Generative models create new data similar to the training data using machine learning algorithms. To create new data, these models undergo a series of training wherein they are exposed to large datasets.
Generative AI models, such as GANs and VAEs, have the potential to create realistic and diverse synthetic data for various applications, from image and speech synthesis to drug discovery and language modeling. However, training these models can be challenging due to the instability and mode collapse issues that often arise. In this workshop, we will explore how stable diffusion, a recent training method that combines diffusion models and Langevin dynamics, can address these challenges and improve the performance and stability of generative models. We will use a pre-configured development environment for machine learning, to run hands-on experiments and train stable diffusion models on different datasets. By the end of the session, attendees will have a better understanding of generative AI and stable diffusion, and how to build and deploy stable generative models for real-world use cases.
The development of AI/ML has given video stitching and its applications an entirely new dimension. Let’s examine the evolution of video stitching combined with AI/ML throughout time, the main difficulties developers have had to overcome, and the numerous uses for video stitching that use the newest AI-infused technology.
To know more about Video Stitching + AL/ML- Changing Landscape over the years, see https://www.logic-fruit.com/blog/al-ml/video-stitching-ai-ml/
About Logic Fruit Technologies
Logic Fruit Technologies is a product engineering R&D & consulting services provider for embedded systems and application development. We provide end-to-end solutions from the conception of the idea and design to the finished product. We have been servicing customers globally for over a decade.
The company has specific experience in various fields, such as
FPGA Design & hardware design
RTL IP Design
A variety of digital protocols
Communication buses as1G, 10G Ethernet
PCIe
DIGRF
STM16/64
HDMI.
Logic Fruit Technologies is also an expert in developing,
software-defined radio (SDR) IPs
Encryption
Signal generation
Data analysis, and
Multiple Image Processing Techniques.
Recently Logic Fruit technologies are also exploring FPGA acceleration on data centers for real-time data processing.
**Our Social Media Channels**
Facebook: https://www.facebook.com/LogicFruit/
Twitter: https://twitter.com/logicfruit
LinkedIn: https://www.linkedin.com/company/logi…
Website: https://www.logic-fruit.com/
#LFT #LogicFruitTechnologies #LogicFruit
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How to prepare a perfect video abstract for your research paper – Pubrica.pdfPubrica
A video abstract is a series of moving pictures taken from a lengthier movie that is significantly shorter than the original yet retains the original's essential meaning.
Learn More : https://bit.ly/3JVyrCW
Reference: https://pubrica.com/services/publication-support/Video-Abstract/
Why Pubrica:
When you order our services, we promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Bio statistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44-1618186353
Generative AI models, such as GANs and VAEs, have the potential to create realistic and diverse synthetic data for various applications, from image and speech synthesis to drug discovery and language modeling. However, training these models can be challenging due to the instability and mode collapse issues that often arise. In this workshop, we will explore how stable diffusion, a recent training method that combines diffusion models and Langevin dynamics, can address these challenges and improve the performance and stability of generative models. We will use a pre-configured development environment for machine learning, to run hands-on experiments and train stable diffusion models on different datasets. By the end of the session, attendees will have a better understanding of generative AI and stable diffusion, and how to build and deploy stable generative models for real-world use cases.
The development of AI/ML has given video stitching and its applications an entirely new dimension. Let’s examine the evolution of video stitching combined with AI/ML throughout time, the main difficulties developers have had to overcome, and the numerous uses for video stitching that use the newest AI-infused technology.
To know more about Video Stitching + AL/ML- Changing Landscape over the years, see https://www.logic-fruit.com/blog/al-ml/video-stitching-ai-ml/
About Logic Fruit Technologies
Logic Fruit Technologies is a product engineering R&D & consulting services provider for embedded systems and application development. We provide end-to-end solutions from the conception of the idea and design to the finished product. We have been servicing customers globally for over a decade.
The company has specific experience in various fields, such as
FPGA Design & hardware design
RTL IP Design
A variety of digital protocols
Communication buses as1G, 10G Ethernet
PCIe
DIGRF
STM16/64
HDMI.
Logic Fruit Technologies is also an expert in developing,
software-defined radio (SDR) IPs
Encryption
Signal generation
Data analysis, and
Multiple Image Processing Techniques.
