine Art of Combining Capacity Management with Machine LearningPrecisely
Today, capacity management within the enterprise continues to evolve. In the past, we were focused on the hardware – but now we are focused on the services. With that in mind, the amount of data available has increased significantly and has become difficult for individuals to sort through.
It is apparent that to be successful in this discipline, we need the machines to do more of the heavy lifting. This includes automatically creating reports, calling out anomalies and producing forecasts. The intuition of the human computer is imperative to the success.
View this webinar on-demand where we discuss:
• The strengths and weaknesses of capacity management with and without machine learning
• What machine learning can provide throughout the process
• The benefits of using capacity management and machine learning within your organization
Mobile Recommendation Engine
collaborative filtering and content based approach in hybrid manner then Genetic Algorithm for Enhancement of the Recommendation Engine. by this marketers also will get the unique characteristics of the product that must be created and also recommend to the user.
Big data is set to offer tremendous insight. But with terabytes and petabytes of data pouring in to organizations today, traditional architectures and infrastructures are not up to the challenge. This begs the question: How do you present big data in a way that can be quickly understood and used? These data present tremendous opportunities in data mining, a burgeoning field in computer science that focuses on the development of methods that can extract knowledge from data. In many real world problems, data mining algorithms have access to massive amounts of data. Mining all the available data is prohibitive due to computational (time and memory) constraints. Much of the current research is concerned with scaling up data mining algorithms (i.e. improving on existing data mining algorithms for larger datasets). An alternative approach is to scale down the data. Thus, determining a smallest sufficient training set size that obtains the same accuracy as the entire available dataset remains an important research question. Our research focuses on selecting how many (sampling) instances to present to the data mining algorithm and also how to improve the quality of the data.
Dr. Ashwin Satyanarayana is an Assistant Professor in the Computer Systems Technology department at CityTech. Prior to joining CityTech, Ashwin was a Research Scientist at Microsoft, where he worked on several Big Data problems including Query Reformulation on Microsoft's search engine Bing. Ashwin's prior experience also includes a Senior Research Scientist on the area of Location Analytics at Placed Inc. He holds a PhD in Computer Science (Data Mining) from SUNY, with particular emphasis on Data Mining, Machine Learning and Applied Probability with applications in Real World Learning Problems.
ATI Courses Professional Development Short Course Applied Measurement Engin...Jim Jenkins
How do you know your test measurements are valid? Since NIST traceability actually guarantees little about your test data, how do you know? Could you prove validity to your customer? What is the right measurements solution for your testing requirements? Is it really as simple as the vendors say? What is your real cost of invalid, ambiguous data causing retest or, worst of all, hardware redesign?
This course is for engineers, scientists, and managers who must use systems to understand experimental test measurements on a daily basis. Learn how to design, buy and operate effective automated measurement systems providing demonstrably valid test data, the first time.
Fundamental & underlying engineering principles governing the design and operation of effective automated systems are demonstrated experimentally.
الموعد الإثنين 03 يناير 2022
143
مبادرة
#تواصل_تطوير
المحاضرة ال 143 من المبادرة
المهندس / محمد الرافعي طرباي
نقيب المبرمجين بالدقهلية
بعنوان
"IT INDUSTRY"
How To Getting Into IT With Zero Experience
وذلك يوم الإثنين 03 يناير2022
السابعة مساء توقيت القاهرة
الثامنة مساء توقيت مكة المكرمة
و الحضور من تطبيق زووم
https://us02web.zoom.us/meeting/register/tZUpf-GsrD4jH9N9AxO39J013c1D4bqJNTcu
علما ان هناك بث مباشر للمحاضرة على القنوات الخاصة بجمعية المهندسين المصريين
ونأمل أن نوفق في تقديم ما ينفع المهندس ومهمة الهندسة في عالمنا العربي
والله الموفق
للتواصل مع إدارة المبادرة عبر قناة التليجرام
https://t.me/EEAKSA
ومتابعة المبادرة والبث المباشر عبر نوافذنا المختلفة
رابط اللينكدان والمكتبة الالكترونية
https://www.linkedin.com/company/eeaksa-egyptian-engineers-association/
رابط قناة التويتر
https://twitter.com/eeaksa
رابط قناة الفيسبوك
https://www.facebook.com/EEAKSA
رابط قناة اليوتيوب
https://www.youtube.com/user/EEAchannal
رابط التسجيل العام للمحاضرات
https://forms.gle/vVmw7L187tiATRPw9
ملحوظة : توجد شهادات حضور مجانية لمن يسجل فى رابط التقيم اخر المحاضرة
Machine Learning 2 deep Learning: An IntroSi Krishan
Provides a brief introduction to machine learning, reasons for its popularity, a simple walk through example and then a need for deep learning and some of its characteristics. This is an updated version of an earlier presentation.
