Brief Introduction about the technology - Deep Learning & Microsoft Cognitive ToolKit (CNTK).
by Marvin, Heng
Twitter: @hmheng
Blog: www.techconnect.io
#artificialintelligence #ai #MachineLearning
Andras Barthazi on Google Analytics API & Open Source Analytics - WAWWeb Analytics Hungary
Andras Barthazi on Google Analytics API & Open Source Analytics - WAW (Web Analytics Wednesday Budapest Hungary)
Piwik and Open Web Analytics was compared to GA API
Mining Social Web APIs with IPython Notebook - Data Day Texas 2014Matthew Russell
Slides from a 2-hour workshop at Data Day Texas 2014 on how to mine social web APIs. This workshop specifically focused on extracting insight from Twitter data and was partitioned into two hour long segments. The first segment focused on familiarity with Twitter's API, while the latter segment focused on using pandas to extract insight from tweets from the firehose via the Streaming API.
AI/ML/DL: Getting Started with Machine Learning on AzureMarvin Heng
The machine learning allows your application gets smarter and smarter over the time. It can predict more accurately, identify purposes more precisely and it keeps learning by itself.
Read more @ www.techconnect.io
by Marvin, Heng
Twitter: @hmheng
Blog: www.techconnect.io
#artificialintelligence #ai #MachineLearning
Mining Social Web APIs with IPython Notebook (Strata 2013)Matthew Russell
Slides from my Strata / Hadoop World 2013 (NYC) hands-on workshop.
Workshop Description from http://strataconf.com/stratany2013/public/schedule/detail/30863
Social web properties such as Twitter, Facebook, LinkedIn, and Google+ have vast amounts of valuable insights lurking just beneath the surface, and this workshop minimizes the barriers to exploring and mining this valuable data by presenting turn-key examples from Mining the Social Web (2nd Edition) with IPython Notebook.
Each module consists of a brief period in which each attendee will customize the corresponding notebook for the module with their own account credentials with the remainder of the module devoted to learning what data is available from the API and exercises demonstrating analysis of the data—all from a pre-populated IPython Notebook. Even attendees with minimal programming experience should be able to walk away from this workshop with a working knowledge of the material and be equipped with sample code that can be easily repurposed given the design of this tutorial.
Time will be set aside at the end of each module’s follow-along presentation for attendees to hack on the code, discuss examples, and ask any lingering questions.
Intelligence Artificielle sur AWS - Retours d'expériencesAdeline Garrigues
Trouver son cas d'usage nécessite de sortir des sentiers battus.
Savoir se servir concrètement des différents outils mis à notre disposition sur la plateforme AWS en tant qu'Entreprise nécessite également de l'expériences et une bonne dose de bonnes pratiques à suivre.
Découvrez la présentation de notre meetup IoT / Data du 13 décembre dernier à Toulouse !
Andras Barthazi on Google Analytics API & Open Source Analytics - WAWWeb Analytics Hungary
Andras Barthazi on Google Analytics API & Open Source Analytics - WAW (Web Analytics Wednesday Budapest Hungary)
Piwik and Open Web Analytics was compared to GA API
Mining Social Web APIs with IPython Notebook - Data Day Texas 2014Matthew Russell
Slides from a 2-hour workshop at Data Day Texas 2014 on how to mine social web APIs. This workshop specifically focused on extracting insight from Twitter data and was partitioned into two hour long segments. The first segment focused on familiarity with Twitter's API, while the latter segment focused on using pandas to extract insight from tweets from the firehose via the Streaming API.
AI/ML/DL: Getting Started with Machine Learning on AzureMarvin Heng
The machine learning allows your application gets smarter and smarter over the time. It can predict more accurately, identify purposes more precisely and it keeps learning by itself.
Read more @ www.techconnect.io
by Marvin, Heng
Twitter: @hmheng
Blog: www.techconnect.io
#artificialintelligence #ai #MachineLearning
Mining Social Web APIs with IPython Notebook (Strata 2013)Matthew Russell
Slides from my Strata / Hadoop World 2013 (NYC) hands-on workshop.
Workshop Description from http://strataconf.com/stratany2013/public/schedule/detail/30863
Social web properties such as Twitter, Facebook, LinkedIn, and Google+ have vast amounts of valuable insights lurking just beneath the surface, and this workshop minimizes the barriers to exploring and mining this valuable data by presenting turn-key examples from Mining the Social Web (2nd Edition) with IPython Notebook.
