DeepScale develops perception systems for automated vehicles using redundant deep learning models. Their approach involves developing small and efficient neural networks that can run on embedded automotive processors, avoiding the need for power-hungry GPU servers. This allows their perception systems to be robust, accurate, redundant and efficient.
Forrest Iandola: My Adventures in Artificial Intelligence and EntrepreneurshipForrest Iandola
Slides to go with this talk:
https://www.youtube.com/watch?v=ocOxZM6jHNM
Originally presented at UC Berkeley -
A. Richard Newton Distinguished Innovator Lecture Series, March 5, 2018
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
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/09/explainability-in-computer-vision-a-machine-learning-engineers-overview-a-presentation-from-altaml/
Navaneeth Kamballur Kottayil, Lead Machine Learning Developer at AltaML, presents the “Explainability in Computer Vision: A Machine Learning Engineer’s Overview” tutorial at the May 2021 Embedded Vision Summit.
With the increasing use of deep neural networks in computer vision applications, it has become more difficult for developers to explain how their algorithms work. This can make it difficult to establish trust and confidence among customers and other stakeholders, such as regulators. Lack of explainability also makes it more difficult for developers to improve their solutions.
In this talk, Kottayil introduces methods for enabling explainability in deep-learning-based computer vision solutions. He also illustrates some of these techniques via real-world examples, and shows how they can be used to improve customer trust in computer vision models, to debug computer vision models, to obtain additional insights about data and to detect bias in models.
Deep Learning: Evolution of ML from Statistical to Brain-like Computing- Data...Impetus Technologies
Presentation on 'Deep Learning: Evolution of ML from Statistical to Brain-like Computing'
Speaker- Dr. Vijay Srinivas Agneeswaran,Director, Big Data Labs, Impetus
The main objective of the presentation is to give an overview of our cutting edge work on realizing distributed deep learning networks over GraphLab. The objectives can be summarized as below:
- First-hand experience and insights into implementation of distributed deep learning networks.
- Thorough view of GraphLab (including descriptions of code) and the extensions required to implement these networks.
- Details of how the extensions were realized/implemented in GraphLab source – they have been submitted to the community for evaluation.
- Arrhythmia detection use case as an application of the large scale distributed deep learning network.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit-baidu
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Dr. Ren Wu, former distinguished scientist at Baidu's Institute of Deep Learning (IDL), presents the keynote talk, "Enabling Ubiquitous Visual Intelligence Through Deep Learning," at the May 2015 Embedded Vision Summit.
Deep learning techniques have been making headlines lately in computer vision research. Using techniques inspired by the human brain, deep learning employs massive replication of simple algorithms which learn to distinguish objects through training on vast numbers of examples. Neural networks trained in this way are gaining the ability to recognize objects as accurately as humans.
Some experts believe that deep learning will transform the field of vision, enabling the widespread deployment of visual intelligence in many types of systems and applications. But there are many practical problems to be solved before this goal can be reached. For example, how can we create the massive sets of real-world images required to train neural networks? And given their massive computational requirements, how can we deploy neural networks into applications like mobile and wearable devices with tight cost and power consumption constraints?
In this talk, Ren shares an insider’s perspective on these and other critical questions related to the practical use of neural networks for vision, based on the pioneering work being conducted by his former team at Baidu.
Note 1: Regarding the ImageNet results included in this presentation, the organizers of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) have said: “Because of the violation of the regulations of the test server, these results may not be directly comparable to results obtained and reported by other teams.” (http://www.image-net.org/challenges/LSVRC/announcement-June-2-2015)
Note 2: The presenter, Ren Wu, has told the Embedded Vision Alliance that “There was some ambiguity with the rules. According to the ‘official’ interpretation of the rules, there should be no more than 52 submissions within a half year. For us, we achieved the reported results after 200 tests total within a half year. We believe there is no way to obtain any measurable gains, nor did we try to obtain any gains, from an 'extra' hundred tests as our networks have billions of parameters and are trained by tens of billions of training samples.”
Forrest Iandola: My Adventures in Artificial Intelligence and EntrepreneurshipForrest Iandola
Slides to go with this talk:
https://www.youtube.com/watch?v=ocOxZM6jHNM
Originally presented at UC Berkeley -
A. Richard Newton Distinguished Innovator Lecture Series, March 5, 2018
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
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/09/explainability-in-computer-vision-a-machine-learning-engineers-overview-a-presentation-from-altaml/
Navaneeth Kamballur Kottayil, Lead Machine Learning Developer at AltaML, presents the “Explainability in Computer Vision: A Machine Learning Engineer’s Overview” tutorial at the May 2021 Embedded Vision Summit.