Recently Logic Fruit technologies are also exploring FPGA acceleration on data centers for real-time data processing.
**Our Social Media Channels**
Facebook: https://www.facebook.com/LogicFruit/
Twitter: https://twitter.com/logicfruit
LinkedIn: https://www.linkedin.com/company/logi…
Website: https://www.logic-fruit.com/
#LFT #LogicFruitTechnologies #LogicFruit
Interested to view more SlideShares, Click on the below links,
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https://www.slideshare.net/LogicFruit/end-system-design-parameters-of-the-arinc-664-part-7
https://www.slideshare.net/LogicFruit/compute-express-link-cxl-everything-you-ought-to-know
https://www.logic-fruit.com/blog/fpga/what-is-fpga/
https://www.slideshare.net/LogicFruit/cxl-vs-pcie-gen-5-the-brief-comparison
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How to prepare a perfect video abstract for your research paper – Pubrica.pdfPubrica
A video abstract is a series of moving pictures taken from a lengthier movie that is significantly shorter than the original yet retains the original's essential meaning.
Learn More : https://bit.ly/3JVyrCW
Reference: https://pubrica.com/services/publication-support/Video-Abstract/
Why Pubrica:
When you order our services, we promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Bio statistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44-1618186353
Here are Important things about Image and Video Annotation that you should know for machine learning and to make your annotation project well & good your vision our thoughts.
Automatic semantic content extraction in videos using a fuzzy ontology and ru...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
How to prepare a perfect video abstract for your research paper – Pubrica.pptxPubrica
A video abstract is a series of moving pictures taken from a lengthier movie that is significantly shorter than the original yet retains the original's essential meaning.
Learn More : https://bit.ly/3JVyrCW
Reference: https://pubrica.com/services/publication-support/Video-Abstract/
Why Pubrica:
When you order our services, we promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Bio statistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44-1618186353
Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential.
In this presentation you’ll learn about:
- Why on-device AI is key
- Full-stack AI optimizations to make on-device AI possible and efficient
- Advanced techniques like quantization, distillation, and speculative decoding
- How generative AI models can be run on device and examples of some running now
- Qualcomm Technologies’ role in scaling on-device generative AI
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Collaborative Video Making Platform: Ideal for teams, the platform supports real-time collaboration and cloud-based storage, resulting in optimized workflows. Teams can concurrently script, edit, and finalize, speeding up production timelines and improving team productivity.
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"AdvertAi" being the "World's First App Fully Powered by Google's New AI Technology - Adanet & TensorFlow, Crafting Real-Time AI Ads Instantly." It's possible that this is a development that has occurred after my last update or it could be a product or service that hasn't gained significant visibility up until that point.
"This app's capabilities are truly unbelievable! With just a handful of voice commands, I transformed my concepts into captivating Ad Copies and Stunning Ad images and videos.
The AI technology it employs is undeniably remarkable, substantially reducing the time and energy I invest."-Henry J.
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Generative AI 101 A Beginners Guide.pdfSoluLab1231
Generative AI has emerged as a transformative technology in recent years, revolutionizing various industries with its potential to create original content such as images, text, and even music. The advancements in generative AI have enabled machines to learn, create and produce new content, leading to unprecedented innovation across various sectors. As a result, many companies are now considering generative AI technology and hiring Generative AI Development Companies to leverage its benefits and enhance their operations with AI-led automation.
Generative AI is the new future AI that focuses on learning, analyzing, and producing original content through machine learning algorithms. This technology is transforming businesses’ operations and enhancing their ability to provide customized solutions. It has become a hot topic in the market, with many companies investing in this technology to leverage its benefits.
Inverted File Based Search Technique for Video Copy Retrievalijcsa
A video copy detection system is a content-based search engine focusing on Spatio-temporal features. It
aims to find whether a query video segment is a copy of video from the video database or not based on the
signature of the video. It is hard to find whether a video is a copied video or a similar video since the
features of the content are very similar from one video to the other. The main focus is to detect that the
query video is present in the video database with robustness depending on the content of video and also by
fast search of fingerprints. The Fingerprint Extraction Algorithm and Fast Search Algorithm are adopted
to achieve robust, fast, efficient and accurate video copy detection. As a first step, the Fingerprint
Extraction algorithm is employed which extracts a fingerprint through the features from the image content
of video. The images are represented as Temporally Informative Representative Images (TIRI). Then the
next step is to find the presence of copy of a query video in a video database, in which a close match of its
fingerprint in the corresponding fingerprint database is searched using inverted-file-based method.
