For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/auvizsystems/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Nagesh Gupta, CEO and Founder of Auviz Systems, presents the "Trade-offs in Implementing Deep Neural Networks on FPGAs" tutorial at the May 2015 Embedded Vision Summit.
Video and images are a key part of Internet traffic—think of all the data generated by social networking sites such as Facebook and Instagram—and this trend continues to grow. Extracting usable information from video and images is thus a growing requirement in the data center. For example, object and face recognition are valuable for a wide range of uses, from social applications to security applications. Deep neural networks are currently the most popular form of convolutional neural networks (CNN) used in data centers for such applications. 3D convolutions are a core part of CNNs. Nagesh presents alternative implementations of 3D convolutions on FPGAs, and discusses trade-offs among them.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/auvizsystems/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Nagesh Gupta, CEO and Founder of Auviz Systems, presents the "Trade-offs in Implementing Deep Neural Networks on FPGAs" tutorial at the May 2015 Embedded Vision Summit.
Video and images are a key part of Internet traffic—think of all the data generated by social networking sites such as Facebook and Instagram—and this trend continues to grow. Extracting usable information from video and images is thus a growing requirement in the data center. For example, object and face recognition are valuable for a wide range of uses, from social applications to security applications. Deep neural networks are currently the most popular form of convolutional neural networks (CNN) used in data centers for such applications. 3D convolutions are a core part of CNNs. Nagesh presents alternative implementations of 3D convolutions on FPGAs, and discusses trade-offs among them.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/auvizsystems/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Nagesh Gupta, Founder and CEO of Auviz Systems, presents the "Semantic Segmentation for Scene Understanding: Algorithms and Implementations" tutorial at the May 2016 Embedded Vision Summit.
Recent research in deep learning provides powerful tools that begin to address the daunting problem of automated scene understanding. Modifying deep learning methods, such as CNNs, to classify pixels in a scene with the help of the neighboring pixels has provided very good results in semantic segmentation. This technique provides a good starting point towards understanding a scene. A second challenge is how such algorithms can be deployed on embedded hardware at the performance required for real-world applications. A variety of approaches are being pursued for this, including GPUs, FPGAs, and dedicated hardware.
This talk provides insights into deep learning solutions for semantic segmentation, focusing on current state of the art algorithms and implementation choices. Gupta discusses the effect of porting these algorithms to fixed-point representation and the pros and cons of implementing them on FPGAs.
Massively Parallel K-Nearest Neighbor Computation on Distributed Architectures Intel® Software
This session discuss the implementation and performance of the K-nearest neighbor (KNN) computation on a distributed architecture using the Intel® Xeon Phi™ processor.
Urs Köster - Convolutional and Recurrent Neural NetworksIntel Nervana
Speaker: Urs Köster, PhD
Urs will join us to dive deep into the field of Deep Learning and focus on Convolutional and Recurrent Neural Networks. The talk will be followed by a workshop highlighting neon™, an open source python based deep learning framework that has been built from the ground up for speed and ease of use.
Startup.Ml: Using neon for NLP and Localization Applications Intel Nervana
Speaker: Arjun Bansal, co-founder of Nervana Systems
Arjun Bansal’s workshop focused on neon, an open-source python based deep learning framework that has been build from the ground up for speed and ease of use. The workshop highlights how to use neon, build Recurrent Recurrent Neural Networks to generate and analyze text, and build Convolutional Autoencoders to generate images and to localize objects. Arjun also demoed the integration of neon with the Nervana cloud (in private beta) for multi-GPU training of deep networks.
Accelerate Machine Learning Software on Intel Architecture Intel® Software
This session presents performance data for deep learning training for image recognition that achieves greater than 24 times speedup performance with a single Intel® Xeon Phi™ processor 7250 when compared to Caffe*. In addition, we present performance data that shows training time is further reduced by 40 times the speedup with a 128-node Intel® Xeon Phi™ processor cluster over Intel® Omni-Path Architecture (Intel® OPA).