With the increasing use of deep neural networks in computer vision applications, it has become more difficult for developers to explain how their algorithms work. This can make it difficult to establish trust and confidence among customers and other stakeholders, such as regulators. Lack of explainability also makes it more difficult for developers to improve their solutions.
In this talk, Kottayil introduces methods for enabling explainability in deep-learning-based computer vision solutions. He also illustrates some of these techniques via real-world examples, and shows how they can be used to improve customer trust in computer vision models, to debug computer vision models, to obtain additional insights about data and to detect bias in models.
Deep Learning: Evolution of ML from Statistical to Brain-like Computing- Data...Impetus Technologies
Presentation on 'Deep Learning: Evolution of ML from Statistical to Brain-like Computing'
Speaker- Dr. Vijay Srinivas Agneeswaran,Director, Big Data Labs, Impetus
The main objective of the presentation is to give an overview of our cutting edge work on realizing distributed deep learning networks over GraphLab. The objectives can be summarized as below:
- First-hand experience and insights into implementation of distributed deep learning networks.
- Thorough view of GraphLab (including descriptions of code) and the extensions required to implement these networks.
- Details of how the extensions were realized/implemented in GraphLab source – they have been submitted to the community for evaluation.
- Arrhythmia detection use case as an application of the large scale distributed deep learning network.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit-baidu
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Dr. Ren Wu, former distinguished scientist at Baidu's Institute of Deep Learning (IDL), presents the keynote talk, "Enabling Ubiquitous Visual Intelligence Through Deep Learning," at the May 2015 Embedded Vision Summit.
Deep learning techniques have been making headlines lately in computer vision research. Using techniques inspired by the human brain, deep learning employs massive replication of simple algorithms which learn to distinguish objects through training on vast numbers of examples. Neural networks trained in this way are gaining the ability to recognize objects as accurately as humans.
Some experts believe that deep learning will transform the field of vision, enabling the widespread deployment of visual intelligence in many types of systems and applications. But there are many practical problems to be solved before this goal can be reached. For example, how can we create the massive sets of real-world images required to train neural networks? And given their massive computational requirements, how can we deploy neural networks into applications like mobile and wearable devices with tight cost and power consumption constraints?
In this talk, Ren shares an insider’s perspective on these and other critical questions related to the practical use of neural networks for vision, based on the pioneering work being conducted by his former team at Baidu.
Note 1: Regarding the ImageNet results included in this presentation, the organizers of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) have said: “Because of the violation of the regulations of the test server, these results may not be directly comparable to results obtained and reported by other teams.” (http://www.image-net.org/challenges/LSVRC/announcement-June-2-2015)
Note 2: The presenter, Ren Wu, has told the Embedded Vision Alliance that “There was some ambiguity with the rules. According to the ‘official’ interpretation of the rules, there should be no more than 52 submissions within a half year. For us, we achieved the reported results after 200 tests total within a half year. We believe there is no way to obtain any measurable gains, nor did we try to obtain any gains, from an 'extra' hundred tests as our networks have billions of parameters and are trained by tens of billions of training samples.”
State-of-the-art Image Processing across all domainsKnoldus Inc.
Ever thought of going beyond TensorFlow, GPU or TPU to solve your image classification problems?
From the standpoint of deep learning, the problem of image processing can be solved in a much better way with Transfer Learning. It is a computer vision method that helps develop accurate models while saving a lot of time. This presentation will help you find out why it is so beneficial?
Agenda:
The history of image processing
What is Transfer Learning?
Introduction to Convolutional Neural Networks (CNNs)
Different types of CNN architectures like AlexNet, VGG, Inception, and ResNet
Performance of various CNN architectures
Solving a medical image diagnosis problem with the above-discussed architectures
A Distributed Deep Learning Approach for the Mitosis Detection from Big Medic...Databricks
The strongest indicator of a cancer patient's prognosis is the number of mitotic bodies that a pathologist manually counts from the high-resolution whole-slide histopathology images. Obviously, it is not efficient to manually count the mitosis number. But it is still challenging to automate the process of mitosis detection due to the limited training datasets and the intensive computing involved in the model training and inference. This presentation introduces a large-scale deep learning approach to train a two-stage CNN-based model with high accuracy to detect the mitosis locations directly from the high-resolution whole-slide images. In details, we first train a nuclei detection model to remove the background information from the raw whole-slide histopathology images. Second, a customized ResNet-50 model is trained on the cleaned dataset in the first step. The first step saves the training time while improving the model performance in the second step. A false-positive oversampling approach is used to further improve the model performance. With these models, the inference process is conducted to detect the mitosis locations from the large volume of histopathology images in parallel. Meanwhile, the whole pipeline, including data preprocessing, model training, hyperparameter tuning, and inference, is parallelized by utilizing the distributed TensorFlow, Apache Spark, and HDFS. The experiences and techniques in this project can be applied to other large scale deep learning problems as well.