The journal publishes original works with practical significance and academic value. All papers submitted to IJMRR are subject to a double-blind Authors are invited to submit theoretical or empirical papers in all aspects of management, including strategy, human resources, marketing, operations, technology, information systems, finance and accounting, business economics, public sector management. peer review process. IJMRR is an international forum for research that advances the theory and practice of management.
Authors are invited to submit theoretical or empirical papers in all aspects of management, including strategy, human resources, marketing, operations, technology, information systems, finance and accounting, business economics, and public sector management. The journal publishes original works with practical significance and academic value.
Drones generate vast amounts of data, which is usually in the form of images or video streams. Identification of objects of interest, counting them, or detecting change over time, are some of the tasks that are monotonous and labor intensive.
FlytBase AI platform offers a complete solution to automate such tasks. It has been designed and optimised specifically for drone applications.
Content based video retrieval using discrete cosine transformnooriasukmaningtyas
A content based video retrieval (CBVR) framework is built in this paper.
One of the essential features of video retrieval process and CBVR is a color
value. The discrete cosine transform (DCT) is used to extract a query video
features to compare with the video features stored in our database. Average
result of 0.6475 was obtained by using the DCT after implementing it to the
database we created and collected, and on all categories. This technique was
applied on our database of video, check 100 database videos, 5 videos in
Keywords: each category.
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Here are Important things about Image and Video Annotation that you should know for machine learning and to make your annotation project well & good your vision our thoughts.
Automatic semantic content extraction in videos using a fuzzy ontology and ru...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
How to prepare a perfect video abstract for your research paper – Pubrica.pptxPubrica
A video abstract is a series of moving pictures taken from a lengthier movie that is significantly shorter than the original yet retains the original's essential meaning.
Learn More : https://bit.ly/3JVyrCW
Reference: https://pubrica.com/services/publication-support/Video-Abstract/
Why Pubrica:
When you order our services, we promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Bio statistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44-1618186353
Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential.
In this presentation you’ll learn about:
- Why on-device AI is key
- Full-stack AI optimizations to make on-device AI possible and efficient
- Advanced techniques like quantization, distillation, and speculative decoding
- How generative AI models can be run on device and examples of some running now
- Qualcomm Technologies’ role in scaling on-device generative AI
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Collaborative Video Making Platform: Ideal for teams, the platform supports real-time collaboration and cloud-based storage, resulting in optimized workflows. Teams can concurrently script, edit, and finalize, speeding up production timelines and improving team productivity.
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Generative AI has emerged as a transformative technology in recent years, revolutionizing various industries with its potential to create original content such as images, text, and even music. The advancements in generative AI have enabled machines to learn, create and produce new content, leading to unprecedented innovation across various sectors. As a result, many companies are now considering generative AI technology and hiring Generative AI Development Companies to leverage its benefits and enhance their operations with AI-led automation.
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Inverted File Based Search Technique for Video Copy Retrievalijcsa
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aims to find whether a query video segment is a copy of video from the video database or not based on the
signature of the video. It is hard to find whether a video is a copied video or a similar video since the
features of the content are very similar from one video to the other. The main focus is to detect that the
query video is present in the video database with robustness depending on the content of video and also by
fast search of fingerprints. The Fingerprint Extraction Algorithm and Fast Search Algorithm are adopted
to achieve robust, fast, efficient and accurate video copy detection. As a first step, the Fingerprint
Extraction algorithm is employed which extracts a fingerprint through the features from the image content
of video. The images are represented as Temporally Informative Representative Images (TIRI). Then the
next step is to find the presence of copy of a query video in a video database, in which a close match of its
fingerprint in the corresponding fingerprint database is searched using inverted-file-based method.
