The document provides an overview of artificial neural networks and deep learning. It begins with introductions to AI and machine learning, then discusses the history and basic concepts of artificial neural networks, including neurons, biological neural networks, and how ANNs learn through backpropagation. It also covers deep learning approaches like convolutional neural networks, recurrent neural networks, attention models, and recent achievements in language modeling. Examples of applications like autonomous vehicles are presented. It concludes with discussions of capsule networks and the SAS platform for deep learning.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/09/from-inference-to-action-ai-beyond-pattern-recognition-a-keynote-presentation-from-pieter-abbeel/
Professor Pieter Abbeel, Director of the Berkeley Robot Learning Lab and Co-Director of the Berkeley Artificial Intelligence (BAIR) Lab, presents the “From Inference to Action: AI Beyond Pattern Recognition” tutorial at the May 2021 Embedded Vision Summit.
Pattern recognition—such as that used in image recognition, speech recognition and machine translation—has been the primary focus of the last decade’s progress in artificial intelligence. But intelligence fundamentally requires more than mere pattern recognition: It also requires the ability to achieve goal-oriented behaviors. Two new methods, deep reinforcement learning and deep imitation learning, provide paradigms for learning goal-oriented behaviors and have shown great promise in recent research. These approaches have demonstrated remarkable success in learning to play video games, learning to control simulated and real robots, mastering the classical game of Go and automation of character animation.
In this talk, Abbeel describes the ideas underlying these advances, and their current capabilities and limitations, with a focus on practical applications. He explores the characteristics that have unlocked important new use cases (e.g. AI robotic automation in warehouses) while others (e.g., self-driving cars) remain AI-bottlenecked. He also highlights important areas where significant breakthroughs are still needed.
This presentation attempts to explain some of the concepts used when describing data science, machine learning, and deep learning. IT also describes data science as a process, rather than as a set of specific tools and services.
Deep Learning Projects - Anomaly Detection Using Deep LearningDezyreAcademy
Deep Learning Projects- Learn how to use state-of-the-art deep learning methods and autoencoders for anomaly detection.
Check out other interesting deep learning project ideas here - https://www.dezyre.com/projects/data-science-projects/deep-learning-projects
Deep Learning: Towards General Artificial IntelligenceRukshan Batuwita
For the past several years Deep Learning methods have revolutionized the areas in Pattern Recognition, namely, Computer Vision, Speech Recognition, Natural Language Processing etc. These techniques have been mainly developed by academics, closely working with tech giants such as Google, Microsoft and Facebook where the research outcomes have been successfully integrated into commercial products such as Google image and voice search, Google Translate, Microsoft Cortana, Facebook M and many more interesting applications that are yet to come. More recently, Google DeepMind Technologies has been working on Artificial General Intelligence using Deep Reinforcement Learning methods, where their AlphaGo system beat the world champion of the complex Chinese game 'Go' in March 2016. This talk will present a thorough introduction to major Deep Learning techniques, recent breakthroughs and some exciting applications.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/09/from-inference-to-action-ai-beyond-pattern-recognition-a-keynote-presentation-from-pieter-abbeel/
Professor Pieter Abbeel, Director of the Berkeley Robot Learning Lab and Co-Director of the Berkeley Artificial Intelligence (BAIR) Lab, presents the “From Inference to Action: AI Beyond Pattern Recognition” tutorial at the May 2021 Embedded Vision Summit.
Pattern recognition—such as that used in image recognition, speech recognition and machine translation—has been the primary focus of the last decade’s progress in artificial intelligence. But intelligence fundamentally requires more than mere pattern recognition: It also requires the ability to achieve goal-oriented behaviors. Two new methods, deep reinforcement learning and deep imitation learning, provide paradigms for learning goal-oriented behaviors and have shown great promise in recent research. These approaches have demonstrated remarkable success in learning to play video games, learning to control simulated and real robots, mastering the classical game of Go and automation of character animation.
In this talk, Abbeel describes the ideas underlying these advances, and their current capabilities and limitations, with a focus on practical applications. He explores the characteristics that have unlocked important new use cases (e.g. AI robotic automation in warehouses) while others (e.g., self-driving cars) remain AI-bottlenecked. He also highlights important areas where significant breakthroughs are still needed.
This presentation attempts to explain some of the concepts used when describing data science, machine learning, and deep learning. IT also describes data science as a process, rather than as a set of specific tools and services.