Speaker: Fei Hu
Yangqing Jia at AI Frontiers: Towards Better DL FrameworksAI Frontiers
The last few years has seen an abundance of deep learning and general machine learning frameworks, and such frameworks have created deep impacts to the machine learning industry. In this talk, Yangqing shares and discusses lessons we learned from building deep learning and general machine learning framework designs in the last few years, and share thoughts and philosophy in building the next generation of machine learning solutions for the AI industry. When applicable he draws examples from Caffe, a widely adopted deep learning framework that has evolved to serve computer vision, speech recognition, natural language understanding.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/mathworks/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-hiremath-chou
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Sandeep Hiremath, Product Manager, and Bill Chou, Senior Computer Vision Scientist, both of MathWorks, present the "Deploying Deep Learning Models on Embedded Processors for Autonomous Systems with MATLAB" tutorial at the May 2019 Embedded Vision Summit.
In this presentation, Hiremath and Chou explain how to bring the power of deep neural networks to memory- and power-constrained devices like those used in robotics and automated driving. The workflow starts with an algorithm design in MATLAB, which enjoys universal appeal among engineers and scientists because of its expressive power and ease of use. The algorithm may employ deep learning networks augmented with traditional computer vision techniques and can be tested and verified within MATLAB.
Next, the networks are trained using MATLAB’s GPU and parallel computing support either on the desktop, a local compute cluster or in the cloud. In the deployment phase, code generation tools are employed to automatically generate optimized code that can target both embedded GPUs like Jetson, Jetson Drive AGX Xavier, Intel-based CPU platforms or ARM-based embedded platforms. The generated code leverages target-specific libraries that are highly optimized for the target architecture and memory model.
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-gormish
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Michael Gormish, Research Manager at Clarifai, presents the "Machine Learning- based Image Compression: Ready for Prime Time?" tutorial at the May 2019 Embedded Vision Summit.
Computer vision is undergoing dramatic changes because deep learning techniques are now able to solve complex non-linear problems. Computer vision pipelines used to consist of hand engineered stages mathematically optimized for some carefully chosen objective function. These pipelines are being replaced with machine- learned stages or end-to-end learning techniques where enough ground truth data is available.
Similarly, for decades image compression has relied on hand crafted algorithm pipelines, but recent efforts using deep learning are reporting higher image quality than that provided by conventional techniques. Is it time to replaced discrete cosine transforms with machine-learned compression techniques?
This talk examines practical aspects of deep learned image compression systems as compared with traditional approaches. Gormish considers memory, computation and other aspects, in addition to rate-distortion, to see when ML-based compression should be considered or avoided. He also discusses approaches using a combination of machine learned and traditional techniques.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/09/introduction-to-dnn-model-compression-techniques-a-presentation-from-xailient/
Sabina Pokhrel, Customer Success AI Engineer at Xailient, presents the “Introduction to DNN Model Compression Techniques” tutorial at the May 2021 Embedded Vision Summit.
Embedding real-time large-scale deep learning vision applications at the edge is challenging due to their huge computational, memory, and bandwidth requirements. System architects can mitigate these demands by modifying deep-neural networks to make them more energy efficient and less demanding of processing resources by applying various model compression approaches.
In this talk, Pokhrel provides an introduction to four established techniques for model compression. She discusses network pruning, quantization, knowledge distillation and low-rank factorization compression approaches.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/08/high-fidelity-conversion-of-floating-point-networks-for-low-precision-inference-using-distillation-with-limited-data-a-presentation-from-imagination-technologies/
James Imber, Senior Research Engineer at Imagination Technologies, presents the “High-fidelity Conversion of Floating-point Networks for Low-precision Inference using Distillation with Limited Data” tutorial at the May 2021 Embedded Vision Summit.
When converting floating-point networks to low-precision equivalents for high-performance inference, the primary objective is to maximally compress the network whilst maintaining fidelity to the original, floating-point network. This is made particularly challenging when only a reduced or unlabelled dataset is available. Data may be limited for reasons of a commercial or legal nature: for example, companies may be unwilling to share valuable data and labels that represent a substantial investment of resources; or the collector of the original dataset may not be permitted to share it for data privacy reasons.
Imber presents a method based on distillation that allows high-fidelity, low-precision networks to be produced for a wide range of different network types, using the original trained network in place of a labeled dataset. The proposed approach is directly applicable across multiple domains (e.g. classification, segmentation and style transfer) and can be adapted to numerous network compression techniques.
