ºÝºÝߣshows by User: guard0g / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: guard0g / Thu, 10 Oct 2019 02:19:38 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: guard0g Kubeflow and Data Science in Kubernetes /slideshow/kubeflow-and-data-science-in-kubernetes/180543017 k8s2019meetupkubeflow-191010021938
Deploying machine learning pipelines robustly at scale is one of the biggest challenges within an organization. Kubeflow is an open-source platform for distributed training, tuning, and serving models on Kubenetes. As a comprehensive solution for deploying and managing end-to-end data science and machine learning pipelines, Kubeflow is rapidly accelerating analytics innovation and adoption. John provides an overview of Kubeflow and how he has been using it in the wild.]]>
Deploying machine learning pipelines robustly at scale is one of the biggest challenges within an organization. Kubeflow is an open-source platform for distributed training, tuning, and serving models on Kubenetes. As a comprehensive solution for deploying and managing end-to-end data science and machine learning pipelines, Kubeflow is rapidly accelerating analytics innovation and adoption. John provides an overview of Kubeflow and how he has been using it in the wild.]]> Thu, 10 Oct 2019 02:19:38 GMT /slideshow/kubeflow-and-data-science-in-kubernetes/180543017 guard0g@slideshare.net(guard0g) Kubeflow and Data Science in Kubernetes guard0g Deploying machine learning pipelines robustly at scale is one of the biggest challenges within an organization. Kubeflow is an open-source platform for distributed training, tuning, and serving models on Kubenetes. As a comprehensive solution for deploying and managing end-to-end data science and machine learning pipelines, Kubeflow is rapidly accelerating analytics innovation and adoption. John provides an overview of Kubeflow and how he has been using it in the wild. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/k8s2019meetupkubeflow-191010021938-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Deploying machine learning pipelines robustly at scale is one of the biggest challenges within an organization. Kubeflow is an open-source platform for distributed training, tuning, and serving models on Kubenetes. As a comprehensive solution for deploying and managing end-to-end data science and machine learning pipelines, Kubeflow is rapidly accelerating analytics innovation and adoption. John provides an overview of Kubeflow and how he has been using it in the wild.
Kubeflow and Data Science in Kubernetes from John Liu
]]> 1139 1 https://cdn.slidesharecdn.com/ss_thumbnails/k8s2019meetupkubeflow-191010021938-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Artificial Intelligence As a Service /slideshow/artificial-intelligence-as-a-service-170695360/170695360 nas2019presentation-190910231251
From Amazon to Google, top technology firms have embraced data science and machine learning to improve business outcomes. Yet AI adoption beyond these firms has been slow due to obstacles such as hiring talent, heterogeneous data, and compute infrastructure. Larger firms have built teams to tackle these issues with some success, but small- and mid-tier firms are at a distinct disadvantage. AI as a Service is a paradigm that levels the playing field and empowers businesses across the spectrum.]]>
From Amazon to Google, top technology firms have embraced data science and machine learning to improve business outcomes. Yet AI adoption beyond these firms has been slow due to obstacles such as hiring talent, heterogeneous data, and compute infrastructure. Larger firms have built teams to tackle these issues with some success, but small- and mid-tier firms are at a distinct disadvantage. AI as a Service is a paradigm that levels the playing field and empowers businesses across the spectrum.]]> Tue, 10 Sep 2019 23:12:51 GMT /slideshow/artificial-intelligence-as-a-service-170695360/170695360 guard0g@slideshare.net(guard0g) Artificial Intelligence As a Service guard0g From Amazon to Google, top technology firms have embraced data science and machine learning to improve business outcomes. Yet AI adoption beyond these firms has been slow due to obstacles such as hiring talent, heterogeneous data, and compute infrastructure. Larger firms have built teams to tackle these issues with some success, but small- and mid-tier firms are at a distinct disadvantage. AI as a Service is a paradigm that levels the playing field and empowers businesses across the spectrum. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nas2019presentation-190910231251-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> From Amazon to Google, top technology firms have embraced data science and machine learning to improve business outcomes. Yet AI adoption beyond these firms has been slow due to obstacles such as hiring talent, heterogeneous data, and compute infrastructure. Larger firms have built teams to tackle these issues with some success, but small- and mid-tier firms are at a distinct disadvantage. AI as a Service is a paradigm that levels the playing field and empowers businesses across the spectrum.