A DEEP LEARNING APPROACH FOR SEMANTIC SEGMENTATION IN BRAIN TUMOR IMAGESPNandaSai
Digital image processing is vast fields which can be using various applications. Which include Detection of criminal face, fingerprint authentication system, in medical field, object recognition etc. Brain tumor detection plays an important role in medical field. Brain tumor detection is detection of tumor affected part in the brain along with its shape size and boundary, so it useful in medical field.
Segmentation and the subsequent quantitative assessment of lesions in medical images provide valuable information for the analysis of neuropathologist and are important for planning of treatment strategies, monitoring of disease progression and prediction of patient outcome. For a better understanding of the pathophysiology of diseases, quantitative imaging can reveal clues about the disease characteristics and effects on particular anatomical structures
Webinar: Machine Learning para MicrocontroladoresEmbarcados
Neste webinar, serão apresentados conceitos sobre inteligência artificial, assim como ferramentas disponíveis para o desenvolvimento integradas ao MPLAB X e ao Harmony 3 e demonstração de um sistema de detecção de anomalia utilizando um microcontrolador da família ATSAMD21 (ARM Cortex M0+).
Keynote presentation from ECBS conference. The talk is about how to use machine learning and AI in improving software engineering. Experiences from our project in Software Center (www.software-center.se).
Machine learning has become an important tool in the modern software toolbox, and high-performing organizations are increasingly coming to rely on data science and machine learning as a core part of their business. eBay introduced machine learning to its commerce search ranking and drove double-digit increases in revenue. Stitch Fix built a multibillion dollar clothing retail business in the US by combining the best of machines with the best of humans. And WeWork is bringing machine-learned approaches to the physical office environment all around the world. In all cases, algorithmic techniques started simple and slowly became more sophisticated over time. This talk will use these examples to derive an agile approach to machine learning, and will explore that approach across several different dimensions. We will set the stage by outlining the kinds of problems that are most amenable to machine-learned approaches as well as describing some important prerequisites, including investments in data quality, a robust data pipeline, and experimental discipline. Next, we will choose the right (algorithmic) tool for the right job, and suggest how to incrementally evolve the algorithmic approaches we bring to bear. Most fancy cutting-edge recommender systems in the real world, for example, started out with simple rules-based techniques or basic regression. Finally, we will integrate machine learning into the broader product development process, and see how it can help us to accelerate business results
Rise of the machines -- Owasp israel -- June 2014 meetupShlomo Yona
Rise of the machines -- Owasp israel -- June 2014 meetup
Shlomo Yona presents why it is a good idea to use Machine Learning in Security and explains some Machine Learning jargon and demonstraits with two fingerprinting examples: a wifi device (PHY) and a browser (L7)
AWS re:Invent 2016: Getting to Ground Truth with Amazon Mechanical Turk (MAC201)Amazon Web Services
Jump-start your machine learning project by using the crowd to build your training set. Before you can train your machine learning algorithm, you need to take your raw inputs and label, annotate, or tag them to build your ground truth. Learn how to use the Amazon Mechanical Turk marketplace to perform these tasks. We share Amazon's best practices, developed while training our own machine learning algorithms, and walk you through quickly getting affordable and high-quality training data.
The Fine Art of Combining Capacity Management with Machine LearningPrecisely
Today, capacity management within the enterprise continues to evolve. In the past, we were focused on the hardware – but now we are focused on the services. With that in mind, the amount of data available has increased significantly and has become difficult for individuals to sort through.
It is apparent that to be successful in this discipline, we need the machines to do more of the heavy lifting. This includes automatically creating reports, calling out anomalies and producing forecasts. The intuition of the human computer is imperative to the success.