Presentation of few recent papers on Deep Learning ... in particular Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding, Song Han, Huizi Mao, William J. Dally International Conference on Learning Representations ICLR2016
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/altera/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Deshanand Singh, Director of Software Engineering at Altera, presents the "Efficient Implementation of Convolutional Neural Networks using OpenCL on FPGAs" tutorial at the May 2015 Embedded Vision Summit.
Convolutional neural networks (CNN) are becoming increasingly popular in embedded applications such as vision processing and automotive driver assistance systems. The structure of CNN systems is characterized by cascades of FIR filters and transcendental functions. FPGA technology offers a very efficient way of implementing these structures by allowing designers to build custom hardware datapaths that implement the CNN structure. One challenge of using FPGAs revolves around the design flow that has been traditionally centered around tedious hardware description languages.
In this talk, Deshanand gives a detailed explanation of how CNN algorithms can be expressed in OpenCL and compiled directly to FPGA hardware. He gives detail on code optimizations and provides comparisons with the efficiency of hand-coded implementations.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2021/02/introduction-to-the-tvm-open-source-deep-learning-compiler-stack-a-presentation-from-octoml/
Luis Ceze, Co-founder and CEO of OctoML, a Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, and Venture Partner at Madrona Venture Group, presents the “Introduction to the TVM Open Source Deep Learning Compiler Stack” tutorial at the September 2020 Embedded Vision Summit.
There is an increasing need to bring machine learning to a wide diversity of hardware devices. Current frameworks rely on vendor-specific operator libraries and optimize for a narrow range of server-class GPUs. Deploying workloads to new platforms — such as mobile phones, embedded devices, and accelerators — requires significant manual effort.
In this talk, Ceze presents his work on the TVM stack, which exposes graph- and operator-level optimizations to provide performance portability for deep learning workloads across diverse hardware back-ends. TVM solves optimization challenges specific to deep learning, such as high-level operator fusion, mapping to arbitrary hardware primitives and memory latency hiding. It also automates optimization of low-level programs to hardware characteristics by employing a novel, learning-based cost modeling method for rapid exploration of optimizations.
Squeezing Deep Learning Into Mobile PhonesAnirudh Koul
A practical talk by Anirudh Koul aimed at how to run Deep Neural Networks to run on memory and energy constrained devices like smart phones. Highlights some frameworks and best practices.
Improving Hardware Efficiency for DNN ApplicationsChester Chen
Speaker: Dr. Hai (Helen) Li is the Clare Boothe Luce Associate Professor of Electrical and Computer Engineering and Co-director of the Duke Center for Evolutionary Intelligence at Duke University
In this talk, I will introduce a few recent research spotlights by the Duke Center for Evolutionary Intelligence. The talk will start with the structured sparsity learning (SSL) method which attempts to learn a compact structure from a bigger DNN to reduce computation cost. It generates a regularized structure with high execution efficiency. Our experiments on CPU, GPU, and FPGA platforms show on average 3~5 times speedup of convolutional layer computation of AlexNet. Then, the implementation and acceleration of DNN applications on mobile computing systems will be introduced. MoDNN is a local distributed system which partitions DNN models onto several mobile devices to accelerate computations. ApesNet is an efficient pixel-wise segmentation network, which understands road scenes in real-time, and has achieved promising accuracy. Our prospects on the adoption of emerging technology will also be given at the end of this talk, offering the audiences an alternative thinking about the future evolution and revolution of modern computing systems.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/arm/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit-iodice
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Gian Marco Iodice, Software Engineer at ARM, presents the "Using SGEMM and FFTs to Accelerate Deep Learning" tutorial at the May 2016 Embedded Vision Summit.
