Submit Search
Upload
Threading Successes 01 Intro
•
Download as PPT, PDF
•
0 likes
•
343 views
G
guest40fc7cd
Follow
Education
Technology
Report
Share
Report
Share
1 of 9
Download now
Recommended
As a Machine Learning engineer, one of the most important tasks is to select the right hardware to run your ML workflow on. Is it an Intel CPU or an NVIDIA GPU? Expect the unexpected from the results of the benchmark performed on the Recommender System created for one of Datatonic's customers, a top-5 UK retailer, on Google Cloud.
Accelerate Machine Learning on Google Cloud
Accelerate Machine Learning on Google Cloud
Samantha Guerriero
Join us to see how Public-sector organizations and AWS Partners are combining Smart Devices and Artificial Intelligence to create flexible, secure and cost-effective solutions. Applying machine learning models to live video/audio, cameras can be transformed into flexible IoT devices that perform critical functions around public safety, security, property management, smart parking & environmental management. Learn how these solutions are architected using AWS services such as AWS IoT Core, AWS GreenGrass, AWS DeepLens, Amazon SageMaker and Amazon Alexa.
Machine Learning at the Edge
Machine Learning at the Edge
Amazon Web Services
Rob Hirschfeld, Co-Founder / CEO of RackN presents a talk on Composable Infrastructure at Interop ITX 2018.
Composable Infrastructure Talk at Interop ITX 2018
Composable Infrastructure Talk at Interop ITX 2018
RackN
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.
Yangqing Jia at AI Frontiers: Towards Better DL Frameworks
Yangqing Jia at AI Frontiers: Towards Better DL Frameworks
AI Frontiers
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-2017-embedded-vision-summit-rabanovich For more information about embedded vision, please visit: http://www.embedded-vision.com Andrew Rabinovich, Director of Deep Learning at Magic Leap, presents the "Performing Multiple Perceptual Tasks With a Single Deep Neural Network" tutorial at the May 2017 Embedded Vision Summit. As more system developers consider incorporating visual perception into smart devices such as self-driving cars, drones and wearable computers, attention is shifting toward practical formulation and implementation of these algorithms. Here, the key challenge is how to deploy very computationally demanding algorithms that achieve state-of-the-art results on platforms with limited computational capability and small power budgets. Visual perception tasks such as face recognition, place recognition and tracking are traditionally solved using multiple single-purpose algorithms. With the approach, power consumption increases as more tasks are performed. In this talk, Rabinovich introduces techniques for performing multiple visual perception tasks within a single learning-based algorithm. He also explores general-purpose model optimization to enable such algorithms to run efficiently on embedded platforms.
"Performing Multiple Perceptual Tasks With a Single Deep Neural Network," a P...
"Performing Multiple Perceptual Tasks With a Single Deep Neural Network," a P...
Edge AI and Vision Alliance
This presentation will demonstrate our recent progress in developing advanced computer vision algorithms using embedded platforms for video-based face recognition, vehicle attribute analysis, urban management event detection, and high-density crowd counting. These algorithms combine the traditional CV approach with recent advances in deep learning to make high-performance computer vision systems practical and enable products in several vertical markets including intelligent transportation systems (ITS), business intelligence (BI), and smart video surveillance. We will demonstrate algorithm design and optimization scheme for several recently available processors from Movidius, Nvidia, and ARM.
Hai Tao at AI Frontiers: Deep Learning For Embedded Vision System
Hai Tao at AI Frontiers: Deep Learning For Embedded Vision System
AI Frontiers
The edge is the domain of the Internet of Things, of personal medical devices, of cars that understand the world, of machines that self-regulate and more. These devices share a common constraint: they can't send full data to the cloud for processing. This talk will review the changing needs for AI at the edge, the demands of learning networks on small cores and changing hardware being provided to meet these demands.
Kevin Shaw at AI Frontiers: AI on the Edge: Bringing Intelligence to Small De...
Kevin Shaw at AI Frontiers: AI on the Edge: Bringing Intelligence to Small De...
AI Frontiers
For the full video of this presentation, please visit: https://www.embedded-vision.com/platinum-members/8tree/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit For more information about embedded vision, please visit: http://www.embedded-vision.com Arun Chhabra, CEO of 8tree, presents the "Designing Vision Systems for Human Operators and Workflows: A Case Study" tutorial at the May 2017 Embedded Vision Summit. During the past several decades, manual measurement methods – using rulers and dial gauges – have been the status quo for inspecting dents, bumps, lightning strikes and corrosion blend-out on an aircraft. These traditional methods have been time-consuming and prone to subjectivity and human error. Emerging optical inspection technologies are changing things for the better. User-centric, vision-based tools empower operators to achieve greater efficiency, accuracy and consistency, slashing inspection and reporting times by 90%, without requiring extensive training or workflow changes. Chhabra's presentation discusses how such emerging tools, designed to address real-world problems, can improve aircraft turn-around-time (TaT), while enhancing safety through an improved understanding of airframe reliability.
"Designing Vision Systems for Human Operators and Workflows: A Case Study," a...
"Designing Vision Systems for Human Operators and Workflows: A Case Study," a...
Edge AI and Vision Alliance
Recommended
As a Machine Learning engineer, one of the most important tasks is to select the right hardware to run your ML workflow on. Is it an Intel CPU or an NVIDIA GPU? Expect the unexpected from the results of the benchmark performed on the Recommender System created for one of Datatonic's customers, a top-5 UK retailer, on Google Cloud.
Accelerate Machine Learning on Google Cloud
Accelerate Machine Learning on Google Cloud
Samantha Guerriero
Join us to see how Public-sector organizations and AWS Partners are combining Smart Devices and Artificial Intelligence to create flexible, secure and cost-effective solutions. Applying machine learning models to live video/audio, cameras can be transformed into flexible IoT devices that perform critical functions around public safety, security, property management, smart parking & environmental management. Learn how these solutions are architected using AWS services such as AWS IoT Core, AWS GreenGrass, AWS DeepLens, Amazon SageMaker and Amazon Alexa.
