The document provides an overview of computer vision and convolutional neural networks (CNNs). It discusses the basic architecture of CNNs, including convolutional layers, pooling layers, fully connected layers, and other concepts like activation functions, loss functions, regularization, and transfer learning. It also covers techniques for measuring CNN performance, such as precision, recall, average precision (AP), and intersection over union (IoU). The goal is to introduce computer vision and explain how CNNs work at a high level.
Deep Learning for Developers: An Introduction, Featuring Samsung SDS (AIM301-...Amazon Web Services
Artificial intelligence (AI) is rapidly evolving, and much of the advancement is driven by deep learning, a machine learning technique inspired by the inner workings of the human brain. In this session, learn what deep learning is and how you can use it in your applications to unlock new and exciting capabilities for your customers and business. Also hear from Samsung SDS about how it developed a deep-learning model for cardiac arrhythmia detection using Apache MXNet, an open-source deep-learning framework. By the end of the session, you will understand how to leverage deep learning in your applications and get started with it.
[REPEAT] Deep Learning for Developers: An Introduction, Featuring Samsung SDS...Amazon Web Services
Artificial intelligence (AI) is rapidly evolving, and much of the advancement is driven by deep learning, a machine learning technique inspired by the inner workings of the human brain. In this session, learn what deep learning is and how you can use it in your applications to unlock new and exciting capabilities for your customers and business. Also hear from Samsung SDS about how it developed a deep-learning model for cardiac arrhythmia detection using Apache MXNet, an open-source deep-learning framework. By the end of the session, you will understand how to leverage deep learning in your applications and get started with it. Please join us for a speaker meet-and-greet following this session at the Speaker Lounge (ARIA East, Level 1, Willow Lounge). The meet-and-greet starts 15 minutes after the session and runs for half an hour.
Deep Learning for Developers: An Introduction, Featuring Samsung SDS (AIM301-...Amazon Web Services
Artificial intelligence (AI) is rapidly evolving, and much of the advancement is driven by deep learning, a machine learning technique inspired by the inner workings of the human brain. In this session, learn what deep learning is and how you can use it in your applications to unlock new and exciting capabilities for your customers and business. Also hear from Samsung SDS about how it developed a deep-learning model for cardiac arrhythmia detection using Apache MXNet, an open-source deep-learning framework. By the end of the session, you will understand how to leverage deep learning in your applications and get started with it.
[REPEAT] Deep Learning for Developers: An Introduction, Featuring Samsung SDS...Amazon Web Services
Artificial intelligence (AI) is rapidly evolving, and much of the advancement is driven by deep learning, a machine learning technique inspired by the inner workings of the human brain. In this session, learn what deep learning is and how you can use it in your applications to unlock new and exciting capabilities for your customers and business. Also hear from Samsung SDS about how it developed a deep-learning model for cardiac arrhythmia detection using Apache MXNet, an open-source deep-learning framework. By the end of the session, you will understand how to leverage deep learning in your applications and get started with it. Please join us for a speaker meet-and-greet following this session at the Speaker Lounge (ARIA East, Level 1, Willow Lounge). The meet-and-greet starts 15 minutes after the session and runs for half an hour.
The Steady State Reduce Spikiness from GPU Utilization with Apache MXNet (inc...Amazon Web Services
Many practitioners of deep learning start from tailoring DL code from existing tutorials and examples on the Internet. These examples are often developed without optimisation of GPU utilisation in mind. In this session we provide attendees with simple steps of best coding practices in order to optimise their code to optimize GPU/CPU utilization for training and utilisation that significantly reduce cost of both training and inference using Apache MXNet and Amazon SageMaker.
MCL310_Building Deep Learning Applications with Apache MXNet and GluonAmazon Web Services
In this workshop, learn how to set up a deep learning environment and explore neural network architectures, namely multilayer perceptrons, convolution neural networks (CNNs), and LSTMs. You'll learn to model problems and use cases that many customers tackle by using Gluon, the new new intuitive, dynamic programming interface for Apache MXNet. You'll also learn how to model common use cases with deep learning on AWS.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/08/high-fidelity-conversion-of-floating-point-networks-for-low-precision-inference-using-distillation-with-limited-data-a-presentation-from-imagination-technologies/
James Imber, Senior Research Engineer at Imagination Technologies, presents the “High-fidelity Conversion of Floating-point Networks for Low-precision Inference using Distillation with Limited Data” tutorial at the May 2021 Embedded Vision Summit.