Cloud Server (VPS)
Net4’s offers high quality VPS hosting services in India. Our virtual private servers operate like a dynamic cloud server and are ideal for businesses who want near infinite scalability, Opex but not Capex, flexibility to upgrade and downgrade on the fly and yet Complete Control. You can choose the resources you want and build your own server in minutes. We offer Cloud Server on windows 2003 & 2008, Red Hat Linux and Cent OS ans also all editions of MSSQL and MySQL Databases. We also have a range of managed database and managed application services. For hosting companies we can also configure and provide licenses for Parallels Plesk or Cpanel.
How Do I Understand Deep Learning Performance?NVIDIA
Introduced at GTC 2018, PLASTER outlines critical problems with machine learning. Learn how to address and tackle these problems to better deliver AI-based services.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2pjvrpW.
Joe Duffy talks about the concurrency's explosion onto the mainstream over the past 15 years. He looks at some of today's hottest trends (Cloud, IoT, Microservices) and attempts to predict what lies ahead not only for concurrent programming, but also distributed, from now to 15 years into the future. Filmed at qconlondon.com.
Joe Duffy is Director of Engineering for the Compiler and Language Group at Microsoft. He leads the teams building C++, C#, VB, and F# languages, compilers, and static analysis platforms, across many architectures and platforms.
Engineering Simulation Meets the CloudBurak Yenier
Dennis Nagy talks about the impact of Cloud computing in the evolution of the engineering simulations market. He will share his insight on how and why Cloud computing will change how engineering simulations are done.
There is a profound architecture transition happening in software in 2011, like we see every 15 years: html5 browsers and powerful mobile platforms (android, iphone) bring new capabilities on the client side of apps, and the switch from vertical to horizontal scalability gave birth to powerful cloud platforms that allow fast development of scalable backends.
This talk will focus on the server side, explaining the opportunities and challenges that the Cloud represents for developers, in 4 areas: Delivery/Monetization/Marketing, Infrastructure, Platform and Development.
I will give an overview of several product and services in these areas: Amazon (AWS, Beanstalk), Google (App Engine), Joyent (Node.js), Salesforce (Heroku), VMWare (Cloud Foundry), GitHub, Cloudbees, Exo, Cloud9, Eclipse Orion.
The Cloud is an opportunity for developers to embrace agility and change, reinvent themselves, make money and have fun. It's time to start building your dreams on it!
A talk on reducing costs & increasing efficiencies by designing, testing & engineering in simulation first, plus examples of robotics & environmental capability.
State-of-the-art Image Processing across all domainsKnoldus Inc.
Ever thought of going beyond TensorFlow, GPU or TPU to solve your image classification problems?
From the standpoint of deep learning, the problem of image processing can be solved in a much better way with Transfer Learning. It is a computer vision method that helps develop accurate models while saving a lot of time. This presentation will help you find out why it is so beneficial?
Agenda:
The history of image processing
What is Transfer Learning?
Introduction to Convolutional Neural Networks (CNNs)
Different types of CNN architectures like AlexNet, VGG, Inception, and ResNet
Performance of various CNN architectures
Solving a medical image diagnosis problem with the above-discussed architectures
A Distributed Deep Learning Approach for the Mitosis Detection from Big Medic...Databricks
The strongest indicator of a cancer patient's prognosis is the number of mitotic bodies that a pathologist manually counts from the high-resolution whole-slide histopathology images. Obviously, it is not efficient to manually count the mitosis number. But it is still challenging to automate the process of mitosis detection due to the limited training datasets and the intensive computing involved in the model training and inference. This presentation introduces a large-scale deep learning approach to train a two-stage CNN-based model with high accuracy to detect the mitosis locations directly from the high-resolution whole-slide images. In details, we first train a nuclei detection model to remove the background information from the raw whole-slide histopathology images. Second, a customized ResNet-50 model is trained on the cleaned dataset in the first step. The first step saves the training time while improving the model performance in the second step. A false-positive oversampling approach is used to further improve the model performance. With these models, the inference process is conducted to detect the mitosis locations from the large volume of histopathology images in parallel. Meanwhile, the whole pipeline, including data preprocessing, model training, hyperparameter tuning, and inference, is parallelized by utilizing the distributed TensorFlow, Apache Spark, and HDFS. The experiences and techniques in this project can be applied to other large scale deep learning problems as well.
