An overview of gradient descent optimization algorithms Hakky St
勾配降下法についての論文をスライドにしたものです。
This is the slide for study meeting of gradient descent.
I use this paper and this is very good information about gradient descent.
https://arxiv.org/abs/1609.04747
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Reducing the Cost of the Linear Growth Effect using Adaptive Rules with Unlin...Mark Proctor
Presentation given at ODBase 2018 to support the submitted conference paper. It covers the enhancements to the Rete algorithm to provide lazy rule evaluation through rule linking, the solution is implemented and benchmarked in the Drools rule engine.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
From Experimentation to Production: The Future of WebGLFITC
Presented at FITC Toronto 2017
More info at http://fitc.ca/event/to17/
Hector Arellano, Firstborn
Morgan Villedieu, Firstborn
Overview
You don’t need an advanced degree in graphics engineering to use WebGL as a robust solution in your web design and development. During this talk you will discover how to harness the power of WebGL for real-world application.
Objective
Discover real-world applications for advanced WebGL techniques
Target Audience
Designers or developers excited to conquer the complexity associated with WebGL
Five Things Audience Members Will Learn
Explore the outer limits of physics effects, shaders and experimentation
Understand how these techniques can be applied to transform 3D to 2D shadows and post-processing
Render real-time liquid in WebGL
Use DOM as a texture so you get the power of WebGL without having to worry about a fallback system
Master the basics by utilizing libraries
Temporal Superpixels Based on Proximity-Weighted Patch MatchingNAVER Engineering
발표자: 이세호(고려대 박사과정)
발표일: 2018.4.
슈퍼픽셀 알고리즘은 입력 영상을 다수의 의미 있는 영역으로 과분할하는 기법이다. 입력 영상을 픽셀 단위로 표현할 때와 비교하여, 슈퍼픽셀 단위의 표현은 입력 영상의 단위의 수를 크게 줄이는 장점이 있어, 여러 컴퓨터 비전 기법에 전처리로 이용된다. 또한 슈퍼픽셀 알고리즘을 동영상으로 확장한 동영상 슈퍼픽셀 (temporal superpixel) 알고리즘은 동영상 기반의 컴퓨터 비전 기법에 적용될 수 있다. 기존의 동영상 슈퍼픽셀 기법은 시간적 유사성을 유지하기 위하여 움직임 정보를 이용하는데, 움직임 정보의 추출에는 많은 계산 복잡도가 요구된다. 따라서 이를 보완하기 위해, 본 연구에서는 근접성 가중치 패치 정합 (proximity-weighted patch matching) 기반의 동영상 슈퍼픽셀 기법을 제안한다.
Nearest neighbor models are conceptually just about the simplest kind of model possible. The problem is that they generally aren’t feasible to apply. Or at least, they weren’t feasible until the advent of Big Data techniques. These slides will describe some of the techniques used in the knn project to reduce thousand-year computations to a few hours. The knn project uses the Mahout math library and Hadoop to speed up these enormous computations to the point that they can be usefully applied to real problems. These same techniques can also be used to do real-time model scoring.
How Machine Learning Helps Organizations to Work More Efficiently?Tuan Yang
Data is increasing day by day and so is the cost of data storage and handling. However, by understanding the concepts of machine learning one can easily handle the excessive data and can process it in an affordable manner.
The process includes making models by using several kinds of algorithms. If the model is created precisely for certain task, then the organizations have a very wide chance of making use of profitable opportunities and avoiding the risks lurking behind the scenes.
Learn more about:
» Understanding Machine Learning Objectives.
» Data dimensions in Machine Learning.
» Fundamentals of Algorithms and Mapping from Input/Output.
» Parametric and Non-parametric Machine Learning Algorithms.
» Supervised, Unsupervised and Semi-Supervised Learning.
» Estimating Over-fitting and Under-fitting.
» Use Cases.
Contour-Constrained Superpixels for Image and Video ProcessingNAVER Engineering
발표자: 이세호(고려대 박사과정)
발표일: 2017.8.