Matrix Multiplication and the Fast Fourier Transform are numerical foundation stones for a wide range of scientific algorithms. With the emergence of deep learning, they are becoming even more important, particularly as use cases extend into mobile and embedded devices. In this presentation, lodice discusses and analyzes how these two key, computationally-intensive algorithms can be used to gain significant performance improvements for convolutional neural network (CNN) implementations.
After a brief introduction to the nature of CNN computations, Iodice explores the use of GEMM (General Matrix Multiplication) and mixed-radix FFTs to accelerate 3D convolution. He shows examples of OpenCL implementations of these functions and highlights their advantages, limitations and trade-offs. Central to the techniques explored is an emphasis on cache-efficient memory accesses and the crucial role of reduced-precision data types.
This slides explains how Convolution Neural Networks can be coded using Google TensorFlow.
Video available at : https://www.youtube.com/watch?v=EoysuTMmmMc
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/auvizsystems/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Nagesh Gupta, Founder and CEO of Auviz Systems, presents the "Semantic Segmentation for Scene Understanding: Algorithms and Implementations" tutorial at the May 2016 Embedded Vision Summit.
Recent research in deep learning provides powerful tools that begin to address the daunting problem of automated scene understanding. Modifying deep learning methods, such as CNNs, to classify pixels in a scene with the help of the neighboring pixels has provided very good results in semantic segmentation. This technique provides a good starting point towards understanding a scene. A second challenge is how such algorithms can be deployed on embedded hardware at the performance required for real-world applications. A variety of approaches are being pursued for this, including GPUs, FPGAs, and dedicated hardware.
This talk provides insights into deep learning solutions for semantic segmentation, focusing on current state of the art algorithms and implementation choices. Gupta discusses the effect of porting these algorithms to fixed-point representation and the pros and cons of implementing them on FPGAs.
Massively Parallel K-Nearest Neighbor Computation on Distributed Architectures Intel® Software
This session discuss the implementation and performance of the K-nearest neighbor (KNN) computation on a distributed architecture using the Intel® Xeon Phi™ processor.
Urs Köster - Convolutional and Recurrent Neural NetworksIntel Nervana
Speaker: Urs Köster, PhD
Urs will join us to dive deep into the field of Deep Learning and focus on Convolutional and Recurrent Neural Networks. The talk will be followed by a workshop highlighting neon™, an open source python based deep learning framework that has been built from the ground up for speed and ease of use.
Startup.Ml: Using neon for NLP and Localization Applications Intel Nervana
Speaker: Arjun Bansal, co-founder of Nervana Systems
Arjun Bansal’s workshop focused on neon, an open-source python based deep learning framework that has been build from the ground up for speed and ease of use. The workshop highlights how to use neon, build Recurrent Recurrent Neural Networks to generate and analyze text, and build Convolutional Autoencoders to generate images and to localize objects. Arjun also demoed the integration of neon with the Nervana cloud (in private beta) for multi-GPU training of deep networks.
Accelerate Machine Learning Software on Intel Architecture Intel® Software
This session presents performance data for deep learning training for image recognition that achieves greater than 24 times speedup performance with a single Intel® Xeon Phi™ processor 7250 when compared to Caffe*. In addition, we present performance data that shows training time is further reduced by 40 times the speedup with a 128-node Intel® Xeon Phi™ processor cluster over Intel® Omni-Path Architecture (Intel® OPA).
Presentation of few recent papers on Deep Learning ... in particular Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding, Song Han, Huizi Mao, William J. Dally International Conference on Learning Representations ICLR2016
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/altera/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Deshanand Singh, Director of Software Engineering at Altera, presents the "Efficient Implementation of Convolutional Neural Networks using OpenCL on FPGAs" tutorial at the May 2015 Embedded Vision Summit.
Convolutional neural networks (CNN) are becoming increasingly popular in embedded applications such as vision processing and automotive driver assistance systems. The structure of CNN systems is characterized by cascades of FIR filters and transcendental functions. FPGA technology offers a very efficient way of implementing these structures by allowing designers to build custom hardware datapaths that implement the CNN structure. One challenge of using FPGAs revolves around the design flow that has been traditionally centered around tedious hardware description languages.