Deep Learning Projects - Anomaly Detection Using Deep LearningDezyreAcademy
Deep Learning Projects- Learn how to use state-of-the-art deep learning methods and autoencoders for anomaly detection.
Check out other interesting deep learning project ideas here - https://www.dezyre.com/projects/data-science-projects/deep-learning-projects
Deep Learning: Towards General Artificial IntelligenceRukshan Batuwita
For the past several years Deep Learning methods have revolutionized the areas in Pattern Recognition, namely, Computer Vision, Speech Recognition, Natural Language Processing etc. These techniques have been mainly developed by academics, closely working with tech giants such as Google, Microsoft and Facebook where the research outcomes have been successfully integrated into commercial products such as Google image and voice search, Google Translate, Microsoft Cortana, Facebook M and many more interesting applications that are yet to come. More recently, Google DeepMind Technologies has been working on Artificial General Intelligence using Deep Reinforcement Learning methods, where their AlphaGo system beat the world champion of the complex Chinese game 'Go' in March 2016. This talk will present a thorough introduction to major Deep Learning techniques, recent breakthroughs and some exciting applications.
Deep Learning for Data Scientists - Data Science ATL Meetup Presentation, 201...Andrew Gardner
Note: these are the slides from a presentation at Lexis Nexis in Alpharetta, GA, on 2014-01-08 as part of the DataScienceATL Meetup. A video of this talk from Dec 2013 is available on vimeo at http://bit.ly/1aJ6xlt
Note: Slideshare mis-converted the images in slides 16-17. Expect a fix in the next couple of days.
---
Deep learning is a hot area of machine learning named one of the "Breakthrough Technologies of 2013" by MIT Technology Review. The basic ideas extend neural network research from past decades and incorporate new discoveries in statistical machine learning and neuroscience. The results are new learning architectures and algorithms that promise disruptive advances in automatic feature engineering, pattern discovery, data modeling and artificial intelligence. Empirical results from real world applications and benchmarking routinely demonstrate state-of-the-art performance across diverse problems including: speech recognition, object detection, image understanding and machine translation. The technology is employed commercially today, notably in many popular Google products such as Street View, Google+ Image Search and Android Voice Recognition.
In this talk, we will present an overview of deep learning for data scientists: what it is, how it works, what it can do, and why it is important. We will review several real world applications and discuss some of the key hurdles to mainstream adoption. We will conclude by discussing our experiences implementing and running deep learning experiments on our own hardware data science appliance.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-parodi
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Facundo Parodi, Research and Machine Learning Engineer at Tryolabs, presents the "An Introduction to Machine Learning and How to Teach Machines to See" tutorial at the May 2019 Embedded Vision Summit.
What is machine learning? How can machines distinguish a cat from a dog in an image? What’s the magic behind convolutional neural networks? These are some of the questions Parodi answers in this introductory talk on machine learning in computer vision.
Parodi introduces machine learning and explores the different types of problems it can solve. He explains the main components of practical machine learning, from data gathering and training to deployment. Parodi then focuses on deep learning as an important machine learning technique and provides an introduction to convolutional neural networks and how they can be used to solve image classification problems. He also touches on recent advancements in deep learning and how they have revolutionized the entire field of computer vision.
"You Can Do It" by Louis Monier (Altavista Co-Founder & CTO) & Gregory Renard (CTO & Artificial Intelligence Lead Architect at Xbrain) for Deep Learning keynote #0 at Holberton School (http://www.meetup.com/Holberton-School/events/228364522/)
If you want to assist to similar keynote for free, checkout http://www.meetup.com/Holberton-School/
A simplified way of approaching machine learning and deep learning from the ground up. The case for deep learning and an attempt to develop intuition for how/why it works. Advantages, state-of-the-art, and trends.
Presented at NYU Center for Genomics for NY Deep Learning Meetup
Tijmen Blankenvoort, co-founder Scyfer BV, presentation at Artificial Intelligence Meetup 15-1-2014. Introduction into Neural Networks and Deep Learning.
Deep learning an Introduction with Competitive LandscapeShivaji Dutta
This gives an introduction to Neural Networks to CNN, RNN, Reinforcement Learning to what competitive tools are out there. Also a comparison of the various frameworks from Tensorflow, Caffe, Chainer and Pytorch. We also capture the work done by various other companies in the enterprise tools space, web service offerings from Google, Sales Force and Amazon. End we mention the various Heroes of the Deep Learning space.