개요:
슈퍼픽셀 알고리즘은 입력 영상을 다수의 의미 있는 영역으로 과분할 하는 기법이다. 입력 영상을 픽셀 단위로 표현할 때와 비교하여, 슈퍼픽셀 단위의 표현은 입력 영상의 단위의 수를 크게 줄이는 장점이 있다. 각 슈퍼픽셀은 객체의 윤곽선을 넘어서는 영역을 포함하지 않는 동시에, 단일 객체만을 담아야 한다. 본 발표에서는 객체의 윤곽선 정보를 고려한 윤곽선 제약 슈퍼픽셀 기법(contour-constrained superpixel algorithm)을 제안한다.
Developing Breakout Models in FEMAP (Includes Tutorial Walk-throughs)Aswin John
The step-by-step walkthroughs of the presentation are included in the Appendix at the end of this presentation.
This presentation includes:
- Definition of Breakout Models
- When to use breakouts
- [Tutorial] Adding a pass-through in a wing rib
- [Tutorial] Adding boss to orthogrid pressure plate
Horizon: Deep Reinforcement Learning at ScaleDatabricks
To build a decision-making system, we must provide answers to two sets of questions: (1) ""What will happen if I make decision X?"" and (2) ""How should I pick which decision to make?"".
Typically, the first set of questions are answered with supervised learning: we build models to forecast whether someone will click on an ad, or visit a post. The second set of questions are more open-ended. In this talk, we will dive into how we can answer ""how"" questions, starting with heuristics and search. This will lead us to bandits, reinforcement learning, and Horizon: an open-source platform for training and deploying reinforcement learning models at massive scale. At Facebook, we are using Horizon, built using PyTorch 1.0 and Apache Spark, in a variety of AI-related and control tasks, spanning recommender systems, marketing & promotion distribution, and bandwidth optimization.
The talk will cover the key components of Horizon and the lessons we learned along the way that influenced the development of the platform.
Author: Jason Gauci
An overview of gradient descent optimization algorithms Hakky St
勾配降下法についての論文をスライドにしたものです。
This is the slide for study meeting of gradient descent.
I use this paper and this is very good information about gradient descent.
https://arxiv.org/abs/1609.04747
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Reducing the Cost of the Linear Growth Effect using Adaptive Rules with Unlin...Mark Proctor
Presentation given at ODBase 2018 to support the submitted conference paper. It covers the enhancements to the Rete algorithm to provide lazy rule evaluation through rule linking, the solution is implemented and benchmarked in the Drools rule engine.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
From Experimentation to Production: The Future of WebGLFITC
Presented at FITC Toronto 2017
More info at http://fitc.ca/event/to17/
Hector Arellano, Firstborn
Morgan Villedieu, Firstborn
Overview
You don’t need an advanced degree in graphics engineering to use WebGL as a robust solution in your web design and development. During this talk you will discover how to harness the power of WebGL for real-world application.
Objective
Discover real-world applications for advanced WebGL techniques
Target Audience
Designers or developers excited to conquer the complexity associated with WebGL
Five Things Audience Members Will Learn
Explore the outer limits of physics effects, shaders and experimentation
Understand how these techniques can be applied to transform 3D to 2D shadows and post-processing
Render real-time liquid in WebGL
Use DOM as a texture so you get the power of WebGL without having to worry about a fallback system
Master the basics by utilizing libraries
Temporal Superpixels Based on Proximity-Weighted Patch MatchingNAVER Engineering
발표자: 이세호(고려대 박사과정)
발표일: 2018.4.
슈퍼픽셀 알고리즘은 입력 영상을 다수의 의미 있는 영역으로 과분할하는 기법이다. 입력 영상을 픽셀 단위로 표현할 때와 비교하여, 슈퍼픽셀 단위의 표현은 입력 영상의 단위의 수를 크게 줄이는 장점이 있어, 여러 컴퓨터 비전 기법에 전처리로 이용된다. 또한 슈퍼픽셀 알고리즘을 동영상으로 확장한 동영상 슈퍼픽셀 (temporal superpixel) 알고리즘은 동영상 기반의 컴퓨터 비전 기법에 적용될 수 있다. 기존의 동영상 슈퍼픽셀 기법은 시간적 유사성을 유지하기 위하여 움직임 정보를 이용하는데, 움직임 정보의 추출에는 많은 계산 복잡도가 요구된다. 따라서 이를 보완하기 위해, 본 연구에서는 근접성 가중치 패치 정합 (proximity-weighted patch matching) 기반의 동영상 슈퍼픽셀 기법을 제안한다.