In this talk, Deshanand gives a detailed explanation of how CNN algorithms can be expressed in OpenCL and compiled directly to FPGA hardware. He gives detail on code optimizations and provides comparisons with the efficiency of hand-coded implementations.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2021/02/introduction-to-the-tvm-open-source-deep-learning-compiler-stack-a-presentation-from-octoml/
Luis Ceze, Co-founder and CEO of OctoML, a Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, and Venture Partner at Madrona Venture Group, presents the “Introduction to the TVM Open Source Deep Learning Compiler Stack” tutorial at the September 2020 Embedded Vision Summit.
There is an increasing need to bring machine learning to a wide diversity of hardware devices. Current frameworks rely on vendor-specific operator libraries and optimize for a narrow range of server-class GPUs. Deploying workloads to new platforms — such as mobile phones, embedded devices, and accelerators — requires significant manual effort.
In this talk, Ceze presents his work on the TVM stack, which exposes graph- and operator-level optimizations to provide performance portability for deep learning workloads across diverse hardware back-ends. TVM solves optimization challenges specific to deep learning, such as high-level operator fusion, mapping to arbitrary hardware primitives and memory latency hiding. It also automates optimization of low-level programs to hardware characteristics by employing a novel, learning-based cost modeling method for rapid exploration of optimizations.
Squeezing Deep Learning Into Mobile PhonesAnirudh Koul
A practical talk by Anirudh Koul aimed at how to run Deep Neural Networks to run on memory and energy constrained devices like smart phones. Highlights some frameworks and best practices.
Improving Hardware Efficiency for DNN ApplicationsChester Chen
Speaker: Dr. Hai (Helen) Li is the Clare Boothe Luce Associate Professor of Electrical and Computer Engineering and Co-director of the Duke Center for Evolutionary Intelligence at Duke University
In this talk, I will introduce a few recent research spotlights by the Duke Center for Evolutionary Intelligence. The talk will start with the structured sparsity learning (SSL) method which attempts to learn a compact structure from a bigger DNN to reduce computation cost. It generates a regularized structure with high execution efficiency. Our experiments on CPU, GPU, and FPGA platforms show on average 3~5 times speedup of convolutional layer computation of AlexNet. Then, the implementation and acceleration of DNN applications on mobile computing systems will be introduced. MoDNN is a local distributed system which partitions DNN models onto several mobile devices to accelerate computations. ApesNet is an efficient pixel-wise segmentation network, which understands road scenes in real-time, and has achieved promising accuracy. Our prospects on the adoption of emerging technology will also be given at the end of this talk, offering the audiences an alternative thinking about the future evolution and revolution of modern computing systems.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/arm/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit-iodice
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Gian Marco Iodice, Software Engineer at ARM, presents the "Using SGEMM and FFTs to Accelerate Deep Learning" tutorial at the May 2016 Embedded Vision Summit.
Matrix Multiplication and the Fast Fourier Transform are numerical foundation stones for a wide range of scientific algorithms. With the emergence of deep learning, they are becoming even more important, particularly as use cases extend into mobile and embedded devices. In this presentation, lodice discusses and analyzes how these two key, computationally-intensive algorithms can be used to gain significant performance improvements for convolutional neural network (CNN) implementations.
After a brief introduction to the nature of CNN computations, Iodice explores the use of GEMM (General Matrix Multiplication) and mixed-radix FFTs to accelerate 3D convolution. He shows examples of OpenCL implementations of these functions and highlights their advantages, limitations and trade-offs. Central to the techniques explored is an emphasis on cache-efficient memory accesses and the crucial role of reduced-precision data types.
This slides explains how Convolution Neural Networks can be coded using Google TensorFlow.