Each module consists of a brief period in which each attendee will customize the corresponding notebook for the module with their own account credentials with the remainder of the module devoted to learning what data is available from the API and exercises demonstrating analysis of the data—all from a pre-populated IPython Notebook. Even attendees with minimal programming experience should be able to walk away from this workshop with a working knowledge of the material and be equipped with sample code that can be easily repurposed given the design of this tutorial.
Time will be set aside at the end of each module’s follow-along presentation for attendees to hack on the code, discuss examples, and ask any lingering questions.
Intelligence Artificielle sur AWS - Retours d'expériencesAdeline Garrigues
Trouver son cas d'usage nécessite de sortir des sentiers battus.
Savoir se servir concrètement des différents outils mis à notre disposition sur la plateforme AWS en tant qu'Entreprise nécessite également de l'expériences et une bonne dose de bonnes pratiques à suivre.
Découvrez la présentation de notre meetup IoT / Data du 13 décembre dernier à Toulouse !
ONNX - the emerging standard for interoperable and optimized AI inference and training. A graduated project of the Linux Foundation Artificial Intelligence - best practice open source - true multi-vendor open governance in a foundation.
Tweepy is an open source Python package that gives you a very convenient way to access the Twitter API with Python. Tweepy includes a set of classes and methods that represent Twitter's models and API endpoints, and it transparently handles various implementation details, such as: Data encoding and decoding.
Welcome to Python Programming Language.pdfdigitaldnyan
Python has a very simple and elegant syntax. It's much easier to read and write Python programs compared to other languages like are much easier.
You can move Python programs from one platform to another, and run it without any changes.
https://digitaldnyan.com/best-python-training-institute-classes-pune
Welcome to Python Programming Language.pptxdigitaldnyan
Python is an interpreter, high-level, broadly useful programming language. Python’s structure theory underlines code readability with its remarkable utilization of huge whitespace. Its language develops and object-oriented methodology intends to enable software engineers to compose clear, logical code for little and enormous scale ventures. This object-oriented framework has dazzled IT lovers fundamentally with its dynamic semantics.
Welcome to Python Programming Language.pdfdigitaldnyan
Python is easy to learn. Its symbol is easy and code is very readable.
Python has a lot of applications. It's used for developing web applications, data science, rapid application development, and so on.
Python allows you to write programs in lesser lines of code than most of the programming languages.Python is easy to learn. Its syntax is easy and code is very readable.
digitaldnyan.com
Blockchain Beyond Finance - Cronos Groep - Jan 17, 2017BigchainDB
Towards the internet of value & trust.
"To develop shared global compute infrastructure,
we must first understand the status quo of infrastructure,
...and how to change it accordingly."
Dimitri De Jonghe, lead developer of BigchainDB talking about blockchain technology beyond the financial sector.
Deep Learning Frameworks 2019 | Which Deep Learning Framework To Use | Deep L...Simplilearn
Deep Learning covers all the essential Deep Learning frameworks that are necessary to build AI models. In this presentation, you will learn about the development of essential frameworks such as TensorFlow, Keras, PyTorch, Theano, etc. You will also understand the programming languages used to build the frameworks, the different companies that use these frameworks, the characteristics of these Deep Learning frameworks, and type of models that were built using these frameworks. Now, let us get started with understanding the different popular Deep Learning frameworks being used in industries.
Below are the different Deep Learning frameworks we'll be discussing in this presentation:
1. TensorFlow
2. Keras
3. PyTorch
4. Theano
5. Deep Learning 4 Java
6. Caffe
7. Chainer
8. Microsoft CNTK
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning, and artificial intelligence
Learn more at https://www.simplilearn.com/deep-learning-course-with-tensorflow-training
In this deck from the 2016 HPC Advisory Council Switzerland Conference, Kenneth Hoste from the University Ghent presents an introduction to EasyBuild, an open-source framework for (automatically) getting scientific software installed on HPC systems.