The journal publishes original works with practical significance and academic value. All papers submitted to IJMRR are subject to a double-blind Authors are invited to submit theoretical or empirical papers in all aspects of management, including strategy, human resources, marketing, operations, technology, information systems, finance and accounting, business economics, public sector management. peer review process. IJMRR is an international forum for research that advances the theory and practice of management.
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Content based video retrieval using discrete cosine transformnooriasukmaningtyas
A content based video retrieval (CBVR) framework is built in this paper.
One of the essential features of video retrieval process and CBVR is a color
value. The discrete cosine transform (DCT) is used to extract a query video
features to compare with the video features stored in our database. Average
result of 0.6475 was obtained by using the DCT after implementing it to the
database we created and collected, and on all categories. This technique was
applied on our database of video, check 100 database videos, 5 videos in
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AI IN REAL ESTATE: IMPACTING THE DYNAMICS OF THE MODERN PROPERTY MARKET.pdfStephenAmell4
The real estate industry has always been a significant pillar of the global economy, connecting buyers and sellers in the pursuit of properties for residential, commercial, or investment purposes. Traditionally, the process of buying, selling, and managing real estate has been largely manual, relying on human expertise and effort.
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Business Process Automation (BPA) stands as an essential paradigm shift in modern business operations. By melding technological advancements with strategic objectives, BPA offers a pathway to a streamlined, efficient, and strategically aligned business model. Its multifaceted applications, ranging from HR to marketing, exemplify the transformative potential of automation, setting a benchmark for the future of business innovation.
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Generative AI in the supply chain leverages advanced algorithms to autonomously create and optimize processes, enhancing efficiency and adaptability. This technology generates intelligent solutions, forecasts demand, and streamlines logistics, ultimately revolutionizing how businesses manage their supply chains by fostering agility and cost-effectiveness through data-driven decision-making.
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AI in business management: An Overview.pdfStephenAmell4
Business management involves overseeing and coordinating an organization’s various functions to effectively achieve its objectives and goals. It includes planning, organizing, leading, staffing, and controlling an organization’s human, financial, and technological resources to ensure efficient operation and the achievement of intended outcomes. Business management encompasses a wide range of responsibilities, from setting strategic goals and making high-level decisions to supervising employees, managing finances, and optimizing operations. Effective business management is crucial for the success of businesses across various industries.
AI in fleet management : An Overview.pdfStephenAmell4
Fleet management is the process of organizing, coordinating, and facilitating the operation and maintenance of a fleet of vehicles within a company or organization. It’s a procedural necessity and a strategic function vital for businesses and agencies where transportation is at the heart of service or product delivery. Its primary objective is to control costs, enhance productivity, and mitigate risks associated with operating a fleet of vehicles.
AI in fuel distribution control Exploring the use cases.pdfStephenAmell4
Fuel distribution control is the administration and supervision of the procedures used to transport different fuels, such as petrol, diesel, and aviation fuel, from production facilities to end-users, which might include consumers, companies, and industries. It includes all actions involved in the extraction, refinement, transportation, storage, and distribution of fuels, as well as its planning, coordination, and optimization.
An AI-based price engine is a pricing tool or system that leverages artificial intelligence and machine learning techniques to make pricing decisions and recommendations based on various factors and variables. The pricing engine goes beyond traditional rule-based approaches and incorporates advanced algorithms to analyze complex data patterns, customer behavior, market trends, and other relevant factors in real-time.
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.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
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!
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
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
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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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How to create a Generative video model?
leewayhertz.com/create-generative-video-model
Generative AI has become the buzzword of 2023. Whether text-generating ChatGPT or
image-generating Midjourney, generative AI tools have transformed businesses and
dominated the content creation industry. With Microsoft’s partnership with OpenAI and
Google creating its own AI-powered chatbot called Bard, it is fast growing into one of the
hottest areas within the tech sphere.
Generative AI aims to generate new data similar to the training dataset. It utilizes
machine learning algorithms called generative models to learn the patterns and
distributions underlying the training data. Although different generative models are
available that produce text, images, audio, codes and videos, this article will take a deep
dive into generative video models.