Video available at : https://www.youtube.com/watch?v=EoysuTMmmMc
Presentation on Neural Networks in Tensorflow. Code available at https://github.com/nfmcclure/tensorflow_cookbook . Presentation for Open Source Bridge, Portland, 2016.
Delivered as plenary at USENIX LISA 2013. video here: https://www.youtube.com/watch?v=nZfNehCzGdw and https://www.usenix.org/conference/lisa13/technical-sessions/plenary/gregg . "How did we ever analyze performance before Flame Graphs?" This new visualization invented by Brendan can help you quickly understand application and kernel performance, especially CPU usage, where stacks (call graphs) can be sampled and then visualized as an interactive flame graph. Flame Graphs are now used for a growing variety of targets: for applications and kernels on Linux, SmartOS, Mac OS X, and Windows; for languages including C, C++, node.js, ruby, and Lua; and in WebKit Web Inspector. This talk will explain them and provide use cases and new visualizations for other event types, including I/O, memory usage, and latency.
Machine Learning Essentials Demystified part2 | Big Data DemystifiedOmid Vahdaty
achine Learning Essentials Abstract:
Machine Learning (ML) is one of the hottest topics in the IT world today. But what is it really all about?
In this session we will talk about what ML actually is and in which cases it is useful.
We will talk about a few common algorithms for creating ML models and demonstrate their use with Python. We will also take a peek at Deep Learning (DL) and Artificial Neural Networks and explain how they work (without too much math) and demonstrate DL model with Python.
The target audience are developers, data engineers and DBAs that do not have prior experience with ML and want to know how it actually works.
Separating Hype from Reality in Deep Learning with Sameer FarooquiDatabricks
Deep Learning is all the rage these days, but where does the reality of what Deep Learning can do end and the media hype begin? In this talk, I will dispel common myths about Deep Learning that are not necessarily true and help you decide whether you should practically use Deep Learning in your software stack.
I’ll begin with a technical overview of common neural network architectures like CNNs, RNNs, GANs and their common use cases like computer vision, language understanding or unsupervised machine learning. Then I’ll separate the hype from reality around questions like:
• When should you prefer traditional ML systems like scikit learn or Spark.ML instead of Deep Learning?
• Do you no longer need to do careful feature extraction and standardization if using Deep Learning?
• Do you really need terabytes of data when training neural networks or can you ‘steal’ pre-trained lower layers from public models by using transfer learning?
• How do you decide which activation function (like ReLU, leaky ReLU, ELU, etc) or optimizer (like Momentum, AdaGrad, RMSProp, Adam, etc) to use in your neural network?
• Should you randomly initialize the weights in your network or use more advanced strategies like Xavier or He initialization?
• How easy is it to overfit/overtrain a neural network and what are the common techniques to ovoid overfitting (like l1/l2 regularization, dropout and early stopping)?
Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017StampedeCon
This technical session provides a hands-on introduction to TensorFlow using Keras in the Python programming language. TensorFlow is Google’s scalable, distributed, GPU-powered compute graph engine that machine learning practitioners used for deep learning. Keras provides a Python-based API that makes it easy to create well-known types of neural networks in TensorFlow. Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to train neural networks of much greater complexity. Deep learning allows a model to learn hierarchies of information in a way that is similar to the function of the human brain.
Deep learning is making news across the country as one of the most promising techniques in machine learning research. However, these methods are complex to implement, finicky to tune, and state-of-the-art accuracy is only achieved by a few experts in the field. In this session, we give a beginner-friendly explanation of deep learning using neural networks—what it is, what it does, and how; and introduce the concept of deep features, which allows you to obtain great performance with reduced running times and data set sizes. We then show how these methods can easily be deployed on GPU instances (G2) on Amazon EC2.