Watch the video presentation: http://wp.me/p3RLHQ-f8J
Learn more: https://github.com/hpcugent/easybuild
See more talks from the Switzerland HPC Conference:
http://insidehpc.com/2016-swiss-hpc-conference/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Presented at IDEAS Conference Dallas at the University of Texas in Dallas 2018. Subtitle: Practical and collaborative method to jump start into machine learning projects using open source with Jupyter Notebooks and Google Collab. Abstract: Not at all machine learning enthusiasts are alike, and, hence, setting up code environment or training the data model can many times be overwhelming for newcomers in the field of machine learning and deep learning. Data scientists may not have the necessary skills in setting up development environments, and, programmers, may not necessarily have the data scientists skills for preparing data sets and evaluating machine learning models. Furthermore, data scientists and programmers together in some enterprise may lack the collaboration platforms to work together on such projects. Even though most of the books on machine learning using some programming language X provide readers with instructions for setting up the coding environment, such chapters can derail the process of getting started if one gets stuck in the setup. Alternatively, collaborative platforms using Jupyter Ipython Notebooks and Google Collab provide a quick starter for programmers and data scientists alike to develop and collaborate on machine learning algorithms through open source without getting stuck with the nuances of setting up their environments or having to depend on commercial products. The talk revisits the value of Jupyter notebooks for newcomers to the field of AI by showcasing live examples and sharing sources of machine learning algorithms running online using Jupyter notebooks and providing a set of guidelines for implementing such technology in the workplace or leveraging existing ones in the market such as Google Collab. The talk is intended to encourage machine learning enthusiasts to enter the field through a practical method that minimizes the stress from the overwhelming material available on the Internet on how to get started with machine learning.
M. Valoriani, A. Musone - Game Changing: Mixed Reality + Machine Learning = ?...Codemotion
In the last 3 years Mixed Reality devices have opened the door to an infinite number of new disrupting opportunities, but it is not the only revolution underway. Thanks to the combination of new powerful cloud services and local computation capabilities, it is now possible to create a complete new category of applications. In this session we will showcase with live code examples two kinds of Mixed Reality applications: an app based on online cloud services such as Azure Custom Vision or Google Cloud AutoML and an app that uses an offline ONNX Model - trained online but deployed locally.
CloudEngine is free and open-source software. Install it on a server and create an instant social website structured around Clouds, Cloudscapes and CloudStreams. CloudEngine powers Cloudworks.
Get CloudEngine, http://getcloudengine.org/
Presented: DevCSI developer day, 17 February 2011. http://wiki.2011.dev8d.org/w/Session-L45 | http://getcloudengine.org
Fine tune and deploy Hugging Face NLP modelsOVHcloud
Are you currently managing AI projects that require a lot of GPU power?
Are you tired of managing the complexity of your infrastructures, GPU instances and your Kubeflow yourself?
Need flexibility for your AI platform or SaaS solution?
OVHcloud innovates in AI by offering simple and turnkey solutions to train your models and put them into production.
Accelerating Personal Development through Microsoft CertificationsMarvin Heng
Professional certifications like Microsoft certifications are important to IT professionals and developers. It does not only help in personal development but also your career. In this session, we will be discussing what exactly is a certification, what preparation can be done, and knowing what to take know during the exam. Some certifications are offered free which you might not want to miss.
Copyright Marvin Heng
@hmheng
Microsoft BotFramework - Global AI Bootcamp Nepal 2022Marvin Heng
Microsoft Botframework - AI
In this hands-on lab you are going to work with Bot Composer.
Microsoft Botframework Composer
Microsoft Botframework Emulator
Ref: https://www.meetup.com/NepalCloudPro/events/279731864/
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ONNX - the emerging standard for interoperable and optimized AI inference and training. A graduated project of the Linux Foundation Artificial Intelligence - best practice open source - true multi-vendor open governance in a foundation.
Tweepy is an open source Python package that gives you a very convenient way to access the Twitter API with Python. Tweepy includes a set of classes and methods that represent Twitter's models and API endpoints, and it transparently handles various implementation details, such as: Data encoding and decoding.
Welcome to Python Programming Language.pdfdigitaldnyan
Python has a very simple and elegant syntax. It's much easier to read and write Python programs compared to other languages like are much easier.
You can move Python programs from one platform to another, and run it without any changes.
https://digitaldnyan.com/best-python-training-institute-classes-pune
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Python is an interpreter, high-level, broadly useful programming language. Python’s structure theory underlines code readability with its remarkable utilization of huge whitespace. Its language develops and object-oriented methodology intends to enable software engineers to compose clear, logical code for little and enormous scale ventures. This object-oriented framework has dazzled IT lovers fundamentally with its dynamic semantics.