From generating video using text descriptions to generating new scenes and characters
and enhancing the quality of a video, generative video models offer a wealth of
opportunities for video content creators. Generative video platforms are often powered by
sophisticated models like GANs, VAEs, or CGANs, capable of translating human language
to build images and videos. In this article, you will learn about generative video models,
their advantages, and how they work, followed by a step-by-step guide on creating your
own generative video model
Generative models and their types
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Generative models create new data similar to the training data using machine learning
algorithms. To create new data, these models undergo a series of training wherein they
are exposed to large datasets. They learn the underlying patterns and relationships in the
training data to produce similar synthetic data based on their knowledge acquired from
the training. Once trained, these models take text prompts (sometimes image prompts) to
generate content based on the text.
There are several different types of generative models, including:
1. Generative Adversarial Networks (GANs): GANs are based on a two-part model,
where one part, called the generator, generates fake data, and the other, the
discriminator, evaluates the fake data’s authenticity. The generator’s goal is to
produce fake data that is so convincing that the discriminator cannot tell the
difference between fake and real data.
2. Stable Diffusion Models (SDMs): SDMs, also known as Flow-based Generative
Models, transform a simple random noise into more complex and structured data,
like an image or a video. They do this by defining a series of simple transformations,
called flows, that gradually change the random noise into the desired data.
3. Autoregressive Models: Autoregressive models generate data one piece at a time,
such as generating one word in a sentence at a time. They do this by predicting the
next piece of data based on the previous pieces.
4. Variational Autoencoders (VAEs): VAEs work by encoding the training data into a
lower-dimensional representation, known as a latent code, and then decoding the
latent code back into the original data space to generate new data. The goal is to find
the best latent code to generate data similar to the original data.
5. Convolutional Generative Adversarial Networks (CGANs): CGANs are a type of GAN
specifically designed for image and video data. They use convolutional neural
networks to learn the relationships between the different parts of an image or video,
making them well-suited for tasks like video synthesis.
These are some of the most typically used generative models, but many others have been
developed for specific use cases. The choice of which model to use will depend on the
specific requirements of the task at hand.
What is a generative video model?
Generative video models are machine learning algorithms that generate new video data
based on patterns and relationships learned from training datasets. In these models, the
underlying structure of the video data is learned, allowing it to be used to create synthetic
video data similar to the original ones. Different types of generative video models are
available, like GANs, VAEs, CGANs and more, each of which takes a different training
approach based on its unique infrastructure.
Generative video models mostly utilize text-to-video prompts where users can enter their
requirements through text, and the model generates the video using the textual
description. Depending on your tools, generative video models also utilize sketch or image
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prompts to generate videos.
What tasks can a generative video model perform?
A wide range of activities can be carried out by generative video models, including:
1. Video synthesis: Generative video models can be used to create new video frames to
complete a sequence that has only been partially completed. This can be handy for
creating new video footage from still photographs or replacing the missing frames in
a damaged movie.
2. Video style transfer: Transferring one video style to another using generative video
models enables the creation of innovative and distinctive visual effects. For instance,
to give a video a distinct look, the style of a well-known artwork could be applied.
3. Video compression: Generative video models can be applied to video compression,
which comprises encoding the original video into a lower-dimensional
representation and decoding it to produce a synthetic video comparable to the
original. Doing this makes it possible to compress video files without compromising
on quality.
4. Video super resolution: By increasing the resolution of poor-quality videos,
generative video models can make them seem sharper and more detailed.
5. Video denoising: Noise can be removed using generative video models to make
video data clearer and simpler to watch.
6. Video prediction: To do real-time video prediction tasks like autonomous driving or
security monitoring, generative video models can be implemented to forecast the
next frames in a video. Based on the patterns and relationships discovered from the
training data, the model can interpret the currently playing video data and produce
the next frames.
Benefits of generative video models
Compared to more conventional techniques, generative video models have a number of
benefits:
1. Efficiency: Generative video models can be taught on massive datasets of videos and
images to produce new videos quickly and efficiently in real time. This makes it
possible to swiftly and affordably produce large volumes of fresh video material.
2. Customization: With the right adjustments, generative video models can produce
video material that is adapted to a variety of needs, including style, genre, and tone.
This enables the development of video content with more freedom and flexibility.
3. Diversity: Generative video models can produce a wide range of video content,
including original scenes and characters and videos created from text descriptions.