Machine Learning at the Edge
Machine Learning at the Edge
Amazon Web Services
Rob Hirschfeld, Co-Founder / CEO of RackN presents a talk on Composable Infrastructure at Interop ITX 2018.
Composable Infrastructure Talk at Interop ITX 2018
Composable Infrastructure Talk at Interop ITX 2018
RackN
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.
Yangqing Jia at AI Frontiers: Towards Better DL Frameworks
Yangqing Jia at AI Frontiers: Towards Better DL Frameworks
AI Frontiers
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-2017-embedded-vision-summit-rabanovich For more information about embedded vision, please visit: http://www.embedded-vision.com Andrew Rabinovich, Director of Deep Learning at Magic Leap, presents the "Performing Multiple Perceptual Tasks With a Single Deep Neural Network" tutorial at the May 2017 Embedded Vision Summit. As more system developers consider incorporating visual perception into smart devices such as self-driving cars, drones and wearable computers, attention is shifting toward practical formulation and implementation of these algorithms. Here, the key challenge is how to deploy very computationally demanding algorithms that achieve state-of-the-art results on platforms with limited computational capability and small power budgets. Visual perception tasks such as face recognition, place recognition and tracking are traditionally solved using multiple single-purpose algorithms. With the approach, power consumption increases as more tasks are performed. In this talk, Rabinovich introduces techniques for performing multiple visual perception tasks within a single learning-based algorithm. He also explores general-purpose model optimization to enable such algorithms to run efficiently on embedded platforms.
"Performing Multiple Perceptual Tasks With a Single Deep Neural Network," a P...
"Performing Multiple Perceptual Tasks With a Single Deep Neural Network," a P...
Edge AI and Vision Alliance
This presentation will demonstrate our recent progress in developing advanced computer vision algorithms using embedded platforms for video-based face recognition, vehicle attribute analysis, urban management event detection, and high-density crowd counting. These algorithms combine the traditional CV approach with recent advances in deep learning to make high-performance computer vision systems practical and enable products in several vertical markets including intelligent transportation systems (ITS), business intelligence (BI), and smart video surveillance. We will demonstrate algorithm design and optimization scheme for several recently available processors from Movidius, Nvidia, and ARM.
Hai Tao at AI Frontiers: Deep Learning For Embedded Vision System
Hai Tao at AI Frontiers: Deep Learning For Embedded Vision System
AI Frontiers
The edge is the domain of the Internet of Things, of personal medical devices, of cars that understand the world, of machines that self-regulate and more. These devices share a common constraint: they can't send full data to the cloud for processing. This talk will review the changing needs for AI at the edge, the demands of learning networks on small cores and changing hardware being provided to meet these demands.
Kevin Shaw at AI Frontiers: AI on the Edge: Bringing Intelligence to Small De...
Kevin Shaw at AI Frontiers: AI on the Edge: Bringing Intelligence to Small De...
AI Frontiers
For the full video of this presentation, please visit: https://www.embedded-vision.com/platinum-members/8tree/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit For more information about embedded vision, please visit: http://www.embedded-vision.com Arun Chhabra, CEO of 8tree, presents the "Designing Vision Systems for Human Operators and Workflows: A Case Study" tutorial at the May 2017 Embedded Vision Summit. During the past several decades, manual measurement methods – using rulers and dial gauges – have been the status quo for inspecting dents, bumps, lightning strikes and corrosion blend-out on an aircraft. These traditional methods have been time-consuming and prone to subjectivity and human error. Emerging optical inspection technologies are changing things for the better. User-centric, vision-based tools empower operators to achieve greater efficiency, accuracy and consistency, slashing inspection and reporting times by 90%, without requiring extensive training or workflow changes. Chhabra's presentation discusses how such emerging tools, designed to address real-world problems, can improve aircraft turn-around-time (TaT), while enhancing safety through an improved understanding of airframe reliability.
"Designing Vision Systems for Human Operators and Workflows: A Case Study," a...
"Designing Vision Systems for Human Operators and Workflows: A Case Study," a...
Edge AI and Vision Alliance
At the Virtual HPC User Forum Special Event, CEO of MemVerge, introduces MemVerge and provides and overview of Big Memory Computing and Memory Machine software.
Big Memory for HPC
Big Memory for HPC
MemVerge
For the full video of this presentation, please visit: https://www.embedded-vision.com/platinum-members/luxoft/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit For more information about embedded vision, please visit: http://www.embedded-vision.com Alexey Rybakov, Senior Director for Embedded Systems at Luxoft, presents the "Deep Learning Beyond Cats and Cars: Developing a Real-life DNN-based Embedded Vision Product for Agriculture, Construction, Medical, or Retail" tutorial at the May 2017 Embedded Vision Summit. By now we know very well how to design and train a neural network to recognize cats, dogs and cars. But what about real projects — for example, in agriculture, construction, medical, and retail? This how-to talk provides an overview of what it takes to design, train, and fine-tune a real-life DNN-based embedded vision solution. Rybakov explores algorithmic, data set, training, and optimization decisions that take you from proofs-of-concepts to solid, reliable, and highly optimized systems. This material is based on Luxoft's own successes, failures, and lessons learned while implementing embedded vision solutions.
"Deep Learning Beyond Cats and Cars: Developing a Real-life DNN-based Embedde...
"Deep Learning Beyond Cats and Cars: Developing a Real-life DNN-based Embedde...
Edge AI and Vision Alliance
Arm Neoverse Roadmap, Cloud Native ecosystem
Arm Neoverse solutions @Graviton2-AWS Japan Webinar Oct2020
Arm Neoverse solutions @Graviton2-AWS Japan Webinar Oct2020
Toshinori Kujiraoka
Talk @ 5th Global Big Data Conference
Distributed deep learning optimizations
Distributed deep learning optimizations
geetachauhan
High performance computing
High performance computing
Guy Tel-Zur
For the full video of this presentation, please visit: https://www.embedded-vision.com/platinum-members/mathworks/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-nehemiah For more information about embedded vision, please visit: http://www.embedded-vision.com Avinash Nehemiah, Product Marketing Manage for, Computer Vision at MathWorks, presents the "How to Test and Validate an Automated Driving System" tutorial at the May 2017 Embedded Vision Summit. Have you ever wondered how ADAS and autonomous driving systems are tested? Automated driving systems combine a diverse set of technologies and engineering skill sets from embedded vision to control systems. This technological diversity and complexity makes it especially challenging to test these systems. This session describes the main challenges engineers face in testing and validating autonomous cars and driver assistance systems, and uses case studies to share best practices used in the automotive industry.