Introduction to computer vision with Convoluted Neural NetworksMarcinJedyk
Introduction to computer vision with Convoluted Neural Networks - going over history of CNNs, describing basic concepts such as convolution and discussing applications of computer vision and image recognition technologies
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. During this workshop, we will provide a short background on Deep Learning focusing on relevant application domains and an introduction to the powerful and scalable Deep Learning framework, Apache MXNet. At the end of this tutorial you’ll be able to train your own deep neural network, fine tune existing state of the art models for image and object recognition. We’ll also deep dive on setting up your deep learning infrastructure on AWS and model deployment on AWS Lambda.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar
The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
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Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
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2. 2
DEEP LEARNING INSTITUTE
DLI Mission
Helping people solve challenging
problems using AI and deep learning.
• Developers, data scientists and
engineers
• Self-driving cars, healthcare and
robotics
• Training, optimizing, and deploying
deep neural networks
5. 5
WHAT THIS LAB IS
• Discussion/Demonstration of image segmentation using Deep
Learning
• Hands-on exercises using TensorFlow for CNN training and
evaluation of image segmentation workflow
6. 6
WHAT THIS LAB IS NOT
• Intro to machine learning from first principles
• Rigorous mathematical formalism of convolutional neural networks
• Survey of all the features and options of TensorFlow
7. 7
BACKGROUND ASSUMPTIONS
• You are familiar with convolutional neural networks (CNN)
• Helpful to have:
• Image recognition experience
• TensorFlow experience
• Python experience
8. 8
TAKE AWAYS
• You can setup your own image segmentation workflow in
TensorFlow and adapt it to your use case
• Know where to go for more info
• Familiarity with TensorFlow
11. 11
IMAGE SEGMENTATION
• “Segmentation” sometimes used to describe similar but slightly different tasks
• In this lab, semantic segmentation will be performed
• i.e., in an image, each pixel will be placed into one of multiple classes
• In a sense it’s a classification problem where each pixel has a class, vs image
recognition where each image (collection of pixels) has a class
• Specifically we’ll be looking at medical imaging data and attempting to determine
where the left ventricle (LV) is
• i.e., for each pixel is it part of LV or not?
13. 13
WHAT IS TENSORFLOW?
Created by Google, tensorflow.org
• “Open source software library for machine intelligence”
• Available on GitHub
• Flexibility—express your computation as a data flow graph
• If you can express it in TF syntax you can run it
• Portability—CPUs and GPUs, workstation, server, mobile
• Language options—Python and C++
• Performance—Tuned for performance on CPUs and GPUs
• Assign tasks to different hardware devices
• Uses CUDNN
TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.
14. 14
RUNNING TENSORFLOW
• Construct a graph—this happens before any real computation happens
• Specify your neural network as a graph
• Variables--characteristics of the graph that can change over time
• i.e., learned weights
• Operations—computations that combine the variables and the data
• e.g., convolution, activation, matrix multiply, etc.
• Launch a session
• This is TF verbiage for executing a graph
• Taking data and running it through a previously-created graph
15. 15
SAMPLE WORKFLOW
• Prepare input data
• Can use numpy arrays but for very large datasets TFRecords are recommended
• Build the computation graph
• Create inference, loss, training nodes
• Train the model
• Inject input data into graph in a TF session and loop over your input data.
• Specify things like batch size, number of epochs, learning rate, etc.