Artificial Intelligence As a Service from John Liu
]]> 508 0 https://cdn.slidesharecdn.com/ss_thumbnails/nas2019presentation-190910231251-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Machine Learning with Small Data /guard0g/machine-learning-with-small-data-150646219 nashvillemeetupjune182019-190619155218
The concurrent rise of big data, modern hardware, and deep learning promises to transform analytics within healthcare and life science organizations. But big data is expensive to annotate, and not all data is created equal. The practicality of most problems requires inference from small datasets and incorporation of external knowledge. This is particularly true for tasks that involve natural language processing. John will discuss several methods that allow us to introduce prior knowledge to learn from small data, including deep contextual representations, inductive transfer learning, and adversarial augmentation. Presentation at the Joint Meeting: Nashville Data Science and Greater Nashville Healthcare Analytics on June 18, 2019.]]>
The concurrent rise of big data, modern hardware, and deep learning promises to transform analytics within healthcare and life science organizations. But big data is expensive to annotate, and not all data is created equal. The practicality of most problems requires inference from small datasets and incorporation of external knowledge. This is particularly true for tasks that involve natural language processing. John will discuss several methods that allow us to introduce prior knowledge to learn from small data, including deep contextual representations, inductive transfer learning, and adversarial augmentation. Presentation at the Joint Meeting: Nashville Data Science and Greater Nashville Healthcare Analytics on June 18, 2019.]]> Wed, 19 Jun 2019 15:52:18 GMT /guard0g/machine-learning-with-small-data-150646219 guard0g@slideshare.net(guard0g) Machine Learning with Small Data guard0g The concurrent rise of big data, modern hardware, and deep learning promises to transform analytics within healthcare and life science organizations. But big data is expensive to annotate, and not all data is created equal. The practicality of most problems requires inference from small datasets and incorporation of external knowledge. This is particularly true for tasks that involve natural language processing. John will discuss several methods that allow us to introduce prior knowledge to learn from small data, including deep contextual representations, inductive transfer learning, and adversarial augmentation. Presentation at the Joint Meeting: Nashville Data Science and Greater Nashville Healthcare Analytics on June 18, 2019. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nashvillemeetupjune182019-190619155218-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The concurrent rise of big data, modern hardware, and deep learning promises to transform analytics within healthcare and life science organizations. But big data is expensive to annotate, and not all data is created equal. The practicality of most problems requires inference from small datasets and incorporation of external knowledge. This is particularly true for tasks that involve natural language processing. John will discuss several methods that allow us to introduce prior knowledge to learn from small data, including deep contextual representations, inductive transfer learning, and adversarial augmentation. Presentation at the Joint Meeting: Nashville Data Science and Greater Nashville Healthcare Analytics on June 18, 2019.
Machine Learning with Small Data from John Liu
]]> 322 3 https://cdn.slidesharecdn.com/ss_thumbnails/nashvillemeetupjune182019-190619155218-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Data Analytics in Computational Law /slideshow/data-analytics-in-computational-law/137908180 vanderbiltcomputationallawpanel-190323231952
Presented at the Vanderbilt Law School: Computational Law + Blockchain Fest]]>
Presented at the Vanderbilt Law School: Computational Law + Blockchain Fest]]> Sat, 23 Mar 2019 23:19:52 GMT /slideshow/data-analytics-in-computational-law/137908180 guard0g@slideshare.net(guard0g) Data Analytics in Computational Law guard0g Presented at the Vanderbilt Law School: Computational Law + Blockchain Fest <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/vanderbiltcomputationallawpanel-190323231952-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presented at the Vanderbilt Law School: Computational Law + Blockchain Fest
Data Analytics in Computational Law from John Liu
]]> 99 3 https://cdn.slidesharecdn.com/ss_thumbnails/vanderbiltcomputationallawpanel-190323231952-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 AI & Machine Learning: Business Transformation /slideshow/ai-machine-learning-business-transformation/125458463 nashvilleciocouncilpresentation-181209222002
Macro perspective on how AI and Machine Learning are transforming businesses and the economy. Presented at the Nashville CIO Council on November 9, 2018.]]>
Macro perspective on how AI and Machine Learning are transforming businesses and the economy. Presented at the Nashville CIO Council on November 9, 2018.]]> Sun, 09 Dec 2018 22:20:01 GMT /slideshow/ai-machine-learning-business-transformation/125458463 guard0g@slideshare.net(guard0g) AI & Machine Learning: Business Transformation guard0g Macro perspective on how AI and Machine Learning are transforming businesses and the economy. Presented at the Nashville CIO Council on November 9, 2018. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nashvilleciocouncilpresentation-181209222002-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Macro perspective on how AI and Machine Learning are transforming businesses and the economy. Presented at the Nashville CIO Council on November 9, 2018.