Speaker: Fei Hu
Yangqing Jia at AI Frontiers: Towards Better DL FrameworksAI Frontiers
The last few years has seen an abundance of deep learning and general machine learning frameworks, and such frameworks have created deep impacts to the machine learning industry. In this talk, Yangqing shares and discusses lessons we learned from building deep learning and general machine learning framework designs in the last few years, and share thoughts and philosophy in building the next generation of machine learning solutions for the AI industry. When applicable he draws examples from Caffe, a widely adopted deep learning framework that has evolved to serve computer vision, speech recognition, natural language understanding.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/mathworks/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-hiremath-chou
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Sandeep Hiremath, Product Manager, and Bill Chou, Senior Computer Vision Scientist, both of MathWorks, present the "Deploying Deep Learning Models on Embedded Processors for Autonomous Systems with MATLAB" tutorial at the May 2019 Embedded Vision Summit.
In this presentation, Hiremath and Chou explain how to bring the power of deep neural networks to memory- and power-constrained devices like those used in robotics and automated driving. The workflow starts with an algorithm design in MATLAB, which enjoys universal appeal among engineers and scientists because of its expressive power and ease of use. The algorithm may employ deep learning networks augmented with traditional computer vision techniques and can be tested and verified within MATLAB.
Next, the networks are trained using MATLAB’s GPU and parallel computing support either on the desktop, a local compute cluster or in the cloud. In the deployment phase, code generation tools are employed to automatically generate optimized code that can target both embedded GPUs like Jetson, Jetson Drive AGX Xavier, Intel-based CPU platforms or ARM-based embedded platforms. The generated code leverages target-specific libraries that are highly optimized for the target architecture and memory model.
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-gormish
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Michael Gormish, Research Manager at Clarifai, presents the "Machine Learning- based Image Compression: Ready for Prime Time?" tutorial at the May 2019 Embedded Vision Summit.
Computer vision is undergoing dramatic changes because deep learning techniques are now able to solve complex non-linear problems. Computer vision pipelines used to consist of hand engineered stages mathematically optimized for some carefully chosen objective function. These pipelines are being replaced with machine- learned stages or end-to-end learning techniques where enough ground truth data is available.
Similarly, for decades image compression has relied on hand crafted algorithm pipelines, but recent efforts using deep learning are reporting higher image quality than that provided by conventional techniques. Is it time to replaced discrete cosine transforms with machine-learned compression techniques?
This talk examines practical aspects of deep learned image compression systems as compared with traditional approaches. Gormish considers memory, computation and other aspects, in addition to rate-distortion, to see when ML-based compression should be considered or avoided. He also discusses approaches using a combination of machine learned and traditional techniques.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/09/introduction-to-dnn-model-compression-techniques-a-presentation-from-xailient/
Sabina Pokhrel, Customer Success AI Engineer at Xailient, presents the “Introduction to DNN Model Compression Techniques” tutorial at the May 2021 Embedded Vision Summit.
Embedding real-time large-scale deep learning vision applications at the edge is challenging due to their huge computational, memory, and bandwidth requirements. System architects can mitigate these demands by modifying deep-neural networks to make them more energy efficient and less demanding of processing resources by applying various model compression approaches.
In this talk, Pokhrel provides an introduction to four established techniques for model compression. She discusses network pruning, quantization, knowledge distillation and low-rank factorization compression approaches.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/08/high-fidelity-conversion-of-floating-point-networks-for-low-precision-inference-using-distillation-with-limited-data-a-presentation-from-imagination-technologies/
James Imber, Senior Research Engineer at Imagination Technologies, presents the “High-fidelity Conversion of Floating-point Networks for Low-precision Inference using Distillation with Limited Data” tutorial at the May 2021 Embedded Vision Summit.
When converting floating-point networks to low-precision equivalents for high-performance inference, the primary objective is to maximally compress the network whilst maintaining fidelity to the original, floating-point network. This is made particularly challenging when only a reduced or unlabelled dataset is available. Data may be limited for reasons of a commercial or legal nature: for example, companies may be unwilling to share valuable data and labels that represent a substantial investment of resources; or the collector of the original dataset may not be permitted to share it for data privacy reasons.
Imber presents a method based on distillation that allows high-fidelity, low-precision networks to be produced for a wide range of different network types, using the original trained network in place of a labeled dataset. The proposed approach is directly applicable across multiple domains (e.g. classification, segmentation and style transfer) and can be adapted to numerous network compression techniques.
Cloud Server (VPS)
Net4’s offers high quality VPS hosting services in India. Our virtual private servers operate like a dynamic cloud server and are ideal for businesses who want near infinite scalability, Opex but not Capex, flexibility to upgrade and downgrade on the fly and yet Complete Control. You can choose the resources you want and build your own server in minutes. We offer Cloud Server on windows 2003 & 2008, Red Hat Linux and Cent OS ans also all editions of MSSQL and MySQL Databases. We also have a range of managed database and managed application services. For hosting companies we can also configure and provide licenses for Parallels Plesk or Cpanel.