• Evaluate the model
• Run inference on graph and then evaluate accuracy based on suitable metric
16. 16
TENSORBOARD
• TF tool to visualize training progress
• Plots of loss, learning rate, accuracy
• Visualize computation graph
• Will use TensorBoard during this lab
• Extremely useful to aggregate training and evaluation statistics for clear analysis of
the model behavior
17. 17
TENSORBOARD GRAPH EXAMPLE
• Evaluation graph for NN with 1
hidden layer
• Each box clicks to expand
• Shows you the operations and the
variables in each user-defined
node
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DATASET
• Cardiac MRI short-axis (SAX) scans
• Sunnybrook cardiac images from earlier competition
http://smial.sri.utoronto.ca/LV_Challenge/Data.html
• "Sunnybrook Cardiac MR Database" is made available under the CC0 1.0 Universal
license described above, and with more detail here:
http://creativecommons.org/publicdomain/zero/1.0/
• Attribution:
• Radau P, Lu Y, Connelly K, Paul G, Dick AJ, Wright GA. "Evaluation Framework for
Algorithms Segmenting Short Axis Cardiac MRI." The MIDAS Journal -Cardiac MR Left
Ventricle Segmentation Challenge, http://hdl.handle.net/10380/3070
23. 23
DATA DETAILS
• Original images are 256 x 256 grayscale DICOM format
• Output is a tensor of size 256 x 256 x 2
• Each pixel belongs to one of two classes
• Training set consist of 234 images
• Validation set consist of 26 images
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BACKGROUND DATA SETUP
• Lots of guidance and code for how to setup/extract data taken from here:
• https://www.kaggle.com/c/second-annual-data-science-bowl/details/deep-learning-
tutorial
• Images and contours have been extracted from the raw data and packaged up for
ingest into TensorFlow
• Data extraction code is included but won’t be demo’d.
• TensorFlow data records provided but raw data is NOT provided for this lab
• If interested you can download yourself
25. 25
TASK 1
Ensure things are working properly!
• Train and test a fully-connected neural network with one hidden layer
• Visual representation of this network appears on next slide
• For the loss computation we’ll use TF built-in
sparse_softmax_cross_entropy_with_logits
• Computes softmax of the inference output then cross entropy against the correct
labels
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TASK 1 – NEURAL NETWORK
One hidden layer only
Input layer
65536 (256 x 256) pixels
hidden layer
512
output layer
256 x 256 x 2
27. 27
TASK 1 - TENSORBOARD
• You can choose the
train or eval graph
• This is the training
graph
• You can click to
expand any node to
see more detail
• If you run more
training and/or eval
runs, and refresh
your browser, the
stats will update
28. 28
TASK 2 - ADDITIONAL LAYERS
• Convolution layers
• Previous example focused on each input pixel
• What if features encompass multiple input pixels
• Can use convolutions to capture larger receptive fields
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TASK 2 - ADDITIONAL LAYERS
• Pooling layers
• Essentially a down-sampling method retaining information while
eliminating some computational complexity
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TASK 2 - ADDITIONAL LAYER
• Deconvolution (transpose convolution) layer
• Up-sampling method to bring a smaller image data set back up to it’s original size for
final pixel classification
• Long et al (CVPR2015) has nice paper re: FCN for segmentation
• Created FCNs from AlexNet and other canonical networks
• Zeiler et al (CVPR2010) describes deconvolution
• Network we will use is very similar to Vu Tran’s kaggle example here:
https://www.kaggle.com/c/second-annual-data-science-bowl/details/deep-
learning-tutorial
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TASK 2 - FULLY CONVOLUTIONAL NETWORK
(FCN)
• RECALL: Image classification layers—Convolutions, pooling, activations, fully
connected
• Output is an N-dimensional vector where N == Number_of_classes
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TASK 2 - FULLY CONVOLUTIONAL NETWORK
(FCN)
• Can we leverage this network to do segmentation? YES!
• Replace fully connected layer(s) with deconvolution (transpose convolution)
• Output is a 256 x 256 x N tensor where N == Number_of_classes
• In this lab N == 2
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TASK 3 - DICE METRIC
• Metric to compare the similarity of two samples:
2 𝐴 𝑛𝑙________________________________
𝐴 𝑛 + 𝐴𝑙
• Where:
• An is the area of the contour predicted by the network
• Al is the area of the contour from the label
• Anl is the intersection of the two
• The area of the contour that is predicted correctly by the network
• 1.0 means perfect score.