This opens up new channels for the production and dissemination of video content.
4. Data augmentation: Generative video models can produce more training data for
computer vision and machine learning models, which can help these models
perform better and become more resilient to changes in the distribution of the data.
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5. Novelty: Generative video models can produce innovative and unique video content
that is still related to the training data creating new possibilities for investigating
novel forms of storytelling and video content.
How do generative video models work?
Like any other AI model, generative video models are trained on large data sets to
produce new videos. However, the training process varies from model to model
depending on the model’s architecture. Let us understand how this may work by taking
the example of two different models: VAE and GAN.
Variational Autoencoders (VAEs)
A Variational Autoencoder (VAE) is a generative model for generating videos and images.
In a VAE, two main components are present: an encoder and a decoder. An encoder maps
a video to a lower-dimensional representation, called a latent code, while a decoder
reverses the process.
A VAE uses encoders and decoders to model the distribution of videos in training data. In
the encoder, each video is mapped into a latent code, which becomes a parameter for
parametrizing a probability distribution (such as a normal distribution). To calculate a
reconstruction loss, the decoder maps the latent code back to a video, then compares it to
the original video.
To maximize the diversity of the generated videos, the VAE encourages the latent codes to
follow the prior distribution, which minimizes the reconstruction loss. After the VAE has
been trained, it can be leveraged to generate new videos by sampling latent codes from a
prior distribution and passing them through the decoder.
Generative Adversarial Networks (GANs)
GANs are deep learning model that generates images or videos when given a text prompt.
A GAN has two core components: a generator and a discriminator. Both the generator and
the discriminator, being neural networks, process the video input to generate different
kinds of output. While the generator generates fake videos, the discriminator assesses
these videos’ originality to provide feedback to the generator.
Using a random noise vector as input, the generator in the GAN generates a video.
Discriminators take in videos as input and produce probability scores indicating the
likelihood of the video is real. Here, the generator classifies the videos as real if taken
from the training data and the video generated by the generator is stamped as fake.
Generators and discriminators have trained adversarially during training. Generators are
trained to create fake videos that discriminators cannot detect, while discriminators are
trained to identify fake videos created by generators. The generator continues this process
until it produces videos that the discriminator can no longer distinguish from actual
videos.
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Following the training process, a noise vector can be sampled and passed through the
generator to generate a brand-new video. While incorporating some randomness and
diversity, the resultant videos should reflect the characteristics of the training data.
Random data
samples
Generator
Real/training
data sample
Generated
data sample
Fine tune training
Discriminator
Fine tune training
Random data
samples
Generator classifies as
real/fake
How does a GAN model work?
LeewayHertz
How to create a generative video model?
Here, we discuss how to create a generative video model similar to the VToonify
framework that combines the advantages of StyleGAN and Toonify frameworks.
Set up the environment
The first step to creating a generative video model is setting up the environment. To set up
the environment for creating a generative video model, you must decide on the right
programming language to write codes. Here, we are moving forward with Python. Next,
you must install several software packages, including a deep learning framework such as
TensorFlow or PyTorch, and any additional libraries you will need to preprocess and
visualize your data.
Install the following dependencies:
A deep learning framework like PyTorch, Keras or TensorFlow. For this tutorial, we
are using PyTorch. To install, run the following command:
pip install torch torchvision
Install Anaconda and CUDA toolkit based on your system.
Additional libraries that match your project requirements. We need the given
libraries to create a generative video model.
NumPy:
pip install numpy
OpenCV:
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pip install opencv-python
Matplotlib:
pip install matplotlib
Other necessary dependencies can be found here.You may need to modify the file
‘vtoonify_env.yaml‘ to install PyTorch that matches with your own CUDA version.
Set up a GPU environment for faster training. You can utilize cloud services like
Google Cloud Platform (GCP) or Amazon Web Services (AWS)
To train the model, obtain a dataset of images or video clips. Here, we are using this
dataset to train the model.
Model architecture design
You cannot create a generative video model without designing the architecture of the
model. It determines the quality and capacity of the generated video sequences.
Considering the sequential nature of video data is critical when designing the architecture
of the generative model since video sequences consist of multiple frames linked by time.