This ppt contains basic idea of deep learning, motivation behind deep learning, technical overview of AI and deep learning, types of learning approaches, basic DL model development steps and at last applications of deep learning including various areas where deep learning can prominently applied.
Mengenal Machine/Deep Learning, Artificial Intelligence dan mengenal apa bedanya dengan Business Intelligence, apa hubungannya dengan Big Data dan Data Science/Analytics.
AI, machine learning, especially deep learning achieved a lot of milestones in the last few years. I have followed the latest trends in AI for years. Then, I noticed something interesting. Between the 5 years period from 2012 to 2016, a lot of new things happened and it’s hard to follow everything. However, in 2017, there were only a handful of breakthroughs. This provided us a very rare opportunity window. If we can identify and follow the handful of breakthroughs, we have the potential to stay cutting edge like the big players. And this is the motivation of my talk.
deep-learning-and-what's-next-with-Chinese-annotationTao Wang
AI, machine learning, especially deep learning achieved a lot of milestones in the last few years. This presentation with start with some basics of AI and machine learning. Then, it will focus on deep learning and some latest trends. Many pages have Chinese annotation because I gave this talk to CAST-NC which consists of mainly Chinese living in North Carolina.
Deep Learning for Data Scientists - Data Science ATL Meetup Presentation, 201...Andrew Gardner
Note: these are the slides from a presentation at Lexis Nexis in Alpharetta, GA, on 2014-01-08 as part of the DataScienceATL Meetup. A video of this talk from Dec 2013 is available on vimeo at http://bit.ly/1aJ6xlt
Note: Slideshare mis-converted the images in slides 16-17. Expect a fix in the next couple of days.
---
Deep learning is a hot area of machine learning named one of the "Breakthrough Technologies of 2013" by MIT Technology Review. The basic ideas extend neural network research from past decades and incorporate new discoveries in statistical machine learning and neuroscience. The results are new learning architectures and algorithms that promise disruptive advances in automatic feature engineering, pattern discovery, data modeling and artificial intelligence. Empirical results from real world applications and benchmarking routinely demonstrate state-of-the-art performance across diverse problems including: speech recognition, object detection, image understanding and machine translation. The technology is employed commercially today, notably in many popular Google products such as Street View, Google+ Image Search and Android Voice Recognition.
In this talk, we will present an overview of deep learning for data scientists: what it is, how it works, what it can do, and why it is important. We will review several real world applications and discuss some of the key hurdles to mainstream adoption. We will conclude by discussing our experiences implementing and running deep learning experiments on our own hardware data science appliance.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-parodi
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Facundo Parodi, Research and Machine Learning Engineer at Tryolabs, presents the "An Introduction to Machine Learning and How to Teach Machines to See" tutorial at the May 2019 Embedded Vision Summit.
What is machine learning? How can machines distinguish a cat from a dog in an image? What’s the magic behind convolutional neural networks? These are some of the questions Parodi answers in this introductory talk on machine learning in computer vision.
Parodi introduces machine learning and explores the different types of problems it can solve. He explains the main components of practical machine learning, from data gathering and training to deployment. Parodi then focuses on deep learning as an important machine learning technique and provides an introduction to convolutional neural networks and how they can be used to solve image classification problems. He also touches on recent advancements in deep learning and how they have revolutionized the entire field of computer vision.
"You Can Do It" by Louis Monier (Altavista Co-Founder & CTO) & Gregory Renard (CTO & Artificial Intelligence Lead Architect at Xbrain) for Deep Learning keynote #0 at Holberton School (http://www.meetup.com/Holberton-School/events/228364522/)
If you want to assist to similar keynote for free, checkout http://www.meetup.com/Holberton-School/
A simplified way of approaching machine learning and deep learning from the ground up. The case for deep learning and an attempt to develop intuition for how/why it works. Advantages, state-of-the-art, and trends.
Presented at NYU Center for Genomics for NY Deep Learning Meetup
Tijmen Blankenvoort, co-founder Scyfer BV, presentation at Artificial Intelligence Meetup 15-1-2014. Introduction into Neural Networks and Deep Learning.