"How to Test and Validate an Automated Driving System," a Presentation from M...
"How to Test and Validate an Automated Driving System," a Presentation from M...
Edge AI and Vision Alliance
This is a flexible demo that will start by showing you all of the various technologies involved in using Puppet and Puppet Enterprise. It is meant to be interactive, so there should be plenty of time for additional demos to answer your specific questions about using Puppet and the related technologies.
Puppet Camp Berlin 2015: Nicholas Corrarello | Puppet Demo
Puppet Camp Berlin 2015: Nicholas Corrarello | Puppet Demo
NETWAYS
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.
"Deploying Deep Learning Models on Embedded Processors for Autonomous Systems...
"Deploying Deep Learning Models on Embedded Processors for Autonomous Systems...
Edge AI and Vision Alliance
Bofu Chen
Affordable AI Connects To A Better Life
Affordable AI Connects To A Better Life
NVIDIA Taiwan
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-2017-embedded-vision-summit-warden For more information about embedded vision, please visit: http://www.embedded-vision.com Pete Warden, Research Engineer at Google, presents the "Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power DSP" tutorial at the May 2017 Embedded Vision Summit. TensorFlow is Google’s second-generation deep learning software framework. TensorFlow was designed from the ground up to enable efficient implementation of deep learning algorithms at different scales, from high-performance data centers to low-power embedded and mobile devices. In this talk, Warden presents the technical details of how the TensorFlow and Qualcomm teams collaborated to target TensorFlow to Qualcomm’s low-power Hexagon DSP using Hexagon Vector Extensions, which enables deep learning models to run fast and efficiently. Warden explains how the two companies split up the work between them and how they measured progress with specific benchmarks, and he looks at some of the code optimizations they implemented. Since the majority of the resulting code has been open-sourced, he's able to dive deeply into the specifics of the implementation decisions they made.
"Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power ...
"Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power ...
Edge AI and Vision Alliance
Overview about edge computing concepts, how to get started and build a project using those concepts.
Edge computing in practice using IoT, Tensorflow and Google Cloud
Edge computing in practice using IoT, Tensorflow and Google Cloud
Alvaro Viebrantz
5 min talk @ try! Swift Tokyo 2017
Client-Side Deep Learning
Client-Side Deep Learning
Shuichi Tsutsumi
Learn how to optimize Tensorflow for your Intel CPU and techniques for distributed deep learning. Talk @ CSPA AI Unconference organized by Intel
Intel optimized tensorflow, distributed deep learning
Intel optimized tensorflow, distributed deep learning
geetachauhan
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-2017-embedded-vision-summit-maslan For more information about embedded vision, please visit: http://www.embedded-vision.com Carter Maslan, CEO of Camio, presents the "Blending Cloud and Edge Machine Learning to Deliver Real-time Video Monitoring" tutorial at the May 2017 Embedded Vision Summit. Network cameras and other edge devices are collecting ever-more video – far more than can be economically transported to the cloud. This argues for putting intelligence in edge devices. But the cloud offers unique, valuable capabilities, such as aggregating information from multiple cameras, applying state-of-the-art algorithms, and providing users with access to their data anywhere, any time. Camio uses a combination of machine learning at the edge (in network cameras and network video recorders) and in the cloud to generate alerts, highlight the most significant events captured by a camera, and to let users search for events of interest. In this talk, Maslan explores the trade-offs between edge and cloud processing for systems that extract meaning from video, and explains how the two approaches can be combined to create big opportunities.
"Blending Cloud and Edge Machine Learning to Deliver Real-time Video Monitori...
"Blending Cloud and Edge Machine Learning to Deliver Real-time Video Monitori...
Edge AI and Vision Alliance
Some resources how to navigate in the hardware space in order to build your own workstation for training deep learning models. Alternative download link: https://www.dropbox.com/s/o7cwla30xtf9r74/deepLearning_buildComputer.pdf?dl=0
Deep Learning Computer Build
Deep Learning Computer Build
PetteriTeikariPhD
For the full video of this presentation, please visit: https://www.embedded-vision.com/platinum-members/intel/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-park For more information about embedded vision, please visit: http://www.embedded-vision.com Minje Park, Software Engineering Manager at Intel, presents the "Designing Deep Neural Network Algorithms for Embedded Devices" tutorial at the May 2017 Embedded Vision Summit. Deep neural networks have shown state-of-the-art results in a variety of vision tasks. Although accurate, most of these deep neural networks are computationally intensive, creating challenges for embedded devices. In this talk, Park provides several ideas and insights on how to design deep neural network architectures small enough for embedded deployment. He also explores how to further reduce the processing load by adopting simple but effective compression and quantization techniques. He shows a set of practical applications, such as face recognition, facial attribute classification, and person detection, which can be run in near real-time without any heavy GPU or dedicated DSP and without losing accuracy.
"Designing Deep Neural Network Algorithms for Embedded Devices," a Presentati...
"Designing Deep Neural Network Algorithms for Embedded Devices," a Presentati...
Edge AI and Vision Alliance
Real-time DeepLearning on IoT Sensor Data
Real-time DeepLearning on IoT Sensor Data
Real-time DeepLearning on IoT Sensor Data
Romeo Kienzler
Invited talk. An introduction to high-performance computing given at the Society of Actuaries Life & Annuity Symposium in 2011.
High Performance Computing: an Introduction for the Society of Actuaries
High Performance Computing: an Introduction for the Society of Actuaries
Adam DeConinck
For the full video of this presentation, please visit: https://www.embedded-vision.com/platinum-members/cadence/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-desai For more information about embedded vision, please visit: http://www.embedded-vision.com Pulin Desai, Vision Product Marketing Director at Cadence, presents the "Highly Efficient, Scalable Vision and AI Processors IP for the Edge" tutorial at the May 2019 Embedded Vision Summit. This presentation describes the architecture of the latest Tensilica-based vision and AI processor family, and illustrates how easily vision algorithms (e.g., SLAM, 3D capture) and AI inference can be implemented on these processors. See how this low-power architecture simplifies development of a scalable vision and AI solution from low to high end for mobile, AR/VR, surveillance and automotive markets.
"Highly Efficient, Scalable Vision and AI Processors IP for the Edge," a Pres...
"Highly Efficient, Scalable Vision and AI Processors IP for the Edge," a Pres...
Edge AI and Vision Alliance
These are the slides from the talk I did on GPU Programming for CocoaConf Columbus 2014
Gpu Programming With GPUImage and Metal
Gpu Programming With GPUImage and Metal
Janie Clayton
Threading Successes 06 Allegorithmic
Threading Successes 06 Allegorithmic
guest40fc7cd
Threading Successes 04 Hellgate
Threading Successes 04 Hellgate
guest40fc7cd
More Related Content
What's hot
At the Virtual HPC User Forum Special Event, CEO of MemVerge, introduces MemVerge and provides and overview of Big Memory Computing and Memory Machine software.
Big Memory for HPC
Big Memory for HPC
MemVerge
For the full video of this presentation, please visit: https://www.embedded-vision.com/platinum-members/luxoft/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit For more information about embedded vision, please visit: http://www.embedded-vision.com Alexey Rybakov, Senior Director for Embedded Systems at Luxoft, presents the "Deep Learning Beyond Cats and Cars: Developing a Real-life DNN-based Embedded Vision Product for Agriculture, Construction, Medical, or Retail" tutorial at the May 2017 Embedded Vision Summit. By now we know very well how to design and train a neural network to recognize cats, dogs and cars. But what about real projects — for example, in agriculture, construction, medical, and retail? This how-to talk provides an overview of what it takes to design, train, and fine-tune a real-life DNN-based embedded vision solution. Rybakov explores algorithmic, data set, training, and optimization decisions that take you from proofs-of-concepts to solid, reliable, and highly optimized systems. This material is based on Luxoft's own successes, failures, and lessons learned while implementing embedded vision solutions.
"Deep Learning Beyond Cats and Cars: Developing a Real-life DNN-based Embedde...
"Deep Learning Beyond Cats and Cars: Developing a Real-life DNN-based Embedde...
Edge AI and Vision Alliance
Arm Neoverse Roadmap, Cloud Native ecosystem
Arm Neoverse solutions @Graviton2-AWS Japan Webinar Oct2020
Arm Neoverse solutions @Graviton2-AWS Japan Webinar Oct2020
Toshinori Kujiraoka
Talk @ 5th Global Big Data Conference
Distributed deep learning optimizations
Distributed deep learning optimizations
geetachauhan
High performance computing
High performance computing
Guy Tel-Zur
For the full video of this presentation, please visit: https://www.embedded-vision.com/platinum-members/mathworks/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-nehemiah For more information about embedded vision, please visit: http://www.embedded-vision.com Avinash Nehemiah, Product Marketing Manage for, Computer Vision at MathWorks, presents the "How to Test and Validate an Automated Driving System" tutorial at the May 2017 Embedded Vision Summit. Have you ever wondered how ADAS and autonomous driving systems are tested? Automated driving systems combine a diverse set of technologies and engineering skill sets from embedded vision to control systems. This technological diversity and complexity makes it especially challenging to test these systems. This session describes the main challenges engineers face in testing and validating autonomous cars and driver assistance systems, and uses case studies to share best practices used in the automotive industry.
"How to Test and Validate an Automated Driving System," a Presentation from M...
"How to Test and Validate an Automated Driving System," a Presentation from M...
Edge AI and Vision Alliance
This is a flexible demo that will start by showing you all of the various technologies involved in using Puppet and Puppet Enterprise. It is meant to be interactive, so there should be plenty of time for additional demos to answer your specific questions about using Puppet and the related technologies.
Puppet Camp Berlin 2015: Nicholas Corrarello | Puppet Demo
Puppet Camp Berlin 2015: Nicholas Corrarello | Puppet Demo
NETWAYS
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.
"Deploying Deep Learning Models on Embedded Processors for Autonomous Systems...
"Deploying Deep Learning Models on Embedded Processors for Autonomous Systems...
Edge AI and Vision Alliance
Bofu Chen
Affordable AI Connects To A Better Life
Affordable AI Connects To A Better Life
NVIDIA Taiwan
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-2017-embedded-vision-summit-warden For more information about embedded vision, please visit: http://www.embedded-vision.com Pete Warden, Research Engineer at Google, presents the "Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power DSP" tutorial at the May 2017 Embedded Vision Summit. TensorFlow is Google’s second-generation deep learning software framework. TensorFlow was designed from the ground up to enable efficient implementation of deep learning algorithms at different scales, from high-performance data centers to low-power embedded and mobile devices. In this talk, Warden presents the technical details of how the TensorFlow and Qualcomm teams collaborated to target TensorFlow to Qualcomm’s low-power Hexagon DSP using Hexagon Vector Extensions, which enables deep learning models to run fast and efficiently. Warden explains how the two companies split up the work between them and how they measured progress with specific benchmarks, and he looks at some of the code optimizations they implemented. Since the majority of the resulting code has been open-sourced, he's able to dive deeply into the specifics of the implementation decisions they made.
"Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power ...
"Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power ...
Edge AI and Vision Alliance
Overview about edge computing concepts, how to get started and build a project using those concepts.
Edge computing in practice using IoT, Tensorflow and Google Cloud
Edge computing in practice using IoT, Tensorflow and Google Cloud
Alvaro Viebrantz
5 min talk @ try! Swift Tokyo 2017
Client-Side Deep Learning
Client-Side Deep Learning
Shuichi Tsutsumi
Learn how to optimize Tensorflow for your Intel CPU and techniques for distributed deep learning. Talk @ CSPA AI Unconference organized by Intel
Intel optimized tensorflow, distributed deep learning
Intel optimized tensorflow, distributed deep learning
geetachauhan
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-2017-embedded-vision-summit-maslan For more information about embedded vision, please visit: http://www.embedded-vision.com Carter Maslan, CEO of Camio, presents the "Blending Cloud and Edge Machine Learning to Deliver Real-time Video Monitoring" tutorial at the May 2017 Embedded Vision Summit. Network cameras and other edge devices are collecting ever-more video – far more than can be economically transported to the cloud. This argues for putting intelligence in edge devices. But the cloud offers unique, valuable capabilities, such as aggregating information from multiple cameras, applying state-of-the-art algorithms, and providing users with access to their data anywhere, any time. Camio uses a combination of machine learning at the edge (in network cameras and network video recorders) and in the cloud to generate alerts, highlight the most significant events captured by a camera, and to let users search for events of interest. In this talk, Maslan explores the trade-offs between edge and cloud processing for systems that extract meaning from video, and explains how the two approaches can be combined to create big opportunities.
"Blending Cloud and Edge Machine Learning to Deliver Real-time Video Monitori...
"Blending Cloud and Edge Machine Learning to Deliver Real-time Video Monitori...
Edge AI and Vision Alliance
Some resources how to navigate in the hardware space in order to build your own workstation for training deep learning models. Alternative download link: https://www.dropbox.com/s/o7cwla30xtf9r74/deepLearning_buildComputer.pdf?dl=0
Deep Learning Computer Build
Deep Learning Computer Build
PetteriTeikariPhD
For the full video of this presentation, please visit: https://www.embedded-vision.com/platinum-members/intel/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-park For more information about embedded vision, please visit: http://www.embedded-vision.com Minje Park, Software Engineering Manager at Intel, presents the "Designing Deep Neural Network Algorithms for Embedded Devices" tutorial at the May 2017 Embedded Vision Summit. Deep neural networks have shown state-of-the-art results in a variety of vision tasks. Although accurate, most of these deep neural networks are computationally intensive, creating challenges for embedded devices. In this talk, Park provides several ideas and insights on how to design deep neural network architectures small enough for embedded deployment. He also explores how to further reduce the processing load by adopting simple but effective compression and quantization techniques. He shows a set of practical applications, such as face recognition, facial attribute classification, and person detection, which can be run in near real-time without any heavy GPU or dedicated DSP and without losing accuracy.
"Designing Deep Neural Network Algorithms for Embedded Devices," a Presentati...
"Designing Deep Neural Network Algorithms for Embedded Devices," a Presentati...
Edge AI and Vision Alliance
Real-time DeepLearning on IoT Sensor Data
Real-time DeepLearning on IoT Sensor Data
Real-time DeepLearning on IoT Sensor Data
Romeo Kienzler
Invited talk. An introduction to high-performance computing given at the Society of Actuaries Life & Annuity Symposium in 2011.
High Performance Computing: an Introduction for the Society of Actuaries
High Performance Computing: an Introduction for the Society of Actuaries
Adam DeConinck
For the full video of this presentation, please visit: https://www.embedded-vision.com/platinum-members/cadence/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-desai For more information about embedded vision, please visit: http://www.embedded-vision.com Pulin Desai, Vision Product Marketing Director at Cadence, presents the "Highly Efficient, Scalable Vision and AI Processors IP for the Edge" tutorial at the May 2019 Embedded Vision Summit. This presentation describes the architecture of the latest Tensilica-based vision and AI processor family, and illustrates how easily vision algorithms (e.g., SLAM, 3D capture) and AI inference can be implemented on these processors. See how this low-power architecture simplifies development of a scalable vision and AI solution from low to high end for mobile, AR/VR, surveillance and automotive markets.
"Highly Efficient, Scalable Vision and AI Processors IP for the Edge," a Pres...
"Highly Efficient, Scalable Vision and AI Processors IP for the Edge," a Pres...
Edge AI and Vision Alliance
These are the slides from the talk I did on GPU Programming for CocoaConf Columbus 2014
Gpu Programming With GPUImage and Metal
Gpu Programming With GPUImage and Metal
Janie Clayton
What's hot
(20)
Big Memory for HPC
Big Memory for HPC
"Deep Learning Beyond Cats and Cars: Developing a Real-life DNN-based Embedde...
"Deep Learning Beyond Cats and Cars: Developing a Real-life DNN-based Embedde...
Arm Neoverse solutions @Graviton2-AWS Japan Webinar Oct2020
Arm Neoverse solutions @Graviton2-AWS Japan Webinar Oct2020
Distributed deep learning optimizations
Distributed deep learning optimizations
High performance computing
High performance computing
"How to Test and Validate an Automated Driving System," a Presentation from M...
"How to Test and Validate an Automated Driving System," a Presentation from M...
Puppet Camp Berlin 2015: Nicholas Corrarello | Puppet Demo
Puppet Camp Berlin 2015: Nicholas Corrarello | Puppet Demo
"Deploying Deep Learning Models on Embedded Processors for Autonomous Systems...
"Deploying Deep Learning Models on Embedded Processors for Autonomous Systems...
Affordable AI Connects To A Better Life
Affordable AI Connects To A Better Life
"Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power ...
"Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power ...
Edge computing in practice using IoT, Tensorflow and Google Cloud
Edge computing in practice using IoT, Tensorflow and Google Cloud
Client-Side Deep Learning
Client-Side Deep Learning
Intel optimized tensorflow, distributed deep learning
Intel optimized tensorflow, distributed deep learning
"Blending Cloud and Edge Machine Learning to Deliver Real-time Video Monitori...
"Blending Cloud and Edge Machine Learning to Deliver Real-time Video Monitori...
Deep Learning Computer Build
Deep Learning Computer Build
"Designing Deep Neural Network Algorithms for Embedded Devices," a Presentati...
"Designing Deep Neural Network Algorithms for Embedded Devices," a Presentati...
Real-time DeepLearning on IoT Sensor Data
Real-time DeepLearning on IoT Sensor Data
High Performance Computing: an Introduction for the Society of Actuaries
High Performance Computing: an Introduction for the Society of Actuaries
"Highly Efficient, Scalable Vision and AI Processors IP for the Edge," a Pres...
"Highly Efficient, Scalable Vision and AI Processors IP for the Edge," a Pres...
Gpu Programming With GPUImage and Metal
Gpu Programming With GPUImage and Metal
Viewers also liked
Threading Successes 06 Allegorithmic
Threading Successes 06 Allegorithmic
guest40fc7cd
Threading Successes 04 Hellgate
Threading Successes 04 Hellgate
guest40fc7cd
Threading Successes 05 Smoke
Threading Successes 05 Smoke
guest40fc7cd
Threading Successes 03 Gamebryo
Threading Successes 03 Gamebryo
guest40fc7cd
Threading Successes 02 Supreme Commander
Threading Successes 02 Supreme Commander
guest40fc7cd
Introduction of Gamebryo LightSpeed
Gamebryo LightSpeed(English)
Gamebryo LightSpeed(English)
Gamebryo
Viewers also liked
(6)
Threading Successes 06 Allegorithmic
Threading Successes 06 Allegorithmic
Threading Successes 04 Hellgate
Threading Successes 04 Hellgate
Threading Successes 05 Smoke
Threading Successes 05 Smoke
Threading Successes 03 Gamebryo
Threading Successes 03 Gamebryo
Threading Successes 02 Supreme Commander
Threading Successes 02 Supreme Commander
Gamebryo LightSpeed(English)
Gamebryo LightSpeed(English)
Similar to Threading Successes 01 Intro
Multiple Cores, Multiple Pipes, Multiple Threads – Do we have more Parallelism than we can handle?
Multiple Cores, Multiple Pipes, Multiple Threads – Do we have more Parallelis...
Multiple Cores, Multiple Pipes, Multiple Threads – Do we have more Parallelis...
Slide_N
Unreal Engine* 4 is a high-performance game engine for game developers. Learn how Intel and Epic Games* worked together to improve engine performance both for CPUs and GPUs and how developers can take advantage of it.
Scalability for All: Unreal Engine* 4 with Intel
Scalability for All: Unreal Engine* 4 with Intel
Intel® Software
This talk will briefly discuss performance threading of Quake4 and Quake Wars Engine. It will go over the issues involved parallelizing serial code, working with different backends, load balancing and design considerations. It will also offer some insight into extracting parallelism in game engines on next-generation hardware.Get a first-time look at Havok Behavior 5.5, demonstrating how the Havok Behavior Tool combines the fidelity of traditional animation assets with powerful physical and procedural animation techniques in a single creative environment. View Havok’s extensible end-to-end character content creation pipeline spanning physics, animation, and real-time behavior asset composition and conditioning.
Threading Game Engines: QUAKE 4 & Enemy Territory QUAKE Wars
Threading Game Engines: QUAKE 4 & Enemy Territory QUAKE Wars
psteinb
Slides from the Paris Game/AI Conference 2011 talk by Neil Henning - covering the
Paris Game/AI Conference 2011
Paris Game/AI Conference 2011
Neil Henning
This is the version of the GPU Programming slides I created for my talk at CocoaConf Chicago 2015
GPU Programming: Chicago CocoaConf 2015
GPU Programming: Chicago CocoaConf 2015
Janie Clayton
This is the final version of my GPU programming talk that I presented at CocoaConf Atlanta.
GPU Programming: CocoaConf Atlanta
GPU Programming: CocoaConf Atlanta
Janie Clayton
This talk will focus solution architects toward thinking about parallelism when designing applications and solutions specifically Threads vs Tasks on TPL, LINQ vs. PLINQ, and Object Oriented versus Functional Programming techniques. This talk will also compare programming languages, how languages differ when dealing with manycore programming, and the different advantages to these languages. Demonstration include C#, VB, and F# features for functional programming, LINQ and TPL. A demonstration of the Concurrency Visualizer in Visual Studio 2010 will also be included.
Architecting Solutions for the Manycore Future
Architecting Solutions for the Manycore Future
Talbott Crowell
A talk given to students at the University of Texas's Game Development program. General information about my experiences in the game industry (from ~10 years ago), as well as more recent work around the game industry.
The nitty gritty of game development
The nitty gritty of game development
basisspace
These are the modified slides I created for 360|iDev in August 2014. These slides will continue to evolve from conference to conference.
GPU Programming 360iDev
GPU Programming 360iDev
Janie Clayton
This presentation was delivered as the closing keynote for the 2015 IoT Slam virtual conference. During the presentation, Ryft VP of Engineering, Pat McGarry, took a close look at how the IoT revolution is changing data analytics and driving the move of data analysis to the network’s edge where the data is being created. - See more at: http://www.ryft.com/blog/2015-iot-slam-keynote-harnessing-flood-of-iot-data-with-heterogenenous-computing-at-the-edge#sthash.x1Anoapb.dpuf
IoT Slam Keynote: Harnessing the Flood of Data with Heterogeneous Computing a...
IoT Slam Keynote: Harnessing the Flood of Data with Heterogeneous Computing a...
Ryft
High End Modeling & Imaging with Intel Iris Pro Graphics Jay Tedeschi, Sr. Technical Marketing Specialist, Autodesk Inc.
High End Modeling & Imaging with Intel Iris Pro Graphics
High End Modeling & Imaging with Intel Iris Pro Graphics
Intel® Software
This talk covers the work Intel and Epic Games have done together to enable improved performance of UE4 on Intel platforms, including DirectX 12 and Android. Many techniques presented are general and apply to all games and engines.
Optimization Deep Dive: Unreal Engine 4 on Intel
Optimization Deep Dive: Unreal Engine 4 on Intel
Intel® Software
Os Lamothe
Os Lamothe
oscon2007
Building accurate machine learning models has been an art of data scientists, i.e., algorithm selection, hyper parameter tuning, feature selection and so on. Recently, challenges to breakthrough this “black-arts” have got started. We have developed a Spark-based automatic predictive modeling system. The system automatically searches the best algorithm, the best parameters and the best features without any manual work. In this talk, we will share how the automation system is designed to exploit attractive advantages of Spark. Our evaluation with real open data demonstrates that our system could explore hundreds of predictive models and discovers the highly-accurate predictive model in minutes on a Ultra High Density Server, which employs 272 CPU cores, 2TB memory and 17TB SSD in 3U chassis. We will also share open challenges to learn such a massive amount of models on Spark, particularly from reliability and stability standpoints.
No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark with Ma...
No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark with Ma...
Databricks
SIGGRAPH 2013 presentation on the future of visual computing with OpenGL 4.4 on ARM.
Sig13 ce future_gfx
Sig13 ce future_gfx
Cass Everitt
Presentation
Presentation
butest
Building accurate machine learning models has been an art of data scientists, i.e., algorithm selection, hyper parameter tuning, feature selection and so on. Recently, challenges to breakthrough this “black-arts” have got started. We have developed a Spark-based automatic predictive modeling system. The system automatically searches the best algorithm, the best parameters and the best features without any manual work. In this talk, we will share how the automation system is designed to exploit attractive advantages of Spark. Our evaluation with real open data demonstrates that our system could explore hundreds of predictive models and discovers the highly-accurate predictive model in minutes on a Ultra High Density Server, which employs 272 CPU cores, 2TB memory and 17TB SSD in 3U chassis. We will also share open challenges to learn such a massive amount of models on Spark, particularly from reliability and stability standpoints.
No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark with Ma...
No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark with Ma...
Databricks
A Survey on in-a-box parallel computing and its implications on system softwa...
A Survey on in-a-box parallel computing and its implications on system softwa...
ChangWoo Min
I have introduced developments in multi-core computers along with their architectural developments. Also, I have explained about high performance computing, where these are used. At the end, openMP is introduced with many ready to run parallel programs.
Webinaron muticoreprocessors
Webinaron muticoreprocessors
Nagasuri Bala Venkateswarlu
Joint Dell and NVIDIA iTech webinar
Dell NVIDIA AI Powered Transformation Webinar
Dell NVIDIA AI Powered Transformation Webinar
Bill Wong
Similar to Threading Successes 01 Intro
(20)
Multiple Cores, Multiple Pipes, Multiple Threads – Do we have more Parallelis...
Multiple Cores, Multiple Pipes, Multiple Threads – Do we have more Parallelis...
Scalability for All: Unreal Engine* 4 with Intel
Scalability for All: Unreal Engine* 4 with Intel
Threading Game Engines: QUAKE 4 & Enemy Territory QUAKE Wars
Threading Game Engines: QUAKE 4 & Enemy Territory QUAKE Wars
Paris Game/AI Conference 2011
Paris Game/AI Conference 2011
GPU Programming: Chicago CocoaConf 2015
GPU Programming: Chicago CocoaConf 2015
GPU Programming: CocoaConf Atlanta
GPU Programming: CocoaConf Atlanta
Architecting Solutions for the Manycore Future
Architecting Solutions for the Manycore Future
The nitty gritty of game development
The nitty gritty of game development
GPU Programming 360iDev
GPU Programming 360iDev
IoT Slam Keynote: Harnessing the Flood of Data with Heterogeneous Computing a...
IoT Slam Keynote: Harnessing the Flood of Data with Heterogeneous Computing a...
High End Modeling & Imaging with Intel Iris Pro Graphics
High End Modeling & Imaging with Intel Iris Pro Graphics
Optimization Deep Dive: Unreal Engine 4 on Intel
Optimization Deep Dive: Unreal Engine 4 on Intel
Os Lamothe
Os Lamothe
No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark with Ma...
No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark with Ma...
Sig13 ce future_gfx
Sig13 ce future_gfx
Presentation
Presentation
No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark with Ma...
No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark with Ma...
A Survey on in-a-box parallel computing and its implications on system softwa...
A Survey on in-a-box parallel computing and its implications on system softwa...
Webinaron muticoreprocessors
Webinaron muticoreprocessors
Dell NVIDIA AI Powered Transformation Webinar
Dell NVIDIA AI Powered Transformation Webinar
Recently uploaded
Código Creativo y Arte de Software | Unidad 1
Código Creativo y Arte de Software | Unidad 1
Código Creativo y Arte de Software | Unidad 1
Maestría en Comunicación Digital Interactiva - UNR
test
Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdf
ciinovamais
Students will get the knowledge of the following- meaning of the pricing, its importance, objectives, methods of pricing, factors affecting the price of products, An overview of DPCO (Drug Price Control Order) and NPPA (National Pharmaceutical Pricing Authority)
Unit-V; Pricing (Pharma Marketing Management).pptx
Unit-V; Pricing (Pharma Marketing Management).pptx
VishalSingh1417
INDIA THAT IS BHARAT IN 2024 The preliminary round of Swadesh, The india quiz conducted on 30th April, 2024.
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
RAM LAL ANAND COLLEGE, DELHI UNIVERSITY.
exam for kinder
fourth grading exam for kindergarten in writing
fourth grading exam for kindergarten in writing
TeacherCyreneCayanan
Students will get the knowledge of the following: - meaning of Pharmaceutical sales representative (PSR) - purpose of detailing, training & supervision - norms of customer calls - motivating, evaluating, compensation and future aspects of PSR
Unit-IV; Professional Sales Representative (PSR).pptx
Unit-IV; Professional Sales Representative (PSR).pptx
VishalSingh1417
This slide will show how to set domains for a field in odoo 17. Domain is mainly used to select records from the models. It is possible to limit the number of records shown in the field by applying domain to a field, i.e. add some conditions for selecting limited records.
How to Give a Domain for a Field in Odoo 17
How to Give a Domain for a Field in Odoo 17
Celine George
This presentation was provided by William Mattingly of the Smithsonian Institution, during the third segment of the NISO training series "AI & Prompt Design." Session Three: Beginning Conversations, was held on April 18, 2024.
Mattingly "AI & Prompt Design: The Basics of Prompt Design"
Mattingly "AI & Prompt Design: The Basics of Prompt Design"
National Information Standards Organization (NISO)
Mixin classes are helpful for developers to extend the models. Using these classes helps to modify fields, methods and other functionalities of models without directly changing the base models. This slide will show how to extend models using mixin classes in odoo 17.
Mixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
Mixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
Celine George
APM Welcome Tuesday 30 April 2024 APM North West Network Conference, Synergies Across Sectors Presented by: Professor Adam Boddison OBE, Chief Executive Officer, APM Conference overview: https://www.apm.org.uk/community/apm-north-west-branch-conference/ Content description: APM welcome from CEO The main conference objective was to promote the Project Management profession with interaction between project practitioners, APM Corporate members, current project management students, academia and all who have an interest in projects.
APM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across Sectors
Association for Project Management
.
Gardella_Mateo_IntellectualProperty.pdf.
Gardella_Mateo_IntellectualProperty.pdf.
MateoGardella
In this webinar, members learned the ABCs of keeping books for a nonprofit organization. Some of the key takeaways were: - What is accounting and how does it work? - How do you read a financial statement? - What are the three things that nonprofits are required to track? -And more
Introduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The Basics
TechSoup
In Bachelor of Pharmacy course, Class- 1st year, sem-II Subject EVS having topic of ECOLOGICAL SUCCESSION under the ECOSYSTEM point in this presentation points like ecological succession , types of ecological succession like primary and secondary explain with diagram. Students having deep knowledge about Ecological Succession after studying this presentation.
Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...
Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...
Shubhangi Sonawane
Foster students' wonder and curiosity about infinity. The "mathematical concepts of the infinite can do much to engage and propel our thinking about God” Bradley & Howell, p. 56.
This PowerPoint helps students to consider the concept of infinity.
This PowerPoint helps students to consider the concept of infinity.
christianmathematics
My CV as of the end of April 2024
Holdier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdf
agholdier
.
Gardella_PRCampaignConclusion Pitch Letter
Gardella_PRCampaignConclusion Pitch Letter
MateoGardella
Mehran University Newsletter is a Quarterly Publication from Public Relations Office
Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University of Engineering & Technology, Jamshoro
In BC’s nearly-decade-old “new” curriculum, the curricular competencies describe the processes that students are expected to develop in areas of learning such as mathematics. They reflect the “Do” in the “Know-Do-Understand” model. Under the “Communicating” header falls the curricular competency “Explain and justify mathematical ideas and decisions.” Note that it contains two processes: “Explain mathematical ideas” and “Justify mathematical decisions.” I have broken it down into its separate parts in order to understand--or reveal--its meaning. The first part is commonplace in classrooms. By now, BC math teachers—and students—understand that “Explain mathematical ideas” means more than “Show your work.” Teachers consistently ask “What did you do?” and “How do you know?” This process is about retelling, not just of steps but of thinking. The second part happens less frequently. Think back to the last time that you observed a student make—a necessary precursor to justify—a mathematical decision. “Justify” is about defending. Like “explain,” it involves reasoning; unlike “explain,” it also involves opinion and debate. In order to reinterpret the curricular competency “Explain and justify mathematical ideas and decisions,” I will continue to take apart its constituent part “Justify mathematical decisions” and carefully examine the term “mathematical decisions.” What, exactly, is a “mathematical decision”? Below, I will categorize answers to this question. These categories, and the provided examples, may help to suggest new opportunities for students to justify.
Making and Justifying Mathematical Decisions.pdf
Making and Justifying Mathematical Decisions.pdf
Chris Hunter
process recording format
PROCESS RECORDING FORMAT.docx
PROCESS RECORDING FORMAT.docx
PoojaSen20
Advance Mobile application development -(firebase Auth) for faculty of computers stuents seiyun University , yemen class - 07
Advance Mobile Application Development class 07
Advance Mobile Application Development class 07
Dr. Mazin Mohamed alkathiri
Recently uploaded
(20)
Código Creativo y Arte de Software | Unidad 1
Código Creativo y Arte de Software | Unidad 1
Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdf
Unit-V; Pricing (Pharma Marketing Management).pptx
Unit-V; Pricing (Pharma Marketing Management).pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
fourth grading exam for kindergarten in writing
fourth grading exam for kindergarten in writing
Unit-IV; Professional Sales Representative (PSR).pptx
Unit-IV; Professional Sales Representative (PSR).pptx
How to Give a Domain for a Field in Odoo 17
How to Give a Domain for a Field in Odoo 17
Mattingly "AI & Prompt Design: The Basics of Prompt Design"
Mattingly "AI & Prompt Design: The Basics of Prompt Design"
Mixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
Mixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
APM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across Sectors
Gardella_Mateo_IntellectualProperty.pdf.
Gardella_Mateo_IntellectualProperty.pdf.
Introduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The Basics
Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...
Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...
This PowerPoint helps students to consider the concept of infinity.
This PowerPoint helps students to consider the concept of infinity.
Holdier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdf
Gardella_PRCampaignConclusion Pitch Letter
Gardella_PRCampaignConclusion Pitch Letter
Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024
Making and Justifying Mathematical Decisions.pdf
Making and Justifying Mathematical Decisions.pdf
PROCESS RECORDING FORMAT.docx
PROCESS RECORDING FORMAT.docx
Advance Mobile Application Development class 07
Advance Mobile Application Development class 07
Threading Successes 01 Intro
1.
Threading Successes of
Popular PC Games and Engines Paul Lindberg and Brad Werth Intel Corporation GDC 2008
2.
3.
4.
5.
6.
7.
8.
9.
Download now