When converting floating-point networks to low-precision equivalents for high-performance inference, the primary objective is to maximally compress the network whilst maintaining fidelity to the original, floating-point network. This is made particularly challenging when only a reduced or unlabelled dataset is available. Data may be limited for reasons of a commercial or legal nature: for example, companies may be unwilling to share valuable data and labels that represent a substantial investment of resources; or the collector of the original dataset may not be permitted to share it for data privacy reasons.
Imber presents a method based on distillation that allows high-fidelity, low-precision networks to be produced for a wide range of different network types, using the original trained network in place of a labeled dataset. The proposed approach is directly applicable across multiple domains (e.g. classification, segmentation and style transfer) and can be adapted to numerous network compression techniques.
Comparative Study of Neural Networks Algorithms for Cloud Computing CPU Sched...IJECEIAES
Cloud Computing is the most powerful computing model of our time. While the major IT providers and consumers are competing to exploit the benefits of this computing model in order to thrive their profits, most of the cloud computing platforms are still built on operating systems that uses basic CPU (Core Processing Unit) scheduling algorithms that lacks the intelligence needed for such innovative computing model. Correspdondingly, this paper presents the benefits of applying Artificial Neural Networks algorithms in regards to enhancing CPU scheduling for Cloud Computing model. Furthermore, a set of characteristics and theoretical metrics are proposed for the sake of comparing the different Artificial Neural Networks algorithms and finding the most accurate algorithm for Cloud Computing CPU Scheduling.
Deploying cost-effective machine learning models - AIM202 - Atlanta AWS SummitAmazon Web Services
Amazon SageMaker offers a broad and deep set of modular capabilities to build, train, and deploy machine learning models in the way you are most comfortable. With the majority of the ML production lifecycle spent on inference, Amazon SageMaker supports different approaches to optimizing your deployments for cost and efficiency. Join us for a discussion on two of these approaches: Amazon Elastic Inference for low-cost GPU-powered acceleration, and Amazon SageMaker Neo for optimizing and compiling models for inference.
Amazon SageMaker offers a broad and deep set of modular capabilities to build, train, and deploy machine learning models the way you are most comfortable with. With the majority of the ML production lifecycle spent on inference, Amazon SageMaker supports different approaches to optimizing your deployments for cost and efficiency. Come join us for a discussion on two of these approaches: Amazon Elastic Inference for low-cost GPU-powered acceleration, and Amazon SageMaker Neo for optimizing and compiling models for inference.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
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Many practitioners of deep learning start from tailoring DL code from existing tutorials and examples on the Internet. These examples are often developed without optimisation of GPU utilisation in mind. In this session we provide attendees with simple steps of best coding practices in order to optimise their code to optimize GPU/CPU utilization for training and utilisation that significantly reduce cost of both training and inference using Apache MXNet and Amazon SageMaker.
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In this workshop, learn how to set up a deep learning environment and explore neural network architectures, namely multilayer perceptrons, convolution neural networks (CNNs), and LSTMs. You'll learn to model problems and use cases that many customers tackle by using Gluon, the new new intuitive, dynamic programming interface for Apache MXNet. You'll also learn how to model common use cases with deep learning on AWS.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/08/high-fidelity-conversion-of-floating-point-networks-for-low-precision-inference-using-distillation-with-limited-data-a-presentation-from-imagination-technologies/
James Imber, Senior Research Engineer at Imagination Technologies, presents the “High-fidelity Conversion of Floating-point Networks for Low-precision Inference using Distillation with Limited Data” tutorial at the May 2021 Embedded Vision Summit.
When converting floating-point networks to low-precision equivalents for high-performance inference, the primary objective is to maximally compress the network whilst maintaining fidelity to the original, floating-point network. This is made particularly challenging when only a reduced or unlabelled dataset is available. Data may be limited for reasons of a commercial or legal nature: for example, companies may be unwilling to share valuable data and labels that represent a substantial investment of resources; or the collector of the original dataset may not be permitted to share it for data privacy reasons.
Imber presents a method based on distillation that allows high-fidelity, low-precision networks to be produced for a wide range of different network types, using the original trained network in place of a labeled dataset. The proposed approach is directly applicable across multiple domains (e.g. classification, segmentation and style transfer) and can be adapted to numerous network compression techniques.