AI & Machine Learning: Business Transformation from John Liu
]]> 188 1 https://cdn.slidesharecdn.com/ss_thumbnails/nashvilleciocouncilpresentation-181209222002-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 DeepREM /slideshow/deeprem/123286120 deepremnhw-181117150615
If you have a computer like mine, you probably have thousands of files stored haphazardly in folders or crowded on the desktop. Who has the time anymore to sort and organize these files? What if there was an autonomous agent that could? I am building DeepREM, a machine learning agent that links, indexes, and organizes documents while running in the background on your computer. Similar to how the human brain uses sleep to organize daily sensory input, DeepREM incrementally organizes your file contents systematically and hierarchically into virtual topics/classes/taxonomies based on metadata and text/numerical contents. It can be customized by automatically learning how you organize files. It’s much more than a file browser or search agent. And if you think it’s helpful for your personal computer, think what it can do to unlock the intelligence stored in all those PC hard drives across your company.]]>
If you have a computer like mine, you probably have thousands of files stored haphazardly in folders or crowded on the desktop. Who has the time anymore to sort and organize these files? What if there was an autonomous agent that could? I am building DeepREM, a machine learning agent that links, indexes, and organizes documents while running in the background on your computer. Similar to how the human brain uses sleep to organize daily sensory input, DeepREM incrementally organizes your file contents systematically and hierarchically into virtual topics/classes/taxonomies based on metadata and text/numerical contents. It can be customized by automatically learning how you organize files. It’s much more than a file browser or search agent. And if you think it’s helpful for your personal computer, think what it can do to unlock the intelligence stored in all those PC hard drives across your company.]]> Sat, 17 Nov 2018 15:06:15 GMT /slideshow/deeprem/123286120 guard0g@slideshare.net(guard0g) DeepREM guard0g If you have a computer like mine, you probably have thousands of files stored haphazardly in folders or crowded on the desktop. Who has the time anymore to sort and organize these files? What if there was an autonomous agent that could? I am building DeepREM, a machine learning agent that links, indexes, and organizes documents while running in the background on your computer. Similar to how the human brain uses sleep to organize daily sensory input, DeepREM incrementally organizes your file contents systematically and hierarchically into virtual topics/classes/taxonomies based on metadata and text/numerical contents. It can be customized by automatically learning how you organize files. It’s much more than a file browser or search agent. And if you think it’s helpful for your personal computer, think what it can do to unlock the intelligence stored in all those PC hard drives across your company. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/deepremnhw-181117150615-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> If you have a computer like mine, you probably have thousands of files stored haphazardly in folders or crowded on the desktop. Who has the time anymore to sort and organize these files? What if there was an autonomous agent that could? I am building DeepREM, a machine learning agent that links, indexes, and organizes documents while running in the background on your computer. Similar to how the human brain uses sleep to organize daily sensory input, DeepREM incrementally organizes your file contents systematically and hierarchically into virtual topics/classes/taxonomies based on metadata and text/numerical contents. It can be customized by automatically learning how you organize files. It’s much more than a file browser or search agent. And if you think it’s helpful for your personal computer, think what it can do to unlock the intelligence stored in all those PC hard drives across your company.