Welcome to Python Programming Language.pdfdigitaldnyan
Python is easy to learn. Its symbol is easy and code is very readable.
Python has a lot of applications. It's used for developing web applications, data science, rapid application development, and so on.
Python allows you to write programs in lesser lines of code than most of the programming languages.Python is easy to learn. Its syntax is easy and code is very readable.
digitaldnyan.com
Blockchain Beyond Finance - Cronos Groep - Jan 17, 2017BigchainDB
Towards the internet of value & trust.
"To develop shared global compute infrastructure,
we must first understand the status quo of infrastructure,
...and how to change it accordingly."
Dimitri De Jonghe, lead developer of BigchainDB talking about blockchain technology beyond the financial sector.
Deep Learning Frameworks 2019 | Which Deep Learning Framework To Use | Deep L...Simplilearn
Deep Learning covers all the essential Deep Learning frameworks that are necessary to build AI models. In this presentation, you will learn about the development of essential frameworks such as TensorFlow, Keras, PyTorch, Theano, etc. You will also understand the programming languages used to build the frameworks, the different companies that use these frameworks, the characteristics of these Deep Learning frameworks, and type of models that were built using these frameworks. Now, let us get started with understanding the different popular Deep Learning frameworks being used in industries.
Below are the different Deep Learning frameworks we'll be discussing in this presentation:
1. TensorFlow
2. Keras
3. PyTorch
4. Theano
5. Deep Learning 4 Java
6. Caffe
7. Chainer
8. Microsoft CNTK
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning, and artificial intelligence
Learn more at https://www.simplilearn.com/deep-learning-course-with-tensorflow-training
In this deck from the 2016 HPC Advisory Council Switzerland Conference, Kenneth Hoste from the University Ghent presents an introduction to EasyBuild, an open-source framework for (automatically) getting scientific software installed on HPC systems.
Watch the video presentation: http://wp.me/p3RLHQ-f8J
Learn more: https://github.com/hpcugent/easybuild
See more talks from the Switzerland HPC Conference:
http://insidehpc.com/2016-swiss-hpc-conference/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Presented at IDEAS Conference Dallas at the University of Texas in Dallas 2018. Subtitle: Practical and collaborative method to jump start into machine learning projects using open source with Jupyter Notebooks and Google Collab. Abstract: Not at all machine learning enthusiasts are alike, and, hence, setting up code environment or training the data model can many times be overwhelming for newcomers in the field of machine learning and deep learning. Data scientists may not have the necessary skills in setting up development environments, and, programmers, may not necessarily have the data scientists skills for preparing data sets and evaluating machine learning models. Furthermore, data scientists and programmers together in some enterprise may lack the collaboration platforms to work together on such projects. Even though most of the books on machine learning using some programming language X provide readers with instructions for setting up the coding environment, such chapters can derail the process of getting started if one gets stuck in the setup. Alternatively, collaborative platforms using Jupyter Ipython Notebooks and Google Collab provide a quick starter for programmers and data scientists alike to develop and collaborate on machine learning algorithms through open source without getting stuck with the nuances of setting up their environments or having to depend on commercial products. The talk revisits the value of Jupyter notebooks for newcomers to the field of AI by showcasing live examples and sharing sources of machine learning algorithms running online using Jupyter notebooks and providing a set of guidelines for implementing such technology in the workplace or leveraging existing ones in the market such as Google Collab. The talk is intended to encourage machine learning enthusiasts to enter the field through a practical method that minimizes the stress from the overwhelming material available on the Internet on how to get started with machine learning.
M. Valoriani, A. Musone - Game Changing: Mixed Reality + Machine Learning = ?...Codemotion
In the last 3 years Mixed Reality devices have opened the door to an infinite number of new disrupting opportunities, but it is not the only revolution underway. Thanks to the combination of new powerful cloud services and local computation capabilities, it is now possible to create a complete new category of applications. In this session we will showcase with live code examples two kinds of Mixed Reality applications: an app based on online cloud services such as Azure Custom Vision or Google Cloud AutoML and an app that uses an offline ONNX Model - trained online but deployed locally.
CloudEngine is free and open-source software. Install it on a server and create an instant social website structured around Clouds, Cloudscapes and CloudStreams. CloudEngine powers Cloudworks.