How Do I Understand Deep Learning Performance?NVIDIA
Introduced at GTC 2018, PLASTER outlines critical problems with machine learning. Learn how to address and tackle these problems to better deliver AI-based services.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2pjvrpW.
Joe Duffy talks about the concurrency's explosion onto the mainstream over the past 15 years. He looks at some of today's hottest trends (Cloud, IoT, Microservices) and attempts to predict what lies ahead not only for concurrent programming, but also distributed, from now to 15 years into the future. Filmed at qconlondon.com.
Joe Duffy is Director of Engineering for the Compiler and Language Group at Microsoft. He leads the teams building C++, C#, VB, and F# languages, compilers, and static analysis platforms, across many architectures and platforms.
Engineering Simulation Meets the CloudBurak Yenier
Dennis Nagy talks about the impact of Cloud computing in the evolution of the engineering simulations market. He will share his insight on how and why Cloud computing will change how engineering simulations are done.
There is a profound architecture transition happening in software in 2011, like we see every 15 years: html5 browsers and powerful mobile platforms (android, iphone) bring new capabilities on the client side of apps, and the switch from vertical to horizontal scalability gave birth to powerful cloud platforms that allow fast development of scalable backends.
This talk will focus on the server side, explaining the opportunities and challenges that the Cloud represents for developers, in 4 areas: Delivery/Monetization/Marketing, Infrastructure, Platform and Development.
I will give an overview of several product and services in these areas: Amazon (AWS, Beanstalk), Google (App Engine), Joyent (Node.js), Salesforce (Heroku), VMWare (Cloud Foundry), GitHub, Cloudbees, Exo, Cloud9, Eclipse Orion.
The Cloud is an opportunity for developers to embrace agility and change, reinvent themselves, make money and have fun. It's time to start building your dreams on it!
A talk on reducing costs & increasing efficiencies by designing, testing & engineering in simulation first, plus examples of robotics & environmental capability.
Bridging the Gap: Analyzing Data in and Below the CloudInside Analysis
The Briefing Room with Dean Abbott and Tableau Software
Live Webcast July 23, 2013
http://www.insideanalysis.com
Today’s desire for analytics extends well beyond the traditional domain of Business Intelligence. That’s partly because business users are realizing the value of mixing and matching all kinds of data, from all kinds of sources. One emerging market driver is Cloud-based data, and the desire companies have to analyze this data cohesively with their on-premise data sets.
Register for this episode of The Briefing Room to learn from Analyst Dean Abbott, who will explain how the ability to access data in the cloud can play a critical role for generating business value from analytics. He’ll be briefed by Ellie Fields of Tableau Software who will tout Tableau’s latest release, which includes native connectors to cloud-based applications like Salesforce.com, Amazon Redshift, Google Analytics and BigQuery. She’ll also demonstrate how Tableau can combine cloud data with other data sources, including spreadsheets, databases, cubes and even Big Data.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/feb-2017-member-meeting-rowen
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Chris Rowen of Cognite Ventures delivers the presentation "The Vision AI Start-ups That Matter Most" at the February 2017 Embedded Vision Alliance Member Meeting. Rowen shares his unique perspective on the vision AI start-ups that matter most.
Gigaom's Structure 2014 conference, June 21-22 in San Francisco Launchpad company profiles
#gigaomlive
More at http://events.gigaom.com/structure-2014/
A new wave of Artificial intelligence has emerged which has revolutionized the industry/academia.. Much like the web took advantage of existing technologies, this new wave builds on trends such as the decline in the cost of computing hardware, the emergence of the cloud, the fundamental consumerization of the enterprise and, of course, the mobile revolution.
Deep Learning has achieved remarkable breakthroughs, which have, in turn, driven performance improvements across AI components.
Maximize Big Data ROI via Best of Breed Patterns and PracticesJeff Bertman
******** Abstract: ********
Not long ago the question was whether your organization had big data. Did you have
the volume, the velocity, the technology. Now those basics are largely given for most of
the people attending this event. The path to success is still fuzzy, however, with so many
technologies to choose from – and so many ways to use them.
This presentation triangulates in a holistic manner on the modern business dilemma:
how can we leverage technology to improve revenue, profit, market share, and numerous
other success criteria. That said, this is not about the analytics or KPIs -- although it is
about measurable improvement. It’s about lining up the right technologies and using them
in effective, proven ways to maximize Return on Investment (ROI). Since the slant here
is holistic, we’ll show how to blend infrastructure, tools, methods, and talent to avoid and
constantly trim technical debt… and to produce success stories that are consistently
repeatable, not a byproduct of individual heroics.