• More accurately compute how well we’re predicting the contour against the label
• We can just count pixels to give us the respective areas
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TASK 3 - TRAINING PARAMETERS
Important to search the space of parameters
• learning_rate: initial learning rate
• decay_rate: the rate that the initial learning rate decays
• e.g., 1.0 is no decay, 0.5 means cut the decay rate in half each number of (decay)
steps
• decay_steps: number of steps to execute before changing learning rate
• num_epochs: number of times to cycle through the input data
• batch_size: keep at 1 for now
• Experiment with learning_rate, decay_rate, decay_steps, num_epoch
• Record the parameters that give you the best Dice score
36. 36
ACCESSING LAB ENVIRONMENT
• Select the event
specific In-
Session Class in
the upper left
• Click the “Image
Segmentation
with TensorFlow”
Class from the
list
38. 38
LAB SUMMARY
• Intro to image segmentation
• Classifying pixels vs images
• Converted image recognition network into FCN for segmentation.
• Used TensorFlow as framework to explore various optimizations to FCN
• Explored new accuracy metric (Dice metric) to better capture true accuracy
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TASK 1 - TRAINING OUTPUT
!python exercises/simple/runTraining.py –-data_dir /data
Output:
OUTPUT: Step 0: loss = 2.621 (0.169 sec)
OUTPUT: Step 100: loss = 4.958 (0.047 sec)
OUTPUT: Step 200: loss = 4.234 (0.047 sec)
OUTPUT: Done training for 1 epochs, 231 steps.
Lots of messages printed to the screen - look for “OUTPUT”
40. 40
TASK 1 - EVALUATION
!python exercises/simple/runEval.py –-data_dir /data
Output:
OUTPUT: 2016-08-23 15:37:26.752794: accuracy = 0.504
OUTPUT: 26 images evaluated from file
/tmp/sunny_data/val_images.tfrecords
• Output shows the accuracy of the predictions and which data was utilized
• 1.0 means the NN classified all the data the same as the label, ie 100% correct
44. 44
TASK 2 - EVALUATION RESULTS
• 1 epoch of training
OUTPUT: 2016-08-26 20:44:55.012370: precision = 0.571
• 30 epochs of training
OUTPUT: 2016-08-26 20:48:16.593103: precision = 0.985
• 98.5% accurate!
• Very good accuracy, are we done?
45. 45
TASK 2 - ACCURACY
• How are we determining accuracy
• We are comparing the pixel value in the label with the value computed by the CNN
• So 98.5% of the time we are predicting the pixel correctly
• However, the size of the contour is relatively small compared to the entire image
Class imbalance problem
• If we simply output the notLV class for every pixel we’d have over 95% accuracy
• Clearly this isn’t what we want
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TASK 3 - EVALUTION RESULTS
• Recall result from prior example:
• 1 epoch: precision = 0.501
• 30 epochs: precision = 0.985
• Now with Dice metric (recall 1.0 is perfect accuracy)
• 1 epoch: Dice metric = 0.033
• 30 epochs: Dice metric = 0.579
• Not as good as we originally thought
47. 47
TASK 3 - RESULT
One possible result
--learning_rate=0.03
--decay_rate=0.75
--num_epochs=100
--decay_steps=10000
OUTPUT: 2016-08-26 21:22:15.590642: Dice metric = 0.861
Accuracy now looking much better!
48. 48
WHAT ELSE?
• Run training longer
• For demo purposes we ran really short training runs
• Need more epochs
• More training data
• We only had 236 images in our training set
• Gather more data
• Augment images that we have with rotations, inversions, etc.
• TF has functions to flip/rotate/transpose automatically
• Larger more complicated networks
49. 49
WHAT’S NEXT
• Use / practice what you learned
• Discuss with peers practical applications of DNN
• Reach out to NVIDIA and the Deep Learning Institute
50. 5050
WHAT’S NEXT
…for the chance to win an NVIDIA SHIELD
TV.
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