Deep learning an Introduction with Competitive LandscapeShivaji Dutta
This gives an introduction to Neural Networks to CNN, RNN, Reinforcement Learning to what competitive tools are out there. Also a comparison of the various frameworks from Tensorflow, Caffe, Chainer and Pytorch. We also capture the work done by various other companies in the enterprise tools space, web service offerings from Google, Sales Force and Amazon. End we mention the various Heroes of the Deep Learning space.
This ppt contains basic idea of deep learning, motivation behind deep learning, technical overview of AI and deep learning, types of learning approaches, basic DL model development steps and at last applications of deep learning including various areas where deep learning can prominently applied.
Mengenal Machine/Deep Learning, Artificial Intelligence dan mengenal apa bedanya dengan Business Intelligence, apa hubungannya dengan Big Data dan Data Science/Analytics.
AI, machine learning, especially deep learning achieved a lot of milestones in the last few years. I have followed the latest trends in AI for years. Then, I noticed something interesting. Between the 5 years period from 2012 to 2016, a lot of new things happened and it’s hard to follow everything. However, in 2017, there were only a handful of breakthroughs. This provided us a very rare opportunity window. If we can identify and follow the handful of breakthroughs, we have the potential to stay cutting edge like the big players. And this is the motivation of my talk.
deep-learning-and-what's-next-with-Chinese-annotationTao Wang
AI, machine learning, especially deep learning achieved a lot of milestones in the last few years. This presentation with start with some basics of AI and machine learning. Then, it will focus on deep learning and some latest trends. Many pages have Chinese annotation because I gave this talk to CAST-NC which consists of mainly Chinese living in North Carolina.
Deep learning beyond the learning - Jörg Schad - Codemotion Rome 2018 Codemotion
Open Source frameworks such as TensorFlow, MXNet, or PyTorch enable anyone to model and train Deep Neural Networks. While there are many great tutorials and talks showing us the best ways for training models, there is few information on what happens after we have trained our model? How can we store, utilize, and update it? In this talk, we look at the complete Deep Learning Pipeline and looks at topics such as deployments, multi-tenancy, jupyter notebooks, model serving, and more.
Building a Brain with Raspberry Pi and Zulu Embedded JVMSimon Ritter
This session takes a cluster of low cost Raspberry Pis using Azul's Zulu JVM and some open source libraries (DL4J and NL4J) and explains some of the basics of machine learning, deep learning and reinforcement learning. This has been applied using Project Malomo from Microsoft to develop an automated system that plays Minecraft!
This is an 1 hour presentation on Neural Networks, Deep Learning, Computer Vision, Recurrent Neural Network and Reinforcement Learning. The talks later have links on how to run Neural Networks on
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/luxoft/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Alexey Rybakov, Senior Director for Embedded Systems at Luxoft, presents the "Deep Learning Beyond Cats and Cars: Developing a Real-life DNN-based Embedded Vision Product for Agriculture, Construction, Medical, or Retail" tutorial at the May 2017 Embedded Vision Summit.
By now we know very well how to design and train a neural network to recognize cats, dogs and cars. But what about real projects — for example, in agriculture, construction, medical, and retail? This how-to talk provides an overview of what it takes to design, train, and fine-tune a real-life DNN-based embedded vision solution. Rybakov explores algorithmic, data set, training, and optimization decisions that take you from proofs-of-concepts to solid, reliable, and highly optimized systems. This material is based on Luxoft's own successes, failures, and lessons learned while implementing embedded vision solutions.
MachinaFiesta: A Vision into Machine Learning 🚀GDSCNiT
🕵️♂️ Embark on an exhilarating journey into the realm of Machine learning and Generative AI with MachinaFiesta! 🚀. Join us for MachinaFiesta, a two-hour event exploring the fascinating world of machine learning and generative AI where you can Vision, Innovate and learn new technologies.
Slide contets:
🎤 Brief introduction to the agenda and speakers of the event
🌐 Get to know the importance and future prospects of machine learning
🧠 Interactive session on core machine learning concepts
🚀 Exploration of cutting-edge generative AI advancements
🤖 Introduction to Gemini, the open-source factual language model
🤔Discussion on Gemini's capabilities and potential applications in research and development
This is a 2 hours overview on the deep learning status as for Q1 2017.
Starting with some basic concepts, continue to basic networks topologies , tools, HW/Accelerators and finally Intel's take on the the different fronts.