Get CloudEngine, http://getcloudengine.org/
Presented: DevCSI developer day, 17 February 2011. http://wiki.2011.dev8d.org/w/Session-L45 | http://getcloudengine.org
Fine tune and deploy Hugging Face NLP modelsOVHcloud
Are you currently managing AI projects that require a lot of GPU power?
Are you tired of managing the complexity of your infrastructures, GPU instances and your Kubeflow yourself?
Need flexibility for your AI platform or SaaS solution?
OVHcloud innovates in AI by offering simple and turnkey solutions to train your models and put them into production.
Accelerating Personal Development through Microsoft CertificationsMarvin Heng
Professional certifications like Microsoft certifications are important to IT professionals and developers. It does not only help in personal development but also your career. In this session, we will be discussing what exactly is a certification, what preparation can be done, and knowing what to take know during the exam. Some certifications are offered free which you might not want to miss.
Copyright Marvin Heng
@hmheng
Microsoft BotFramework - Global AI Bootcamp Nepal 2022Marvin Heng
Microsoft Botframework - AI
In this hands-on lab you are going to work with Bot Composer.
Microsoft Botframework Composer
Microsoft Botframework Emulator
Ref: https://www.meetup.com/NepalCloudPro/events/279731864/
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A Journey with Microsoft Cognitive Service IMarvin Heng
A Journey with Microsoft Cognitive Service I
This slide is about Microsoft Cognitive Services. By going through you will understand what and how Microsoft Cognitive Service works.
Marvin Heng
Medium: @hmheng
Twitter: @hmheng
Github: hmheng
A Journey With Microsoft Cognitive Services IIMarvin Heng
A Journey with Microsoft Cognitive Service II
This slide is about Microsoft Cognitive Services. By going through you will understand what and how Microsoft Cognitive Service works.
Marvin Heng
Medium: @hmheng
Twitter: @hmheng
Github: hmheng
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Technologies involved: Microsoft BotFramework, SignalR and ASP.NET Core on Azure.
www.techconnect.io
Youtube: https://www.youtube.com/watch?v=nwGFZA0h9k8&feature=youtu.be
Many businesses today should understand the importance of using AI to improve the business processes and delight customers.
We will show how you can use AI & ML technologies to get insights from processed data
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Azure Cognitive Services has been an AI solution that close to many developers's heart. They implement it in their applications easily. There are some new Microsoft Cognitive Services that are newly being introduced.
How AI Bot can help to increase the productivity of an organization. Learn the integration of Bot into a team collaboration tool - Microsoft Teams.
www.techconnect.io
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Read more on @ www.techconnect.io
by Marvin, Heng
Twitter: @hmheng
Blog: www.techconnect.io
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Read more @ https://wp.me/p9i67S-cJ
by Marvin, Heng
Twitter: @hmheng
Blog: www.techconnect.io
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by Marvin, Heng
Twitter: @hmheng
Blog: www.techconnect.io
AI: Integrate Search Function into Your App Using Bing Search API.Marvin Heng
Learn how to integrate web search function into your mobile app using Bing Search API.
#xamarin #cognitiveservice #bingsearch #microsoft #appdevelopment #crossplatform #ios #android #uwp
Click here for viewing full tutorial @ www.techconnect.io
by Marvin, Heng
Twitter: @hmheng
Blog: www.techconnect.io
Artificial Intelligent: Intelligent Bot With Microsoft Bot Framework & AzureMarvin Heng
With Microsoft's Botframework - Create a simple chatbot or AI bot that can help your website to serve customers intelligently. By following this tutorial, you should learn how to create a simple bot using Bot Framework.
Read more @ www.techconnect.io/
by Marvin, Heng
Twitter: @hmheng
Blog: www.techconnect.io
AI: Mobile Apps That Understands Your Intention When You TypedMarvin Heng
With Microsoft's Cognitive Services - Language Understanding Intelligent Service (LUIS), we can build a smart app. By following this tutorial, you should learn how to create a intelligent cross platform Mobile App that understands what is your intention.
Read more @ www.techconnect.io
By Marvin Heng
Twitter: @hmheng
Blog: www.techconnect.io
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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.
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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.
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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/
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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/
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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.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
JMeter webinar - integration with InfluxDB and Grafana
AI/ML/DL: Introduction to Deep Learning with Cognitive ToolKit
1. Introduction to
Deep Learning with
Cognitive Toolkit (CNTK)
Marvin Heng
Blog : http://hmheng.pinsland.com
Twitter : @hmheng
YouTube: http://bit.ly/hmheng_yt
SlideShare: http://bit.ly/hmheng_ss
Github: https://github.com/hmheng
HMHENG.