DeepREM from John Liu
]]> 1807 1 https://cdn.slidesharecdn.com/ss_thumbnails/deepremnhw-181117150615-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Social Network Analysis for Healthcare /slideshow/social-network-analysis-for-healthcare/121798350 socialnetworkanalysisforhealthcare-181104142935
Intro to advanced graph analytics and two novel applications in health care. Presented at the 2018 SOA Annual Meeting.]]>
Intro to advanced graph analytics and two novel applications in health care. Presented at the 2018 SOA Annual Meeting.]]> Sun, 04 Nov 2018 14:29:35 GMT /slideshow/social-network-analysis-for-healthcare/121798350 guard0g@slideshare.net(guard0g) Social Network Analysis for Healthcare guard0g Intro to advanced graph analytics and two novel applications in health care. Presented at the 2018 SOA Annual Meeting. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/socialnetworkanalysisforhealthcare-181104142935-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Intro to advanced graph analytics and two novel applications in health care. Presented at the 2018 SOA Annual Meeting.
Social Network Analysis for Healthcare from John Liu
]]> 301 1 https://cdn.slidesharecdn.com/ss_thumbnails/socialnetworkanalysisforhealthcare-181104142935-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Healthy Competition: How Adversarial Reasoning is Leading the Next Wave of Innovation /guard0g/healthy-competition-how-adversarial-reasoning-is-leading-the-next-wave-of-innovation ntcanalyticssummit2017presentationfinal-170809033357
In recent years, machine learning and reinforcement learning algorithms have revolutionized how we tackle problems in pattern recognition, inference and prediction. These learning algorithms are inherently stochastic in nature and collaborative by design. While powerful, they often lead to models that exhibit fragility in noisy real-world domains. A new generation of learning algorithms are evolving to augment robustness by embracing adversarial reasoning. In place of cooperative learning, these algorithms espouse game theoretic concepts of competition, deception, and Nash equilibria. In this talk, John will examine the role of adversarial reasoning in problem solving. Attendees will learn about the principles underpinning adversarial reasoning and their relevance to the new generation of machine learning algorithms including actor-critic A3C methods, generative adversarial networks, and variational autoencoders. In the end, the objective of this talk is to provide an intuitive understanding of the coming learning algorithms that can surmise intent, detect and practice deception, and formulate long-range winning strategies to real world problems. ]]>
In recent years, machine learning and reinforcement learning algorithms have revolutionized how we tackle problems in pattern recognition, inference and prediction. These learning algorithms are inherently stochastic in nature and collaborative by design. While powerful, they often lead to models that exhibit fragility in noisy real-world domains. A new generation of learning algorithms are evolving to augment robustness by embracing adversarial reasoning. In place of cooperative learning, these algorithms espouse game theoretic concepts of competition, deception, and Nash equilibria. In this talk, John will examine the role of adversarial reasoning in problem solving. Attendees will learn about the principles underpinning adversarial reasoning and their relevance to the new generation of machine learning algorithms including actor-critic A3C methods, generative adversarial networks, and variational autoencoders. In the end, the objective of this talk is to provide an intuitive understanding of the coming learning algorithms that can surmise intent, detect and practice deception, and formulate long-range winning strategies to real world problems. ]]> Wed, 09 Aug 2017 03:33:56 GMT /guard0g/healthy-competition-how-adversarial-reasoning-is-leading-the-next-wave-of-innovation guard0g@slideshare.net(guard0g) Healthy Competition: How Adversarial Reasoning is Leading the Next Wave of Innovation guard0g In recent years, machine learning and reinforcement learning algorithms have revolutionized how we tackle problems in pattern recognition, inference and prediction. These learning algorithms are inherently stochastic in nature and collaborative by design. While powerful, they often lead to models that exhibit fragility in noisy real-world domains. A new generation of learning algorithms are evolving to augment robustness by embracing adversarial reasoning. In place of cooperative learning, these algorithms espouse game theoretic concepts of competition, deception, and Nash equilibria. In this talk, John will examine the role of adversarial reasoning in problem solving. Attendees will learn about the principles underpinning adversarial reasoning and their relevance to the new generation of machine learning algorithms including actor-critic A3C methods, generative adversarial networks, and variational autoencoders. In the end, the objective of this talk is to provide an intuitive understanding of the coming learning algorithms that can surmise intent, detect and practice deception, and formulate long-range winning strategies to real world problems. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ntcanalyticssummit2017presentationfinal-170809033357-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In recent years, machine learning and reinforcement learning algorithms have revolutionized how we tackle problems in pattern recognition, inference and prediction. These learning algorithms are inherently stochastic in nature and collaborative by design. While powerful, they often lead to models that exhibit fragility in noisy real-world domains. A new generation of learning algorithms are evolving to augment robustness by embracing adversarial reasoning. In place of cooperative learning, these algorithms espouse game theoretic concepts of competition, deception, and Nash equilibria. In this talk, John will examine the role of adversarial reasoning in problem solving. Attendees will learn about the principles underpinning adversarial reasoning and their relevance to the new generation of machine learning algorithms including actor-critic A3C methods, generative adversarial networks, and variational autoencoders. In the end, the objective of this talk is to provide an intuitive understanding of the coming learning algorithms that can surmise intent, detect and practice deception, and formulate long-range winning strategies to real world problems.