Software Modeling and Artificial Intelligence: friends or foes?Jordi Cabot
See how modeling can help the AI world (e.g. a model-driven approach to build chatbots) and how AI can create smarter modeling tools (e.g. using ML to learn transformations and code generation templates)
Deep learning beyond the learning - Jörg Schad - Codemotion Amsterdam 2018Codemotion
Open Source frameworks such as TensorFlow, MXNet, or PyTorch enable anyone to model and train Deep Neural Networks. While there are many great tutorials and talks showing us the best ways for training models, there is few information on what happens after we have trained our model? How can we store, utilize, and update it? In this talk, we look at the complete Deep Learning Pipeline and looks at topics such as deployments, multi-tenancy, jupyter notebooks, model serving, and more.
Smart Data Webinar: Machine Learning UpdateDATAVERSITY
Machine Learning (ML) approaches and their supporting technologies can generally be classified as Supervised vs Unsupervised, and within those categories as General or Deep Learning (with Reinforcement Learning as a special case within Supervised Learning). The approaches may be based on biological models or statistical models, or hybrids. As demand for machine learning functionality in consumer and enterprise applications increases, it becomes important to have a framework for comparing ML products and services.
This webinar will present an overview of the machine learning landscape, from platform providers to point solutions in each major ML category, and help participants understand their options for experimentation and deployment of ML-based applications.
Practical Artificial Intelligence: Deep Learning Beyond Cats and CarsAlexey Rybakov
Developing a Real-life DNN-based Embedded Vision Product
for Agriculture, Construction, Medical, or Retail.
What it takes to succeed in a real-life development of a DNN-based embedded vision product? You have your hardware and software building blocks – want’s next? Learn how to plan and design for deep learning, how to select and cascade algorithms, where to get the training data and how much is enough, and how to optimize and troubleshoot your product.
By now we very well know how to design and train a neural network to recognize cats, dogs and cars. But what about real projects — agriculture, construction, medical, retail? This how-to talk will provide an overview of what it takes to design, train, and fine-tune a real-life DNN-based embedded vision solution. Presentation will explore algorithmic, data set, training, and optimization decisions that take you from proofs-of-concepts to solid, reliable, and highly optimized systems. This material is based on our own successes, failures, and other lessons we learnt while implementing embedded vision solutions over the past few years.
Alexey Rybakov is Senior Director with Luxoft, and manages software R&D, consulting and optimization services in artificial intelligence, deep learning, computer vision, and video processing.
The Frontier of Deep Learning in 2020 and BeyondNUS-ISS
This talk will be a summary of the recent advances in deep learning research, current trends in the industry, and the opportunities that lie ahead.
We will discuss topics in research such as:
Transformers, GPT-3, BERT
Neural Architecture Search, Evolutionary Search
Distillation, self-learning
NeRF
Self-Attention
Also shifting industry trends such as:
The move to free data
Rising importance of 3D vision
Using synthetic data (Sim2Real)
Mobile vision & Federated Learning
Handwritten Recognition using Deep Learning with RPoo Kuan Hoong
R User Group Malaysia Meet Up - Handwritten Recognition using Deep Learning with R
Source code available at: https://github.com/kuanhoong/myRUG_DeepLearning
Traditional Machine Learning had used handwritten features and modality-specific machine learning to classify images, text or recognize voices. Deep learning / Neural network identifies features and finds different patterns automatically. Time to build these complex tasks has been drastically reduced and accuracy has exponentially increased because of advancements in Deep learning. Neural networks have been partly inspired from how 86 billion neurons work in a human and become more of a mathematical and a computer problem. We will see by the end of the blog how neural networks can be intuitively understood and implemented as a set of matrix multiplications, cost function, and optimization algorithms.
AWS re:Invent 2016: Bringing Deep Learning to the Cloud with Amazon EC2 (CMP314)Amazon Web Services
Algorithmia is a startup with a mission to make state of the art machine learning discoverable by everyone&emdash;they offer the largest algorithm marketplace in the world, with over 2500 algorithms supporting tens of thousands of application developers. Algorithma is the first company to make deep learning, one of the most conceptually difficult areas of computing, accessible to any company via microservices. In this session, you learn how this startup has selected and optimized Amazon EC2 instances for various algorithms (including the latest generation of GPU optimized instances), to create a flexible and scalable platform. They also share their architecture and best practices for getting any computationally-intensive application started quickly.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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/
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.
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.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
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
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
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.
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
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.
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.