PINSLAND.COM
Microsoft
Cognitive Service
Microsoft
Cognitive ToolKit
2. Who is Marvin?
Software + Web Developer
AI Enthusiast
Blog : https://hmheng.pinsland.com
Twitter : @hmheng
YouTube: http://bit.ly/hmheng_yt
SlideShare: http://bit.ly/hmheng_ss
Github: https://github.com/hmheng
Scan & Subscribe!
hmheng.pinsland.com
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5. What is Deep Learning?
A Subfield of Machine Learning
Self Learning
More complex than Machine Learning
Neural Networks mimics the neural systems in our brains.
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6. What is Deep Learning?
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Credit: Nvidia
9. Microsoft Cognitive Toolkit (CNTK)?
• Open-Source Development on GitHub (released 2.5 on 15th Mar 18)
• Fast
• Optimized for GPUs & Libraries
• Best-in-Class multi-GPU/Multi-server algorithms
• Flexible
• BrainScript, C#/.NET, C++, Python, Network Definition Language, Model Editing Language(MEL)
• Linux, Windows and Docker Container
• Ease of Use & Wide-range of Support
• FF-NN, CNN, RNN, LSTM, DSSM,…
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10. Microsoft Cognitive Toolkit (CNTK)
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Arbitrary Neural
Networks Expression
Composing simple building
blocks into complex
computational networks.
Production
Ready
State-of-the-art accuracy
Efficient
Scalable to Multi-GPU/Multi-
Server
11. Microsoft Cognitive Toolkit
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Design
Number of layers,
number of units
Dependent on
concept
complexity
Experimentation
often required
Activation
Function
Enables non-
linear models
Examples include
sigmoid,
hyperbolic
tangent, ReLU,
softmax, …
Loss
Function
Measure the
performance
during training
Examples include
cross entropy
Training
Mechanism for
getting optimal
weights
Overfitting
Stop models from
over-optimizing
on training data
set.
12. Microsoft Cognitive Toolkit (CNTK)
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Windows
CPU|GPU
NuGet – CPU | NuGet – GPU | NuGet – UWP CPU
Linux
CPU|GPU|Docker – CPU| Docker – GPU
Requires OpenMPI
MacOS
Docker – CPU
Cloud
Data Science Virtual Machine | Azure Notebooks
Azure Batch AI Training (CPU/GPU) | Project BrainWave (FPGA)
13. Sharing
Marvin Heng
Blog : http://hmheng.pinsland.com
Twitter : @hmheng
YouTube: http://bit.ly/hmheng_yt
SlideShare: http://bit.ly/hmheng_ss
Github: https://github.com/hmheng
Microsoft
Cognitive Service
HMHENG.
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Microsoft
Cognitive ToolKit
15. Open Neural Network Exchange
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• An open format to represent deep learning models.
• Collaboration between Microsoft, AWS and Facebook.
• Supported by community of partners like Intel, ARM, AMD, nvidia,
etc.
• A share library with Caffe2, PyTorch, MXNet, Cognitive Toolkit.
• Permissive MIT license and no patents.
16. ONNX Aims
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• Allow interoperability between frameworks
• CNTK, Caffe2 and PyTorch
• Allow hardware vendor to focus on one IR in their backend
optimization
• Allow training in one toolkit and deploy in another.
17. ONNX Aims
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• Allow interoperability between frameworks
• CNTK, Caffe2 and PyTorch
• Allow hardware vendor to focus on one IR in their backend
optimization
• Allow training in one toolkit and deploy in another.
19. Marvin Heng
Blog : http://hmheng.pinsland.com
Twitter : @hmheng
YouTube: http://bit.ly/hmheng_yt
SlideShare: http://bit.ly/hmheng_ss
Github: https://github.com/hmheng
Thank
You
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20. Introduction to
Deep Learning with
Cognitive Toolkit (CNTK)
Marvin Heng
Blog : http://hmheng.pinsland.com
Twitter : @hmheng
YouTube: http://bit.ly/hmheng_yt
SlideShare: http://bit.ly/hmheng_ss
Github: https://github.com/hmheng
HMHENG.
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Microsoft
Cognitive Service
Microsoft
Cognitive ToolKit