Healthy Competition: How Adversarial Reasoning is Leading the Next Wave of Innovation from John Liu
]]> 849 3 https://cdn.slidesharecdn.com/ss_thumbnails/ntcanalyticssummit2017presentationfinal-170809033357-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Sentiment-Driven Financial Intelligence /slideshow/sentimentdriven-financial-intelligence/71942860 johnliusentimentsymposiumpresentation-170209041017
Financial markets are driven by fundamentals and sentiment. While investment technologies that leverage fundamental analysis have matured, few exist that effectively incorporate sentiment analysis. At the same time, the availability of financial sentiment data in structured and unstructured form has grown exponentially. In this talk, John will discuss current solutions and ongoing research in the application of sentiment-driven financial intelligence for investment management and trading strategies.]]>
Financial markets are driven by fundamentals and sentiment. While investment technologies that leverage fundamental analysis have matured, few exist that effectively incorporate sentiment analysis. At the same time, the availability of financial sentiment data in structured and unstructured form has grown exponentially. In this talk, John will discuss current solutions and ongoing research in the application of sentiment-driven financial intelligence for investment management and trading strategies.]]> Thu, 09 Feb 2017 04:10:16 GMT /slideshow/sentimentdriven-financial-intelligence/71942860 guard0g@slideshare.net(guard0g) Sentiment-Driven Financial Intelligence guard0g Financial markets are driven by fundamentals and sentiment. While investment technologies that leverage fundamental analysis have matured, few exist that effectively incorporate sentiment analysis. At the same time, the availability of financial sentiment data in structured and unstructured form has grown exponentially. In this talk, John will discuss current solutions and ongoing research in the application of sentiment-driven financial intelligence for investment management and trading strategies. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/johnliusentimentsymposiumpresentation-170209041017-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Financial markets are driven by fundamentals and sentiment. While investment technologies that leverage fundamental analysis have matured, few exist that effectively incorporate sentiment analysis. At the same time, the availability of financial sentiment data in structured and unstructured form has grown exponentially. In this talk, John will discuss current solutions and ongoing research in the application of sentiment-driven financial intelligence for investment management and trading strategies.
Sentiment-Driven Financial Intelligence from John Liu
]]> 411 5 https://cdn.slidesharecdn.com/ss_thumbnails/johnliusentimentsymposiumpresentation-170209041017-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 A Way Forward /guard0g/2017-data-monetization-workshop 2017datamonetizationkeynote-170202013316
"A Way Forward" for corporations to leverage data, understand value & change the conversation, presented at the 2017 Data Monetization Workshop.]]>
"A Way Forward" for corporations to leverage data, understand value & change the conversation, presented at the 2017 Data Monetization Workshop.]]> Thu, 02 Feb 2017 01:33:16 GMT /guard0g/2017-data-monetization-workshop guard0g@slideshare.net(guard0g) A Way Forward guard0g "A Way Forward" for corporations to leverage data, understand value & change the conversation, presented at the 2017 Data Monetization Workshop. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2017datamonetizationkeynote-170202013316-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> &quot;A Way Forward&quot; for corporations to leverage data, understand value &amp; change the conversation, presented at the 2017 Data Monetization Workshop.