Combining CNNs with RNNs or creating a custom architecture may be an option.
As we are designing a model similar to VToonify, understanding in-depth about the
framework is necessary. So, what is VToonify?
VToonify is a framework developed by MMLab@NTU for generating high-quality artistic
portrait videos. It combines the advantages of two existing frameworks: the image
translation framework and the StyleGAN-based framework. The image translation
framework supports variable input size, but achieving high-resolution and controllable
style transfer is difficult. On the other hand, the StyleGAN-based framework is good for
high-resolution and controllable style transfer but is limited to fixed image size and may
lose details.
VToonify uses the StyleGAN model to achieve high-resolution and controllable style
transfer and removes its limitations by adapting the StyleGAN architecture into a fully
convolutional encoder-generator architecture. It uses an encoder to extract multi-scale
content features of the input frame and combines them with the StyleGAN model to
preserve the frame details and control the style. The framework has two instantiations,
namely, VToonify-T and VToonify-D, wherein the first uses Toonify and the latter follows
DualStyleGAN.
The backbone of VToonify-D is DualStyleGAN, developed by MMLab@NTU.
DualStyleGAN utilizes the benefits of StyleGAN and can be considered an advanced
version of it. In this article, we will be moving forward with VToonify-D.
The following steps need to be considered while designing a model architecture:
Determine the input and output data format.
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Since the model we develop is VToonify-like, human face sequences should be fed as
input to the generative model, and anime or cartoon face sequences should be the output.
Images, optical flows, or feature maps can be input and output data formats.
For your base architecture, choose StyleGAN, which utilizes the GAN model to give
the desired outcome.
Add the encoder-generator networks.
Write the following codes for the encoder network:
num_styles = int(np.log2(out_size)) * 2 - 2
encoder_res = [2**i for i in range(int(np.log2(in_size)), 4, -1)]
self.encoder = nn.ModuleList()
self.encoder.append(
nn.Sequential(
nn.Conv2d(img_channels+19, 32, 3, 1, 1, bias=True),
nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Conv2d(32, channels[in_size], 3, 1, 1, bias=True),
nn.LeakyReLU(negative_slope=0.2, inplace=True)))
for res in encoder_res:
in_channels = channels[res]
if res > 32:
out_channels = channels[res // 2]
block = nn.Sequential(
nn.Conv2d(in_channels, out_channels, 3, 2, 1, bias=True),
nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Conv2d(out_channels, out_channels, 3, 1, 1, bias=True),
nn.LeakyReLU(negative_slope=0.2, inplace=True))
self.encoder.append(block)
else:
layers = []
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for _ in range(num_res_layers):
layers.append(VToonifyResBlock(in_channels))
self.encoder.append(nn.Sequential(*layers))
block = nn.Conv2d(in_channels, img_channels, 1, 1, 0, bias=True)
self.encoder.append(block)
You can refer to this GitHub link to add the generator network.
Model training
First, you need to import argparse, math and random to start training the model. Run the
following commands to do so:
import argparse
import math
import random
After importing all prerequisites, specify the parameters for training. It includes total
training iterations, the batch size for each GPU, the local rank for distributed training, the
interval of saving a checkpoint, the learning rate and more. You can refer to the following
command lines to understand.
self.parser = argparse.ArgumentParser(description="Train VToonify-D")
self.parser.add_argument("--iter", type=int, default=2500, help="total training
iterations")
self.parser.add_argument("--batch", type=int, default=9, help="batch sizes for each
gpus")
self.parser.add_argument("--lr", type=float, default=0.0001, help="learning rate")
self.parser.add_argument("--local_rank", type=int, default=0, help="local rank for
distributed training")
self.parser.add_argument("--start_iter", type=int, default=0, help="start iteration")
self.parser.add_argument("--save_every", type=int, default=25000, help="interval of
saving a checkpoint")
self.parser.add_argument("--save_begin", type=int, default=35000, help="when to start
saving a checkpoint")
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self.parser.add_argument("--log_every", type=int, default=300, help="interval of saving
a checkpoint")
Next, we have to pre-train the encoder network for the model.