View this webinar on-demand where we discuss:
• The strengths and weaknesses of capacity management with and without machine learning
• What machine learning can provide throughout the process
• The benefits of using capacity management and machine learning within your organization
Mobile Recommendation Engine
collaborative filtering and content based approach in hybrid manner then Genetic Algorithm for Enhancement of the Recommendation Engine. by this marketers also will get the unique characteristics of the product that must be created and also recommend to the user.
Big data is set to offer tremendous insight. But with terabytes and petabytes of data pouring in to organizations today, traditional architectures and infrastructures are not up to the challenge. This begs the question: How do you present big data in a way that can be quickly understood and used? These data present tremendous opportunities in data mining, a burgeoning field in computer science that focuses on the development of methods that can extract knowledge from data. In many real world problems, data mining algorithms have access to massive amounts of data. Mining all the available data is prohibitive due to computational (time and memory) constraints. Much of the current research is concerned with scaling up data mining algorithms (i.e. improving on existing data mining algorithms for larger datasets). An alternative approach is to scale down the data. Thus, determining a smallest sufficient training set size that obtains the same accuracy as the entire available dataset remains an important research question. Our research focuses on selecting how many (sampling) instances to present to the data mining algorithm and also how to improve the quality of the data.
Dr. Ashwin Satyanarayana is an Assistant Professor in the Computer Systems Technology department at CityTech. Prior to joining CityTech, Ashwin was a Research Scientist at Microsoft, where he worked on several Big Data problems including Query Reformulation on Microsoft's search engine Bing. Ashwin's prior experience also includes a Senior Research Scientist on the area of Location Analytics at Placed Inc. He holds a PhD in Computer Science (Data Mining) from SUNY, with particular emphasis on Data Mining, Machine Learning and Applied Probability with applications in Real World Learning Problems.
ATI Courses Professional Development Short Course Applied Measurement Engin...Jim Jenkins
How do you know your test measurements are valid? Since NIST traceability actually guarantees little about your test data, how do you know? Could you prove validity to your customer? What is the right measurements solution for your testing requirements? Is it really as simple as the vendors say? What is your real cost of invalid, ambiguous data causing retest or, worst of all, hardware redesign?
This course is for engineers, scientists, and managers who must use systems to understand experimental test measurements on a daily basis. Learn how to design, buy and operate effective automated measurement systems providing demonstrably valid test data, the first time.
Fundamental & underlying engineering principles governing the design and operation of effective automated systems are demonstrated experimentally.
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.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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
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.
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.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
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.
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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
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.
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.
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/
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.
Assuring Contact Center Experiences for Your Customers With ThousandEyes
Fcv core liu
1. Foundations & Core in Computer Vision:
A System Perspective
Ce Liu
Microsoft Research New England
2. Vision vs. Learning
• Computer vision: visual application of machine learning?
• Data features algorithms data
• ML: design algorithms given input and output data
• CV: find the best input and output data given available
algorithms
3. Theoretical vs. Experimental
• Theoretical analysis of a visual system
– Best & worst cases
– Average performance
• Theoretical analysis is challenging as many visual
distributions are hard to model (signal processing: 2nd
order processes, machine learning: exponential families)
• Experimental approach: full spectrum of system
performance as a function of the amount of
data, annotation, number of categories, noise, and other
conditions
4. Quality vs. Speed
• HD videos, billions of images to index
• Real time & 90% vs. one hour per frame & 95%?
• Mechanism to balance quality and speed in modeling
5. Automatic vs. semi-automatic
• Common review feedback: parameters are hand-tuned;
not clear how to set the parameters
• Vision system user feedback: I don’t know how to tweak
parameters!
• Computer-oriented vs. human-oriented representations
• Human-in-the-loop (collaborative) vision
– How to optimally use humans (what, which and how
accurate) beyond traditional active learning
– Model design by crowd-sourcing
– Learning by subtraction
6. Algorithms vs. Sensors
• Two approaches to solving a vision problem
– Look at images, design algorithms, experiment, improve…
– Look at cameras, design new/better sensors, …
• Cameras for full-spectrum, high res, low
noise, depth, motion, occluding boundary, object, …
• What’s the optimal sensor/device for solving a vision
problem?
• What’s the limit of sensors?
7. Thank you!
Ce Liu
Microsoft Research New England