A Way Forward from John Liu
]]> 263 4 https://cdn.slidesharecdn.com/ss_thumbnails/2017datamonetizationkeynote-170202013316-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 I2P and the Dark Web /guard0g/i2p-and-the-dark-web-68248739 phreaknic2016presentation-161106012401
The Invisible Internet Project (I2P) is a fully decentralized, self-organizing network layer that provides secure and anonymous communications. As an emerging darknet, I2P addresses much of the surveillance dragnet concerns and flaws of Tor. With a growing list of supported applications (including integration with blockchain crypto-platforms), I2P is poised for mainstream adoption.]]>
The Invisible Internet Project (I2P) is a fully decentralized, self-organizing network layer that provides secure and anonymous communications. As an emerging darknet, I2P addresses much of the surveillance dragnet concerns and flaws of Tor. With a growing list of supported applications (including integration with blockchain crypto-platforms), I2P is poised for mainstream adoption.]]> Sun, 06 Nov 2016 01:24:01 GMT /guard0g/i2p-and-the-dark-web-68248739 guard0g@slideshare.net(guard0g) I2P and the Dark Web guard0g The Invisible Internet Project (I2P) is a fully decentralized, self-organizing network layer that provides secure and anonymous communications. As an emerging darknet, I2P addresses much of the surveillance dragnet concerns and flaws of Tor. With a growing list of supported applications (including integration with blockchain crypto-platforms), I2P is poised for mainstream adoption. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/phreaknic2016presentation-161106012401-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The Invisible Internet Project (I2P) is a fully decentralized, self-organizing network layer that provides secure and anonymous communications. As an emerging darknet, I2P addresses much of the surveillance dragnet concerns and flaws of Tor. With a growing list of supported applications (including integration with blockchain crypto-platforms), I2P is poised for mainstream adoption.
I2P and the Dark Web from John Liu
]]> 1425 6 https://cdn.slidesharecdn.com/ss_thumbnails/phreaknic2016presentation-161106012401-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Beyond Machine Learning: The New Generation of Learning Algorithms Coming to Market /slideshow/beyond-machine-learning-the-new-generation-of-learning-algorithms-coming-to-market/64954876 ntcanalyticssummit2016presentation-160813005405
Machine learning algorithms can be extremely powerful in pattern recognition and prediction, but they suffer from several well-known deficiencies in practice and scope. This has led to the evolution of new learning algorithms that unite concepts from cognitive neuroscience, educational psychology, and artificial intelligence. Examples of these algorithms are seen in self-driving cars, Go-playing computers, and conversational dialogue agents. The fundamental precepts and practical limitations of machine learning and deep learning, along with the principles behind active learning, transfer learning, reinforcement learning, apprenticeship learning, multi-task learning, meta-learning, and lifelong learning will be examined along with some examples. Ultimately, this talk aims to spark interest in new learning algorithms that fundamentally challenge us to broaden our notion of machine learning and its capabilities.]]>
Machine learning algorithms can be extremely powerful in pattern recognition and prediction, but they suffer from several well-known deficiencies in practice and scope. This has led to the evolution of new learning algorithms that unite concepts from cognitive neuroscience, educational psychology, and artificial intelligence. Examples of these algorithms are seen in self-driving cars, Go-playing computers, and conversational dialogue agents. The fundamental precepts and practical limitations of machine learning and deep learning, along with the principles behind active learning, transfer learning, reinforcement learning, apprenticeship learning, multi-task learning, meta-learning, and lifelong learning will be examined along with some examples. Ultimately, this talk aims to spark interest in new learning algorithms that fundamentally challenge us to broaden our notion of machine learning and its capabilities.]]> Sat, 13 Aug 2016 00:54:05 GMT /slideshow/beyond-machine-learning-the-new-generation-of-learning-algorithms-coming-to-market/64954876 guard0g@slideshare.net(guard0g) Beyond Machine Learning: The New Generation of Learning Algorithms Coming to Market guard0g Machine learning algorithms can be extremely powerful in pattern recognition and prediction, but they suffer from several well-known deficiencies in practice and scope. This has led to the evolution of new learning algorithms that unite concepts from cognitive neuroscience, educational psychology, and artificial intelligence. Examples of these algorithms are seen in self-driving cars, Go-playing computers, and conversational dialogue agents. The fundamental precepts and practical limitations of machine learning and deep learning, along with the principles behind active learning, transfer learning, reinforcement learning, apprenticeship learning, multi-task learning, meta-learning, and lifelong learning will be examined along with some examples. Ultimately, this talk aims to spark interest in new learning algorithms that fundamentally challenge us to broaden our notion of machine learning and its capabilities. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ntcanalyticssummit2016presentation-160813005405-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Machine learning algorithms can be extremely powerful in pattern recognition and prediction, but they suffer from several well-known deficiencies in practice and scope. This has led to the evolution of new learning algorithms that unite concepts from cognitive neuroscience, educational psychology, and artificial intelligence. Examples of these algorithms are seen in self-driving cars, Go-playing computers, and conversational dialogue agents. The fundamental precepts and practical limitations of machine learning and deep learning, along with the principles behind active learning, transfer learning, reinforcement learning, apprenticeship learning, multi-task learning, meta-learning, and lifelong learning will be examined along with some examples. Ultimately, this talk aims to spark interest in new learning algorithms that fundamentally challenge us to broaden our notion of machine learning and its capabilities.