Nearest neighbor models are conceptually just about the simplest kind of model possible. The problem is that they generally aren’t feasible to apply. Or at least, they weren’t feasible until the advent of Big Data techniques. These slides will describe some of the techniques used in the knn project to reduce thousand-year computations to a few hours. The knn project uses the Mahout math library and Hadoop to speed up these enormous computations to the point that they can be usefully applied to real problems. These same techniques can also be used to do real-time model scoring.
How Machine Learning Helps Organizations to Work More Efficiently?Tuan Yang
Data is increasing day by day and so is the cost of data storage and handling. However, by understanding the concepts of machine learning one can easily handle the excessive data and can process it in an affordable manner.
The process includes making models by using several kinds of algorithms. If the model is created precisely for certain task, then the organizations have a very wide chance of making use of profitable opportunities and avoiding the risks lurking behind the scenes.
Learn more about:
» Understanding Machine Learning Objectives.
» Data dimensions in Machine Learning.
» Fundamentals of Algorithms and Mapping from Input/Output.
» Parametric and Non-parametric Machine Learning Algorithms.
» Supervised, Unsupervised and Semi-Supervised Learning.
» Estimating Over-fitting and Under-fitting.
» Use Cases.
Contour-Constrained Superpixels for Image and Video ProcessingNAVER Engineering
발표자: 이세호(고려대 박사과정)
발표일: 2017.8.
개요:
슈퍼픽셀 알고리즘은 입력 영상을 다수의 의미 있는 영역으로 과분할 하는 기법이다. 입력 영상을 픽셀 단위로 표현할 때와 비교하여, 슈퍼픽셀 단위의 표현은 입력 영상의 단위의 수를 크게 줄이는 장점이 있다. 각 슈퍼픽셀은 객체의 윤곽선을 넘어서는 영역을 포함하지 않는 동시에, 단일 객체만을 담아야 한다. 본 발표에서는 객체의 윤곽선 정보를 고려한 윤곽선 제약 슈퍼픽셀 기법(contour-constrained superpixel algorithm)을 제안한다.
Developing Breakout Models in FEMAP (Includes Tutorial Walk-throughs)Aswin John
The step-by-step walkthroughs of the presentation are included in the Appendix at the end of this presentation.
This presentation includes:
- Definition of Breakout Models
- When to use breakouts
- [Tutorial] Adding a pass-through in a wing rib
- [Tutorial] Adding boss to orthogrid pressure plate
Horizon: Deep Reinforcement Learning at ScaleDatabricks
To build a decision-making system, we must provide answers to two sets of questions: (1) ""What will happen if I make decision X?"" and (2) ""How should I pick which decision to make?"".
Typically, the first set of questions are answered with supervised learning: we build models to forecast whether someone will click on an ad, or visit a post. The second set of questions are more open-ended. In this talk, we will dive into how we can answer ""how"" questions, starting with heuristics and search. This will lead us to bandits, reinforcement learning, and Horizon: an open-source platform for training and deploying reinforcement learning models at massive scale. At Facebook, we are using Horizon, built using PyTorch 1.0 and Apache Spark, in a variety of AI-related and control tasks, spanning recommender systems, marketing & promotion distribution, and bandwidth optimization.
The talk will cover the key components of Horizon and the lessons we learned along the way that influenced the development of the platform.
Author: Jason Gauci
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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.
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
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
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.
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!
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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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.
35. shape scale artifacts
• large scale appears as slow motion
• avoid scaling, skew, shear in rigid transforms
• Use local scaling instead of global scaling
45. mass ratio artifacts
• pgs constraint solver doesn't handle it well
• heavy of top of light
46. mass ratio artifacts
• Keep masses all very similar
• Promising research in NNCG method
(quadratic convergence)
See http://iphys.wordpress.com/ and
http://jinngine.googlecode.com
49. warm starting artifacts
• applied impulses are similar each frame, cache them
• full warm starting can add energy so scale it down
• assume that the configuration hardly change
• this assumption doesn’t always hold, joint limits etc.
50. constraint solving order artifacts
• complex interaction between various constraints
• randomizing can help convergence
51. friction artifacts
• coupled versus decoupled friction
• friction pyramid approximation
• clamping of friction directions
• friction and warm starting