Comparative Study of Neural Networks Algorithms for Cloud Computing CPU Sched...IJECEIAES
Cloud Computing is the most powerful computing model of our time. While the major IT providers and consumers are competing to exploit the benefits of this computing model in order to thrive their profits, most of the cloud computing platforms are still built on operating systems that uses basic CPU (Core Processing Unit) scheduling algorithms that lacks the intelligence needed for such innovative computing model. Correspdondingly, this paper presents the benefits of applying Artificial Neural Networks algorithms in regards to enhancing CPU scheduling for Cloud Computing model. Furthermore, a set of characteristics and theoretical metrics are proposed for the sake of comparing the different Artificial Neural Networks algorithms and finding the most accurate algorithm for Cloud Computing CPU Scheduling.
Deploying cost-effective machine learning models - AIM202 - Atlanta AWS SummitAmazon Web Services
Amazon SageMaker offers a broad and deep set of modular capabilities to build, train, and deploy machine learning models in the way you are most comfortable. With the majority of the ML production lifecycle spent on inference, Amazon SageMaker supports different approaches to optimizing your deployments for cost and efficiency. Join us for a discussion on two of these approaches: Amazon Elastic Inference for low-cost GPU-powered acceleration, and Amazon SageMaker Neo for optimizing and compiling models for inference.
Amazon SageMaker offers a broad and deep set of modular capabilities to build, train, and deploy machine learning models the way you are most comfortable with. With the majority of the ML production lifecycle spent on inference, Amazon SageMaker supports different approaches to optimizing your deployments for cost and efficiency. Come join us for a discussion on two of these approaches: Amazon Elastic Inference for low-cost GPU-powered acceleration, and Amazon SageMaker Neo for optimizing and compiling models for inference.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
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Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
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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.
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Welocme to ViralQR, your best QR code generator.ViralQR
Welcome to ViralQR, your best QR code generator available on the market!
At ViralQR, we design static and dynamic QR codes. Our mission is to make business operations easier and customer engagement more powerful through the use of QR technology. Be it a small-scale business or a huge enterprise, our easy-to-use platform provides multiple choices that can be tailored according to your company's branding and marketing strategies.
Our Vision
We are here to make the process of creating QR codes easy and smooth, thus enhancing customer interaction and making business more fluid. We very strongly believe in the ability of QR codes to change the world for businesses in their interaction with customers and are set on making that technology accessible and usable far and wide.
Our Achievements
Ever since its inception, we have successfully served many clients by offering QR codes in their marketing, service delivery, and collection of feedback across various industries. Our platform has been recognized for its ease of use and amazing features, which helped a business to make QR codes.
Our Services
At ViralQR, here is a comprehensive suite of services that caters to your very needs:
Static QR Codes: Create free static QR codes. These QR codes are able to store significant information such as URLs, vCards, plain text, emails and SMS, Wi-Fi credentials, and Bitcoin addresses.
Dynamic QR codes: These also have all the advanced features but are subscription-based. They can directly link to PDF files, images, micro-landing pages, social accounts, review forms, business pages, and applications. In addition, they can be branded with CTAs, frames, patterns, colors, and logos to enhance your branding.
Pricing and Packages
Additionally, there is a 14-day free offer to ViralQR, which is an exceptional opportunity for new users to take a feel of this platform. One can easily subscribe from there and experience the full dynamic of using QR codes. The subscription plans are not only meant for business; they are priced very flexibly so that literally every business could afford to benefit from our service.
Why choose us?
ViralQR will provide services for marketing, advertising, catering, retail, and the like. The QR codes can be posted on fliers, packaging, merchandise, and banners, as well as to substitute for cash and cards in a restaurant or coffee shop. With QR codes integrated into your business, improve customer engagement and streamline operations.
Comprehensive Analytics
Subscribers of ViralQR receive detailed analytics and tracking tools in light of having a view of the core values of QR code performance. Our analytics dashboard shows aggregate views and unique views, as well as detailed information about each impression, including time, device, browser, and estimated location by city and country.
So, thank you for choosing ViralQR; we have an offer of nothing but the best in terms of QR code services to meet business diversity!
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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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💥 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
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👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on: