�ݺ�ߣshows by User: JennyMidwinter

�ݺ�ߣshows by User: JennyMidwinter / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: JennyMidwinter / Thu, 12 Sep 2019 18:11:17 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: JennyMidwinter Practical Challenges ML Workflows /slideshow/practical-challenges-ml-workflows/171283914 practicalchallengesmlworkflows-190912181117
A presentation for the Ottawa AI/ML Meetup Group, Sept 9, 2019. A person’s Facebook newsfeed from their vacation is to the real-world as Kaggle competitions are to industrial ML/AI projects. This presentation shares with the audience some common challenges in ML/AI that real-world practitioners face while doing their craft, along with suggestions on how to address them. This is a non-technical presentation but does assume some knowledge of data science process and ML/AI.]]>
A presentation for the Ottawa AI/ML Meetup Group, Sept 9, 2019. A person’s Facebook newsfeed from their vacation is to the real-world as Kaggle competitions are to industrial ML/AI projects. This presentation shares with the audience some common challenges in ML/AI that real-world practitioners face while doing their craft, along with suggestions on how to address them. This is a non-technical presentation but does assume some knowledge of data science process and ML/AI.]]> Thu, 12 Sep 2019 18:11:17 GMT /slideshow/practical-challenges-ml-workflows/171283914 JennyMidwinter@slideshare.net(JennyMidwinter) Practical Challenges ML Workflows JennyMidwinter A presentation for the Ottawa AI/ML Meetup Group, Sept 9, 2019. A person’s Facebook newsfeed from their vacation is to the real-world as Kaggle competitions are to industrial ML/AI projects. This presentation shares with the audience some common challenges in ML/AI that real-world practitioners face while doing their craft, along with suggestions on how to address them. This is a non-technical presentation but does assume some knowledge of data science process and ML/AI. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/practicalchallengesmlworkflows-190912181117-thumbnail.jpg?width=120&height=120&fit=bounds" /> A presentation for the Ottawa AI/ML Meetup Group, Sept 9, 2019. A person’s Facebook newsfeed from their vacation is to the real-world as Kaggle competitions are to industrial ML/AI projects. This presentation shares with the audience some common challenges in ML/AI that real-world practitioners face while doing their craft, along with suggestions on how to address them. This is a non-technical presentation but does assume some knowledge of data science process and ML/AI.

Practical Challenges ML Workflows from Jenny Midwinter

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0 Machine learning applications in clinical brain computer interfacing /slideshow/machine-learning-applications-in-clinical-brain-computer-interfacing/138497580 boulaymlmeetup-190327191917
Brain computer interfaces (BCIs) provide alternative communication channels from the brain to external devices for severely disabled individuals and can be used to induce and guide adaptive plasticity for recovery after central nervous system trauma. Clinical BCI effectiveness depends on robust and accurate modeling of the relationship between brain signals and behaviour. Dr. Boulay will give a brief survey of BCI technologies and discuss common BCI paradigms and implementations, with an emphasis on clinical BCI brain signals and machine-learning applications. * From the Ottawa AI/ML Meetup June 2018. ]]>
Brain computer interfaces (BCIs) provide alternative communication channels from the brain to external devices for severely disabled individuals and can be used to induce and guide adaptive plasticity for recovery after central nervous system trauma. Clinical BCI effectiveness depends on robust and accurate modeling of the relationship between brain signals and behaviour. Dr. Boulay will give a brief survey of BCI technologies and discuss common BCI paradigms and implementations, with an emphasis on clinical BCI brain signals and machine-learning applications. * From the Ottawa AI/ML Meetup June 2018. ]]> Wed, 27 Mar 2019 19:19:17 GMT /slideshow/machine-learning-applications-in-clinical-brain-computer-interfacing/138497580 JennyMidwinter@slideshare.net(JennyMidwinter) Machine learning applications in clinical brain computer interfacing JennyMidwinter Brain computer interfaces (BCIs) provide alternative communication channels from the brain to external devices for severely disabled individuals and can be used to induce and guide adaptive plasticity for recovery after central nervous system trauma. Clinical BCI effectiveness depends on robust and accurate modeling of the relationship between brain signals and behaviour. Dr. Boulay will give a brief survey of BCI technologies and discuss common BCI paradigms and implementations, with an emphasis on clinical BCI brain signals and machine-learning applications. * From the Ottawa AI/ML Meetup June 2018. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/boulaymlmeetup-190327191917-thumbnail.jpg?width=120&height=120&fit=bounds" /> Brain computer interfaces (BCIs) provide alternative communication channels from the brain to external devices for severely disabled individuals and can be used to induce and guide adaptive plasticity for recovery after central nervous system trauma. Clinical BCI effectiveness depends on robust and accurate modeling of the relationship between brain signals and behaviour. Dr. Boulay will give a brief survey of BCI technologies and discuss common BCI paradigms and implementations, with an emphasis on clinical BCI brain signals and machine-learning applications. * From the Ottawa AI/ML Meetup June 2018.

Machine learning applications in clinical brain computer interfacing from Jenny Midwinter

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0 Augmented Intelligence Bridging the Gap Between BI and AI /slideshow/augmented-intelligence-bridging-the-gap-between-bi-and-ai/124180615 ottawaaimeetupnov2018nonotes-181127162925
In this session Matt will give an overview of the Qlik Cognitive Engine and Qlik’s vision for Augmented Intelligence. Using Artificial Intelligence and Machine Learning the Qlik Cognitive Engine help analytics users find insight in their data faster in a self-service framework. Matt will give highlights of the Augmented Intelligence approach and implementation and share the technical journey from research to delivery of the functionality. ** From the Ottawa AI/ML Meetup November 26, 2018.]]>
In this session Matt will give an overview of the Qlik Cognitive Engine and Qlik’s vision for Augmented Intelligence. Using Artificial Intelligence and Machine Learning the Qlik Cognitive Engine help analytics users find insight in their data faster in a self-service framework. Matt will give highlights of the Augmented Intelligence approach and implementation and share the technical journey from research to delivery of the functionality. ** From the Ottawa AI/ML Meetup November 26, 2018.]]> Tue, 27 Nov 2018 16:29:25 GMT /slideshow/augmented-intelligence-bridging-the-gap-between-bi-and-ai/124180615 JennyMidwinter@slideshare.net(JennyMidwinter) Augmented Intelligence Bridging the Gap Between BI and AI JennyMidwinter In this session Matt will give an overview of the Qlik Cognitive Engine and Qlik’s vision for Augmented Intelligence. Using Artificial Intelligence and Machine Learning the Qlik Cognitive Engine help analytics users find insight in their data faster in a self-service framework. Matt will give highlights of the Augmented Intelligence approach and implementation and share the technical journey from research to delivery of the functionality. ** From the Ottawa AI/ML Meetup November 26, 2018. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ottawaaimeetupnov2018nonotes-181127162925-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this session Matt will give an overview of the Qlik Cognitive Engine and Qlik’s vision for Augmented Intelligence. Using Artificial Intelligence and Machine Learning the Qlik Cognitive Engine help analytics users find insight in their data faster in a self-service framework. Matt will give highlights of the Augmented Intelligence approach and implementation and share the technical journey from research to delivery of the functionality. ** From the Ottawa AI/ML Meetup November 26, 2018.

Augmented Intelligence Bridging the Gap Between BI and AI from Jenny Midwinter

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0 Autonomous Learning for Autonomous Systems, by Prof. Plamen Angelov /slideshow/autonomous-learning-for-autonomous-systems-by-prof-plamen-angelov/117970464 autonomouslearning-plamenangelov-181003140251
AI/ML Ottawa Meetup group presentation on September 24, 2018 at Bayview Yards/Invest Ottawa. ]]>
AI/ML Ottawa Meetup group presentation on September 24, 2018 at Bayview Yards/Invest Ottawa. ]]> Wed, 03 Oct 2018 14:02:51 GMT /slideshow/autonomous-learning-for-autonomous-systems-by-prof-plamen-angelov/117970464 JennyMidwinter@slideshare.net(JennyMidwinter) Autonomous Learning for Autonomous Systems, by Prof. Plamen Angelov JennyMidwinter AI/ML Ottawa Meetup group presentation on September 24, 2018 at Bayview Yards/Invest Ottawa. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/autonomouslearning-plamenangelov-181003140251-thumbnail.jpg?width=120&height=120&fit=bounds" /> AI/ML Ottawa Meetup group presentation on September 24, 2018 at Bayview Yards/Invest Ottawa.

Autonomous Learning for Autonomous Systems, by Prof. Plamen Angelov from Jenny Midwinter

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0 Ai and analytics for business /JennyMidwinter/ai-and-analytics-for-business aiandanalyticsforbusiness-180523142845
Charts from Ottawa AI & ML Meetup talk on May 17, 2018, by Dr Stephen W Thomas. ]]>
Charts from Ottawa AI & ML Meetup talk on May 17, 2018, by Dr Stephen W Thomas. ]]> Wed, 23 May 2018 14:28:45 GMT /JennyMidwinter/ai-and-analytics-for-business JennyMidwinter@slideshare.net(JennyMidwinter) Ai and analytics for business JennyMidwinter Charts from Ottawa AI & ML Meetup talk on May 17, 2018, by Dr Stephen W Thomas. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aiandanalyticsforbusiness-180523142845-thumbnail.jpg?width=120&height=120&fit=bounds" /> Charts from Ottawa AI & ML Meetup talk on May 17, 2018, by Dr Stephen W Thomas.

Ai and analytics for business from Jenny Midwinter

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0 Introduction to Natural Language Processing /slideshow/introduction-to-natural-language-processing-95113963/95113963 introtonlp-180426140947
Introduction to Natural Language Processing. Review of techniques , both classic (legacy) and recent deep learning. Presentation from the Ottawa Machine Learning and Artificial Intelligence Meetup, April 2018.]]>
Introduction to Natural Language Processing. Review of techniques , both classic (legacy) and recent deep learning. Presentation from the Ottawa Machine Learning and Artificial Intelligence Meetup, April 2018.]]> Thu, 26 Apr 2018 14:09:47 GMT /slideshow/introduction-to-natural-language-processing-95113963/95113963 JennyMidwinter@slideshare.net(JennyMidwinter) Introduction to Natural Language Processing JennyMidwinter Introduction to Natural Language Processing. Review of techniques , both classic (legacy) and recent deep learning. Presentation from the Ottawa Machine Learning and Artificial Intelligence Meetup, April 2018. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introtonlp-180426140947-thumbnail.jpg?width=120&height=120&fit=bounds" /> Introduction to Natural Language Processing. Review of techniques , both classic (legacy) and recent deep learning. Presentation from the Ottawa Machine Learning and Artificial Intelligence Meetup, April 2018.

Introduction to Natural Language Processing from Jenny Midwinter

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0 Building an NLP DNN in 5 Minutes /slideshow/building-an-nlp-dnn-in-5-minutes/95113837 building-nlp-dnn-in5minutes-180426140837
Instructions on how to build a Natural Language Processing Deep Neural Network in 5 minutes. Presentation from the Ottawa Machine Learning and Artificial Intelligence Meetup, April 2018.]]>
Instructions on how to build a Natural Language Processing Deep Neural Network in 5 minutes. Presentation from the Ottawa Machine Learning and Artificial Intelligence Meetup, April 2018.]]> Thu, 26 Apr 2018 14:08:37 GMT /slideshow/building-an-nlp-dnn-in-5-minutes/95113837 JennyMidwinter@slideshare.net(JennyMidwinter) Building an NLP DNN in 5 Minutes JennyMidwinter Instructions on how to build a Natural Language Processing Deep Neural Network in 5 minutes. Presentation from the Ottawa Machine Learning and Artificial Intelligence Meetup, April 2018. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/building-nlp-dnn-in5minutes-180426140837-thumbnail.jpg?width=120&height=120&fit=bounds" /> Instructions on how to build a Natural Language Processing Deep Neural Network in 5 minutes. Presentation from the Ottawa Machine Learning and Artificial Intelligence Meetup, April 2018.

Building an NLP DNN in 5 Minutes from Jenny Midwinter

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0 Machine Learning meets Granular Computing /slideshow/machine-learning-meets-granular-computing/89111113 180226mlmeetsgrc2-180227203117
Machine Learning meets Granular Computing: the emergence of granular models in the Big Data era ** Presentation �ݺ�ߣs from Dr Rafael Falcon, from Larus Technologies, for the February 2018 Ottawa Machine Learning & Artificial Intelligence Meetup Abstract Traditional Machine Learning (ML) models are unable to effectively cope with the challenges posed by the many V’s (volume, velocity, variety, etc.) characterizing the Big Data phenomenon. This has triggered the need to revisit the underlying principles and assumptions ML stands upon. Dimensionality reduction, feature/instance selection, increased computational power and parallel/distributed algorithm implementations are well-known approaches to deal with these large volumes of data. In this talk we will introduce Granular Computing (GrC), a vibrant research discipline devoted to the design of high-level information granules and their inference frameworks. By adopting more symbolic constructs such as sets, intervals or similarity classes to describe numerical data, GrC has paved the way for a more human-centric manner of interacting with and reasoning about the real world. We will go over several granular models that address common ML tasks such as classification/clustering and will outline a methodology to appropriately design information granules for the problem at hand. Though not a mainstream concept yet, GrC is a promising direction for ML systems to harness Big Data. ]]>
Machine Learning meets Granular Computing: the emergence of granular models in the Big Data era ** Presentation �ݺ�ߣs from Dr Rafael Falcon, from Larus Technologies, for the February 2018 Ottawa Machine Learning & Artificial Intelligence Meetup Abstract Traditional Machine Learning (ML) models are unable to effectively cope with the challenges posed by the many V’s (volume, velocity, variety, etc.) characterizing the Big Data phenomenon. This has triggered the need to revisit the underlying principles and assumptions ML stands upon. Dimensionality reduction, feature/instance selection, increased computational power and parallel/distributed algorithm implementations are well-known approaches to deal with these large volumes of data. In this talk we will introduce Granular Computing (GrC), a vibrant research discipline devoted to the design of high-level information granules and their inference frameworks. By adopting more symbolic constructs such as sets, intervals or similarity classes to describe numerical data, GrC has paved the way for a more human-centric manner of interacting with and reasoning about the real world. We will go over several granular models that address common ML tasks such as classification/clustering and will outline a methodology to appropriately design information granules for the problem at hand. Though not a mainstream concept yet, GrC is a promising direction for ML systems to harness Big Data. ]]> Tue, 27 Feb 2018 20:31:17 GMT /slideshow/machine-learning-meets-granular-computing/89111113 JennyMidwinter@slideshare.net(JennyMidwinter) Machine Learning meets Granular Computing JennyMidwinter Machine Learning meets Granular Computing: the emergence of granular models in the Big Data era ** Presentation �ݺ�ߣs from Dr Rafael Falcon, from Larus Technologies, for the February 2018 Ottawa Machine Learning & Artificial Intelligence Meetup Abstract Traditional Machine Learning (ML) models are unable to effectively cope with the challenges posed by the many V’s (volume, velocity, variety, etc.) characterizing the Big Data phenomenon. This has triggered the need to revisit the underlying principles and assumptions ML stands upon. Dimensionality reduction, feature/instance selection, increased computational power and parallel/distributed algorithm implementations are well-known approaches to deal with these large volumes of data. In this talk we will introduce Granular Computing (GrC), a vibrant research discipline devoted to the design of high-level information granules and their inference frameworks. By adopting more symbolic constructs such as sets, intervals or similarity classes to describe numerical data, GrC has paved the way for a more human-centric manner of interacting with and reasoning about the real world. We will go over several granular models that address common ML tasks such as classification/clustering and will outline a methodology to appropriately design information granules for the problem at hand. Though not a mainstream concept yet, GrC is a promising direction for ML systems to harness Big Data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/180226mlmeetsgrc2-180227203117-thumbnail.jpg?width=120&height=120&fit=bounds" /> Machine Learning meets Granular Computing: the emergence of granular models in the Big Data era ** Presentation �ݺ�ߣs from Dr Rafael Falcon, from Larus Technologies, for the February 2018 Ottawa Machine Learning & Artificial Intelligence Meetup Abstract Traditional Machine Learning (ML) models are unable to effectively cope with the challenges posed by the many V’s (volume, velocity, variety, etc.) characterizing the Big Data phenomenon. This has triggered the need to revisit the underlying principles and assumptions ML stands upon. Dimensionality reduction, feature/instance selection, increased computational power and parallel/distributed algorithm implementations are well-known approaches to deal with these large volumes of data. In this talk we will introduce Granular Computing (GrC), a vibrant research discipline devoted to the design of high-level information granules and their inference frameworks. By adopting more symbolic constructs such as sets, intervals or similarity classes to describe numerical data, GrC has paved the way for a more human-centric manner of interacting with and reasoning about the real world. We will go over several granular models that address common ML tasks such as classification/clustering and will outline a methodology to appropriately design information granules for the problem at hand. Though not a mainstream concept yet, GrC is a promising direction for ML systems to harness Big Data.

Machine Learning meets Granular Computing from Jenny Midwinter

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0 2016 09-19 - stephan jou - machine learning meetup v1 /slideshow/2016-0919-stephan-jou-machine-learning-meetup-v1/78095598 2016-09-19-stephanjou-machinelearningmeetupv1-170720173228
Presentation slides from the Sept, 2016 Ottawa Machine Learning Meetup "Catching Bad Guys with Math: Real World Data Science Use Cases for Cyberattack Detection and Prevention", by Stephan Jou. ]]>
Presentation slides from the Sept, 2016 Ottawa Machine Learning Meetup "Catching Bad Guys with Math: Real World Data Science Use Cases for Cyberattack Detection and Prevention", by Stephan Jou. ]]> Thu, 20 Jul 2017 17:32:27 GMT /slideshow/2016-0919-stephan-jou-machine-learning-meetup-v1/78095598 JennyMidwinter@slideshare.net(JennyMidwinter) 2016 09-19 - stephan jou - machine learning meetup v1 JennyMidwinter Presentation slides from the Sept, 2016 Ottawa Machine Learning Meetup "Catching Bad Guys with Math: Real World Data Science Use Cases for Cyberattack Detection and Prevention", by Stephan Jou. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2016-09-19-stephanjou-machinelearningmeetupv1-170720173228-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presentation slides from the Sept, 2016 Ottawa Machine Learning Meetup "Catching Bad Guys with Math: Real World Data Science Use Cases for Cyberattack Detection and Prevention", by Stephan Jou.

2016 09-19 - stephan jou - machine learning meetup v1 from Jenny Midwinter

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0 Machine Learning at Amazon /slideshow/machine-learning-at-amazon/75479420 2017-04-24mlatamazon-170427191827
Ottawa Machine Learning Meetup April 24, 2017 �ݺ�ߣs. Abstract: Amazon is one of the world's fastest growing companies and is showing no signs of slowing down. This staggering growth often presents serious challenges to the company's ability to scale. In the past, this has tested (and occasionally crushed) conventional software systems, but now this is testing the human decision makers in areas from Product Recommendation to Produce Inspection, from Text Classification to Voice Assistants and more. Matthew Spencer, a Machine Learning Engineer at Amazon will discuss these challenges and how they are being met by a wide variety of Machine Learning applications. ]]>
Ottawa Machine Learning Meetup April 24, 2017 �ݺ�ߣs. Abstract: Amazon is one of the world's fastest growing companies and is showing no signs of slowing down. This staggering growth often presents serious challenges to the company's ability to scale. In the past, this has tested (and occasionally crushed) conventional software systems, but now this is testing the human decision makers in areas from Product Recommendation to Produce Inspection, from Text Classification to Voice Assistants and more. Matthew Spencer, a Machine Learning Engineer at Amazon will discuss these challenges and how they are being met by a wide variety of Machine Learning applications. ]]> Thu, 27 Apr 2017 19:18:27 GMT /slideshow/machine-learning-at-amazon/75479420 JennyMidwinter@slideshare.net(JennyMidwinter) Machine Learning at Amazon JennyMidwinter Ottawa Machine Learning Meetup April 24, 2017 �ݺ�ߣs. Abstract: Amazon is one of the world's fastest growing companies and is showing no signs of slowing down. This staggering growth often presents serious challenges to the company's ability to scale. In the past, this has tested (and occasionally crushed) conventional software systems, but now this is testing the human decision makers in areas from Product Recommendation to Produce Inspection, from Text Classification to Voice Assistants and more. Matthew Spencer, a Machine Learning Engineer at Amazon will discuss these challenges and how they are being met by a wide variety of Machine Learning applications. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2017-04-24mlatamazon-170427191827-thumbnail.jpg?width=120&height=120&fit=bounds" /> Ottawa Machine Learning Meetup April 24, 2017 �ݺ�ߣs. Abstract: Amazon is one of the world's fastest growing companies and is showing no signs of slowing down. This staggering growth often presents serious challenges to the company's ability to scale. In the past, this has tested (and occasionally crushed) conventional software systems, but now this is testing the human decision makers in areas from Product Recommendation to Produce Inspection, from Text Classification to Voice Assistants and more. Matthew Spencer, a Machine Learning Engineer at Amazon will discuss these challenges and how they are being met by a wide variety of Machine Learning applications.

Machine Learning at Amazon from Jenny Midwinter

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0 AI and Machine Learning: The many different approaches /slideshow/ai-and-machine-learning-the-many-different-approaches/74204286 machinelearningmeetup-onai-170402225101
Ottawa Machine Learning Meetup slides for event March 20, 2017, A whirlwind tour of AI and Machine Learning: When to use which technique, by Robin Grosset, Chief Technology Officer, MindBridge.Ai.]]>
Ottawa Machine Learning Meetup slides for event March 20, 2017, A whirlwind tour of AI and Machine Learning: When to use which technique, by Robin Grosset, Chief Technology Officer, MindBridge.Ai.]]> Sun, 02 Apr 2017 22:51:00 GMT /slideshow/ai-and-machine-learning-the-many-different-approaches/74204286 JennyMidwinter@slideshare.net(JennyMidwinter) AI and Machine Learning: The many different approaches JennyMidwinter Ottawa Machine Learning Meetup slides for event March 20, 2017, A whirlwind tour of AI and Machine Learning: When to use which technique, by Robin Grosset, Chief Technology Officer, MindBridge.Ai. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/machinelearningmeetup-onai-170402225101-thumbnail.jpg?width=120&height=120&fit=bounds" /> Ottawa Machine Learning Meetup slides for event March 20, 2017, A whirlwind tour of AI and Machine Learning: When to use which technique, by Robin Grosset, Chief Technology Officer, MindBridge.Ai.

AI and Machine Learning: The many different approaches from Jenny Midwinter

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0 Applying Deep Learning Vision Technology to low-cost/power Embedded Systems /slideshow/applying-deep-learning-vision-technology-to-lowcostpower-embedded-systems/71149753 20170118ottawamachinelearningmtgsent-170118155947
�ݺ�ߣs from Ottawa Machine Learning Meetup from January 16, 2016. Pierre Paulin, Director of R&D at Synopsys (Embedded Vision Subsystems) , will be will be making a presentation on: “Applying Deep Learning Vision Technology to Low-Cost, Low-Power Embedded Systems: An Industrial Perspective” ]]>
�ݺ�ߣs from Ottawa Machine Learning Meetup from January 16, 2016. Pierre Paulin, Director of R&D at Synopsys (Embedded Vision Subsystems) , will be will be making a presentation on: “Applying Deep Learning Vision Technology to Low-Cost, Low-Power Embedded Systems: An Industrial Perspective” ]]> Wed, 18 Jan 2017 15:59:47 GMT /slideshow/applying-deep-learning-vision-technology-to-lowcostpower-embedded-systems/71149753 JennyMidwinter@slideshare.net(JennyMidwinter) Applying Deep Learning Vision Technology to low-cost/power Embedded Systems JennyMidwinter �ݺ�ߣs from Ottawa Machine Learning Meetup from January 16, 2016. Pierre Paulin, Director of R&D at Synopsys (Embedded Vision Subsystems) , will be will be making a presentation on: “Applying Deep Learning Vision Technology to Low-Cost, Low-Power Embedded Systems: An Industrial Perspective” <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20170118ottawamachinelearningmtgsent-170118155947-thumbnail.jpg?width=120&height=120&fit=bounds" /> �ݺ�ߣs from Ottawa Machine Learning Meetup from January 16, 2016. Pierre Paulin, Director of R&D at Synopsys (Embedded Vision Subsystems) , will be will be making a presentation on: “Applying Deep Learning Vision Technology to Low-Cost, Low-Power Embedded Systems: An Industrial Perspective”

Applying Deep Learning Vision Technology to low-cost/power Embedded Systems from Jenny Midwinter

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0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/practicalchallengesmlworkflows-190912181117-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/practical-challenges-ml-workflows/171283914 Practical Challenges M... https://cdn.slidesharecdn.com/ss_thumbnails/boulaymlmeetup-190327191917-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/machine-learning-applications-in-clinical-brain-computer-interfacing/138497580 Machine learning appli... https://cdn.slidesharecdn.com/ss_thumbnails/ottawaaimeetupnov2018nonotes-181127162925-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/augmented-intelligence-bridging-the-gap-between-bi-and-ai/124180615 Augmented Intelligence...