Palestra apresentada por Pedro Mário Cruz e Silva, Solution Architect da NVIDIA, como parte da programação da VIII Semana de Inverno de Geofísica, em 19/07/2017.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
DeepScale: Real-Time Perception for Automated Driving
1. R E A L -‐ T I M E
P E R C E P T I O N
F O R
A U T O M AT E D
D R I V I N G
deepscale.ai
Forrest
Iandola
Co-‐founder
and
CEO,
DeepScale
2. TEAM
THE
DEEPSCALE
Forrest
Iandola
CEO
PhD
in
CS.
Published
20+
papers
that
focus
on
accelerating
and
improving
deep
learning
for
computer
vision.
Kurt
Keutzer
CHIEF
STRATEGY
OFFICER
UC
Berkeley
EECS
Professor.
Former
CTO
of
Synopsys.
Advisor
to
20+
startups.
Lisa
Brughera
DIR
OF
FINANCE
MS
in
Global
Policy.
Project
Manager
for
non-‐profit
housing
sector;
managed
multi-‐million
$,
multi-‐asset
class
budgets.
Anting
Shen
HEAD
OF
PRODUCT
ENGINEERING
MS
in
CS.
Developed
ML
applications
at
Yelp.
Researched
computer
vision
and
launched
ML
startup
at
UC
Berkeley.
Sammy
Sidhu
HEAD
OF
ADVANCED
ENGINEERING
BS
in
EECS.
Built
low-‐latency
ML
at
Apple and
high-‐frequency
trading
systems
at
Two
Sigma
Investments.
Ben
Landen
HEAD
OF
BIZ
DEV
MBA,
BS
in
EE.
Managed
$100M
P&L
of
ADAS/Infotainment
semiconductors
at
Maxim
Integrated.
Paden
Tomasello
ENGINEER
BS
in
EECS.
Developed
high-‐
performance
software
at
Graphistry
and
Cloudera.
Nobie Redmon
ENGINEER
MS
in
Physics.
Implemented
scaled
anti-‐abuse
workflows
at
Google.
Daisyca Woe
EXEC
ASSISTANT
BS
in
Biology.
Managed
multiple
offices
and
studios
in
health
&
wellness
industry.
Matt
Moskewicz
PRINCIPAL
ENGINEER
PhD
in
EECS.
Author
of
SAT
Chaff
algorithm
(3K+
citations);
Co-‐founder
of
CommandCAD (sold
to
Cadence).
Romi Phadte
ENGINEER
BS
in
EECS.
Launched
mobile
consumer
products
reaching
100M+
users
at
Pinterest.
Paras
Jain
ENGINEER
BS
in
CS.
Shipped
ads
product
managing
$100M+
at
Twitter;
accelerated
low-‐
latency
trading
at
Two
Sigma
Investments.
Judy
Thrasher
MANAGER
OF
HR
OPERATIONS
BS
in
Business
Administration. Director
of
HR
and
Head
of
Global
Staffing
at
A10
Networks from
pre-‐IPO
to
post-‐IPO.
Ed
O'Donnell
HEAD
OF
PRODUCT
MANAGEMENT
MBA,
Yale
BA.
Product
Management
at
MapD (GPU
analytics),
Telenav (GPS
nav),
DoubleClick, other
early
stage
startups.
Angie
Nucci Mullen
PR/MARKETING
MANAGER
B.A.,
Public
Relations.
Led
Honda
advanced
product
PR
initiatives.
Established
company
as
leader
in
safety,
electrified,
autonomous,
and
connected
vehicle
technology.
3. Overview
• The
rise
of
the
software-‐defined
car
• How
to
build
a
good
perception
system
for
automated
driving
• DeepScale's
approach
to
building
redundant
and
efficient
perception
systems
4. THE
SOFTWARE-‐DEFINED
CAR
Ubiquitous
sensors
in
cars
Fast
in-‐vehicle
data
network
Central
compute
in
cars
Over-‐The-‐Air
(OTA)
update
adoption
Market
adoption
of
driver-‐assistance
&
automated
driving
1
mbit/s
in-‐vehicle
network
1986 2006
>500
mbit/s
in-‐
vehicle
network
2014 2015 2016 2017 2018 2019 2022
>1
Gbit/s
in-‐
vehicle
network
Tesla
Auto-‐Pilot
OTA
offered,
>75%
adoption
Mass
Production
German
vehicles
w/
centralized
compute
GM
SuperCruise
OTA
offered
Subaru
EyeSight
>80%
Japan
adoption
>10
Gbit/s
in-‐
vehicle
network
Backup
cameras
required
in
all
new
cars
Auto
Emergency
Braking
required
in
all
new
cars
CONFIDENTIAL
5. LEVELS
OF
AUTOMATED
DRIVING
LEVEL
1 DRIVER ASSISTANCE
LEVEL
2 PARTIAL
AUTOMATION
LEVEL
3 CONDITIONAL
AUTOMATION
LEVEL
4 HIGH
AUTOMATION
LEVEL
5 FULL
AUTOMATION
PASSENGER
CARS
ROBOTAXIS
DeepScale
develops
technology
for
every
level
6. THE
FLOW
All
levels
of
vehicle
automation
require
this
flow
to
work.