Beyond Machine Learning: The New Generation of Learning Algorithms Coming to Market from John Liu
]]> 939 5 https://cdn.slidesharecdn.com/ss_thumbnails/ntcanalyticssummit2016presentation-160813005405-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Behavioral Analytics for Financial Intelligence /slideshow/behavioral-analytics-for-financial-intelligence/64954828 johnliuntcanalyticssummitfinal-160813004834
Predictive analytics and machine learning has led to new methods for modeling human behavior and cognition. These methods are collectively known as behavioral analytics, focusing on how and why individuals and groups take actions and respond to them. This presentation discusses how financial services organizations are learning to leverage behavioral analytics for a broad set of applications that span customer insight, fraud detection, compliance, and market/investment intelligence.]]>
Predictive analytics and machine learning has led to new methods for modeling human behavior and cognition. These methods are collectively known as behavioral analytics, focusing on how and why individuals and groups take actions and respond to them. This presentation discusses how financial services organizations are learning to leverage behavioral analytics for a broad set of applications that span customer insight, fraud detection, compliance, and market/investment intelligence.]]> Sat, 13 Aug 2016 00:48:34 GMT /slideshow/behavioral-analytics-for-financial-intelligence/64954828 guard0g@slideshare.net(guard0g) Behavioral Analytics for Financial Intelligence guard0g Predictive analytics and machine learning has led to new methods for modeling human behavior and cognition. These methods are collectively known as behavioral analytics, focusing on how and why individuals and groups take actions and respond to them. This presentation discusses how financial services organizations are learning to leverage behavioral analytics for a broad set of applications that span customer insight, fraud detection, compliance, and market/investment intelligence. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/johnliuntcanalyticssummitfinal-160813004834-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Predictive analytics and machine learning has led to new methods for modeling human behavior and cognition. These methods are collectively known as behavioral analytics, focusing on how and why individuals and groups take actions and respond to them. This presentation discusses how financial services organizations are learning to leverage behavioral analytics for a broad set of applications that span customer insight, fraud detection, compliance, and market/investment intelligence.
Behavioral Analytics for Financial Intelligence from John Liu
]]> 2443 5 https://cdn.slidesharecdn.com/ss_thumbnails/johnliuntcanalyticssummitfinal-160813004834-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Naive Bayes for the Superbowl /guard0g/naive-bayes-for-superbowl naivebayesforsuperbowl-150127211351-conversion-gate01
Nashville Machine Learning Meetup 2015 Kickoff, part 1 of our Foundations of Supervised Machine Learning series.]]>
Nashville Machine Learning Meetup 2015 Kickoff, part 1 of our Foundations of Supervised Machine Learning series.]]> Tue, 27 Jan 2015 21:13:51 GMT /guard0g/naive-bayes-for-superbowl guard0g@slideshare.net(guard0g) Naive Bayes for the Superbowl guard0g Nashville Machine Learning Meetup 2015 Kickoff, part 1 of our Foundations of Supervised Machine Learning series. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/naivebayesforsuperbowl-150127211351-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Nashville Machine Learning Meetup 2015 Kickoff, part 1 of our Foundations of Supervised Machine Learning series.