def pretrain(args, generator, g_optim, g_ema, parsingpredictor, down, directions, styles,
device):
pbar = range(args.iter)
if get_rank() == 0:
pbar = tqdm(pbar, initial=args.start_iter, dynamic_ncols=True, smoothing=0.01)
recon_loss = torch.tensor(0.0, device=device)
loss_dict = {}
if args.distributed:
g_module = generator.module
else:
g_module = generator
accum = 0.5 ** (32 / (10 * 1000))
requires_grad(g_module.encoder, True)
for idx in pbar:
i = idx + args.start_iter
if i > args.iter:
print("Done!")
break
Now train both the generator and the discriminator using paired data.
def train(args, generator, discriminator, g_optim, d_optim, g_ema, percept,
parsingpredictor, down, pspencoder, directions, styles, device):
pbar = range(args.iter)
if get_rank() == 0:
pbar = tqdm(pbar, initial=args.start_iter, smoothing=0.01, ncols=130,
dynamic_ncols=False)
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d_loss = torch.tensor(0.0, device=device)
g_loss = torch.tensor(0.0, device=device)
grec_loss = torch.tensor(0.0, device=device)
gfeat_loss = torch.tensor(0.0, device=device)
temporal_loss = torch.tensor(0.0, device=device)
gmask_loss = torch.tensor(0.0, device=device)
loss_dict = {}
surffix = '_s'
if args.fix_style:
surffix += '%03d'%(args.style_id)
surffix += '_d'
if args.fix_degree:
surffix += '%1.1f'%(args.style_degree)
if not args.fix_color:
surffix += '_c'
if args.distributed:
g_module = generator.module
d_module = discriminator.module
else:
g_module = generator
d_module = discriminator
In the above code snippet, the function ‘train’ establishes various loss tensors for the
generator and the discriminator and generates a dictionary of loss values. Using the
backpropagation algorithm, the algorithm loops over the specified number of iterations
and calculates and minimizes losses.
You can find the whole set of codes to train the model here.
Model evaluation and fine-tuning
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Model evaluation involves evaluating the model’s quality, efficiency, and effectiveness.
When developers evaluate a model carefully, they can identify areas for improvement and
fine-tune its parameters to improve its functionality. This process involves accessing the
quality of the generated video sequences using quantitative metrics such as structural
similarity index (SSIM), Mean Squared Error (MSE) or peak signal-to-noise ratio (PSNR)
and visually inspecting the generated video sequences.
Based on the evaluation results, fine-tune the model by adjusting the architecture,
configuration, or training process to improve its performance. It would be best to
optimize the hyperparameters, which involves adjusting the loss function, fine-tuning the
optimization algorithm and tweaking the model’s parameters to enhance the generative
video model’s performance.
Develop web UI
Building a web User Interface (UI) is necessary if your project needs the end-users to
interact with the video model. It enables users to feed input parameters like effects, style
types, image rescale, style degree or more. For this, you must design the layout,
topography, colors and other visual elements based on your set parameters.
Now, develop the front end as per the design. Once the UI is developed, test it thoroughly
to make it free of bugs and optimize the functionality. You can also use Gradio UI to build
custom UI for the project without coding requirements.
Deployment
Once the model is trained and fine-tuned and the web UI is built, the model needs to be
deployed to a production environment for generating new videos. Integration with a
mobile or web app, setting up a data processing and streaming pipeline, and configuring
the hardware and software infrastructure may be required to deploy the model based on
the requirement.
Wrapping up
The steps involved in creating a generative video model are complex and consist of
preprocessing the video dataset and designing the model architecture to adding layers to
the basic architecture and training and evaluating the model. Generative Adversarial
Networks (GANs) or Variational Autoencoders (VAEs) are frequently used as the
foundation architecture, and the model’s capacity and complexity can be increased by
including Convolutional, Pooling, Recurrent, or Dense layers.
There are several applications for generative video models, such as video synthesis, video
toonification, and video style transfer. Existing image-oriented models can be trained to
produce high-quality, artistic videos with adaptable style settings. The field of generative
video models is rapidly evolving, and new techniques and models are continually being
developed to improve the quality and flexibility of the generated videos.
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Fascinated by a generative video model’s capabilities and want to leverage its power to
level up your business? Contact LeewayHertz today to start building your own
generative video model and transform your vision into reality!