DeepScale
specializes
in
Real-‐Time
Perception.
SENSORS
LIDAR
ULTRASONICCAMERA
RADAR
OFFLINE
MAPS
REAL-‐TIME
PERCEPTION
PATH
PLANNING
&
ACTUATION
7. WHAT
ARE
THE
DESIGN
PRINCIPLES
FOR
AN
IDEAL
PERCEPTION
SYSTEM?
12. TRADITIONAL
COMPUTER
VISION
• Dedicated
processor
bundled
with
specific
camera
in
a
closed
module
• Pre-‐dates
Deep
Neural
Networks
à narrow
capability
based
on
hard-‐coded
algorithms
(e.g.
only
detect
cars
from
certain
angles)
• Major
revisions
dictated
by
hardware
development
cycles
of
2-‐3
years
(an
eternity
given
how
fast
AI
is
changing)
Approach
#1
13. DEEP
LEARNING
IS
THE
TECHNOLOGY
THAT
WILL
BRING
BREAKTHROUGHS
IN
PERCEPTION
IMAGENET
TOP-‐5
ERROR Similar
accuracy
improvements
on
tasks
such
as:
-‐ semantic
segmentation
-‐ object
detection
-‐ 3D
reconstruction
-‐ …the
list
goes
on
14. OPEN-‐SOURCE
DEEP
NEURAL
NETWORKS
[1]
S
Ren,
K
He,
R
Girshick,
J
Sun.
Faster
R-‐CNN.
NIPS,
2015.
[2]
J
Redmon,
A
Farhadi.
YOLO9000.
CVPR,
2016.
[3]
W
Liu,
et
al.
SSD:
Single
shot
multibox detector.
ECCV,
2016
• Modern
Deep
Neural
Networks
(DNNs)
have
brought
order-‐of-‐magnitude
improvements
in
perception
accuracy
…but,
real-‐time
DNNs
for
object
detection
require
250W+
of
GPU
computing
[1,2,3]
• This
leads
to
a
trunk
full
of
hot,
expensive,
power-‐hungry
servers
Approach
#2
15. 50-‐500x
smaller
DNN
models
for
image
classification
30x
speedup
for
object
detection
DNNs
Implementing
DNNs
on
embedded
processors
DeepScale's playbook
for
creating
small
and
efficient
DNN
models
DeepScale's
Unique
Advantage:
Small,
efficient
DNNs
on
low-‐cost,
automotive-‐grade
processors
16. 16
DeepScale
Captures
Best
Attributes
of
Camera
Systems
TRADITIONAL
COMPUTER
VISION
OPEN-‐SOURCE
RESEARCH
MAIN
CAPABILITIES
Object Detection
using
conventional
methods
Object
Detection
using
Deep
Neural
Networks
Object
Detection
using
Deep
Neural
Networks
ERROR
RATE
TREND
Improves
with
hardware
revisions
every
3
years
Improves
all
the
time Improves
all
the
time
COMPUTE
HARDWARE
Custom
ASICs
or
FGPAs
($$)
One
high-‐end
GPU per
camera
($$$)
One
NVIDIA
automotive
GPU
per
multi-‐camera
set
($)
POWER <10W 250W+ <10W
per camera
PORTABILITY Tied to
supplier's
camera
and
ASIC
bundle
Varies Portable across
cameras
and
processors
AUTOMOTIVE
CERTIFICATON
Yes No In
progress
17. Summary
• The
rise
of
the
software-‐defined
car
• Good
perception
systems
for
automated
driving
are
RARE
• Robust
• Accurate
• Redundant
• Efficient
• DeepScale
is
building
RARE
perception
systems
• As
the
creators
of
SqueezeNet,
it's
no
surprise
that
DeepScale
excels
in
Efficiency
18. Where
to
catch
us
next
• May:
AutoSens
Detroit
• June:
CVPR
Efficient
Deep
Learning
Workshop
(organizers)
• ShiftNet:
arxiv.org/pdf/1711.08141.pdf
• SqueezeNext:
arxiv.org/abs/1803.10615
Our
latest
papers
on
small
neural
nets
@DeepScale_