Naive Bayes for the Superbowl from John Liu
]]> 1550 11 https://cdn.slidesharecdn.com/ss_thumbnails/naivebayesforsuperbowl-150127211351-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Neural Networks in the Wild: Handwriting Recognition /slideshow/neural-networks-in-the-wild-handwriting-recognition/40842632 neuralnetworksinthewild-141028173746-conversion-gate01
Demonstration of linear and neural network classification methods for the problem of offline handwriting recognition using the NIST SD19 Dataset. Tutorial on building neural networks in Pylearn2 without YAML. iPython notebook located at nbviewer.ipython.org/github/guard0g/HandwritingRecognition/tree/master/Handwriting%20Recognition%20Workbook.ipynb]]>
Demonstration of linear and neural network classification methods for the problem of offline handwriting recognition using the NIST SD19 Dataset. Tutorial on building neural networks in Pylearn2 without YAML. iPython notebook located at nbviewer.ipython.org/github/guard0g/HandwritingRecognition/tree/master/Handwriting%20Recognition%20Workbook.ipynb]]> Tue, 28 Oct 2014 17:37:46 GMT /slideshow/neural-networks-in-the-wild-handwriting-recognition/40842632 guard0g@slideshare.net(guard0g) Neural Networks in the Wild: Handwriting Recognition guard0g Demonstration of linear and neural network classification methods for the problem of offline handwriting recognition using the NIST SD19 Dataset. Tutorial on building neural networks in Pylearn2 without YAML. iPython notebook located at nbviewer.ipython.org/github/guard0g/HandwritingRecognition/tree/master/Handwriting%20Recognition%20Workbook.ipynb <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/neuralnetworksinthewild-141028173746-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Demonstration of linear and neural network classification methods for the problem of offline handwriting recognition using the NIST SD19 Dataset. Tutorial on building neural networks in Pylearn2 without YAML. iPython notebook located at nbviewer.ipython.org/github/guard0g/HandwritingRecognition/tree/master/Handwriting%20Recognition%20Workbook.ipynb
Neural Networks in the Wild: Handwriting Recognition from John Liu
]]> 11617 13 https://cdn.slidesharecdn.com/ss_thumbnails/neuralnetworksinthewild-141028173746-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Role of Data Science in ERM @ Nashville Analytics Summit Sep 2014 /slideshow/nashville-analytics-summit/40842554 lipscombdssummit20140910v3-141028173426-conversion-gate01
An overview of how organizations can leverage data science and predictive analytics to improve enterprise risk management. Applications for risk identification, mitigation and management will be discussed, as well as methods to facilitate strategic integration across an organization.]]>
An overview of how organizations can leverage data science and predictive analytics to improve enterprise risk management. Applications for risk identification, mitigation and management will be discussed, as well as methods to facilitate strategic integration across an organization.]]> Tue, 28 Oct 2014 17:34:24 GMT /slideshow/nashville-analytics-summit/40842554 guard0g@slideshare.net(guard0g) Role of Data Science in ERM @ Nashville Analytics Summit Sep 2014 guard0g An overview of how organizations can leverage data science and predictive analytics to improve enterprise risk management. Applications for risk identification, mitigation and management will be discussed, as well as methods to facilitate strategic integration across an organization. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lipscombdssummit20140910v3-141028173426-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> An overview of how organizations can leverage data science and predictive analytics to improve enterprise risk management. Applications for risk identification, mitigation and management will be discussed, as well as methods to facilitate strategic integration across an organization.
Role of Data Science in ERM @ Nashville Analytics Summit Sep 2014 from John Liu
]]> 966 3 https://cdn.slidesharecdn.com/ss_thumbnails/lipscombdssummit20140910v3-141028173426-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-guard0g-48x48.jpg?cb=1639587727 Enriching human intelligence through NLP and applied machine learning. CFA charterholder with a B.S., M.S. and Ph.D. from the University of Pennsylvania. 2016 Nashville Data Scientist of the Year. Finalist, 2018 Nashville Community Leader of the Year. https://cdn.slidesharecdn.com/ss_thumbnails/k8s2019meetupkubeflow-191010021938-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/kubeflow-and-data-science-in-kubernetes/180543017 Kubeflow and Data Scie... https://cdn.slidesharecdn.com/ss_thumbnails/nas2019presentation-190910231251-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/artificial-intelligence-as-a-service-170695360/170695360 Artificial Intelligenc... https://cdn.slidesharecdn.com/ss_thumbnails/nashvillemeetupjune182019-190619155218-thumbnail.jpg?width=320&height=320&fit=bounds guard0g/machine-learning-with-small-data-150646219 Machine Learning with ...