�ݺ�ߣshows by User: formulatedby

�ݺ�ߣshows by User: formulatedby / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: formulatedby / Fri, 15 Mar 2019 18:47:29 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: formulatedby Data Science Salon: An Experiment on Data Science Algorithms Enabled by a Pilosa Index /slideshow/an-experiment-on-data-science-algorithms-enabled-by-a-pilosa-index/136616633 dsspilosatalkv51-190315184729
Pilosa, as a technology, changes the dialog around large data sets, both static and in motion. Historically data lakes like Hadoop have been used to store massive amounts of data. However, it is estimated that only 20% of that data is practically analyzable because complex analytical operations on an ad-hoc basis become computationally painful and slow. Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ Enter a distributed binary index: Pilosa. While this can be used to unlock and join massive datasets and streams, it can also be thought of as an accelerator for training Machine Learning models and most importantly running your algorithms in large scale production environments. In this workshop Hypergiant will discuss how Pilosa interacts with several ML ideas including the Winnow algorithm, association schemes, and recommendation engines.]]>
Pilosa, as a technology, changes the dialog around large data sets, both static and in motion. Historically data lakes like Hadoop have been used to store massive amounts of data. However, it is estimated that only 20% of that data is practically analyzable because complex analytical operations on an ad-hoc basis become computationally painful and slow. Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ Enter a distributed binary index: Pilosa. While this can be used to unlock and join massive datasets and streams, it can also be thought of as an accelerator for training Machine Learning models and most importantly running your algorithms in large scale production environments. In this workshop Hypergiant will discuss how Pilosa interacts with several ML ideas including the Winnow algorithm, association schemes, and recommendation engines.]]> Fri, 15 Mar 2019 18:47:29 GMT /slideshow/an-experiment-on-data-science-algorithms-enabled-by-a-pilosa-index/136616633 formulatedby@slideshare.net(formulatedby) Data Science Salon: An Experiment on Data Science Algorithms Enabled by a Pilosa Index formulatedby Pilosa, as a technology, changes the dialog around large data sets, both static and in motion. Historically data lakes like Hadoop have been used to store massive amounts of data. However, it is estimated that only 20% of that data is practically analyzable because complex analytical operations on an ad-hoc basis become computationally painful and slow. Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ Enter a distributed binary index: Pilosa. While this can be used to unlock and join massive datasets and streams, it can also be thought of as an accelerator for training Machine Learning models and most importantly running your algorithms in large scale production environments. In this workshop Hypergiant will discuss how Pilosa interacts with several ML ideas including the Winnow algorithm, association schemes, and recommendation engines. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dsspilosatalkv51-190315184729-thumbnail.jpg?width=120&height=120&fit=bounds" /> Pilosa, as a technology, changes the dialog around large data sets, both static and in motion. Historically data lakes like Hadoop have been used to store massive amounts of data. However, it is estimated that only 20% of that data is practically analyzable because complex analytical operations on an ad-hoc basis become computationally painful and slow. Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ Enter a distributed binary index: Pilosa. While this can be used to unlock and join massive datasets and streams, it can also be thought of as an accelerator for training Machine Learning models and most importantly running your algorithms in large scale production environments. In this workshop Hypergiant will discuss how Pilosa interacts with several ML ideas including the Winnow algorithm, association schemes, and recommendation engines.

Data Science Salon: An Experiment on Data Science Algorithms Enabled by a Pilosa Index from Formulatedby

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0 Data Science Salon: Are you sure you're an ethical technologist?: Build your ethical imagination /slideshow/are-you-sure-youre-an-ethical-technologist-build-your-ethical-imagination/136616343 are-you-sure-youre-an-ethical-technologist-190315184536
This 60-minute workshop reveals our hidden attribution biases and equips attendees with an ethical imagination to build better technologies, companies, and communities. As an Ethics for Data Science instructor at NYU, I was struck by the disjunction between students’ excellent ability to find ethical gaps in other peoples’ projects and the blind spots they exhibited when critiquing their own work. Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ In this interactive workshop, we will identify how to reduce our own good intention biases. ]]>
This 60-minute workshop reveals our hidden attribution biases and equips attendees with an ethical imagination to build better technologies, companies, and communities. As an Ethics for Data Science instructor at NYU, I was struck by the disjunction between students’ excellent ability to find ethical gaps in other peoples’ projects and the blind spots they exhibited when critiquing their own work. Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ In this interactive workshop, we will identify how to reduce our own good intention biases. ]]> Fri, 15 Mar 2019 18:45:36 GMT /slideshow/are-you-sure-youre-an-ethical-technologist-build-your-ethical-imagination/136616343 formulatedby@slideshare.net(formulatedby) Data Science Salon: Are you sure you're an ethical technologist?: Build your ethical imagination formulatedby This 60-minute workshop reveals our hidden attribution biases and equips attendees with an ethical imagination to build better technologies, companies, and communities. As an Ethics for Data Science instructor at NYU, I was struck by the disjunction between students’ excellent ability to find ethical gaps in other peoples’ projects and the blind spots they exhibited when critiquing their own work. Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ In this interactive workshop, we will identify how to reduce our own good intention biases. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/are-you-sure-youre-an-ethical-technologist-190315184536-thumbnail.jpg?width=120&height=120&fit=bounds" /> This 60-minute workshop reveals our hidden attribution biases and equips attendees with an ethical imagination to build better technologies, companies, and communities. As an Ethics for Data Science instructor at NYU, I was struck by the disjunction between students’ excellent ability to find ethical gaps in other peoples’ projects and the blind spots they exhibited when critiquing their own work. Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ In this interactive workshop, we will identify how to reduce our own good intention biases.

Data Science Salon: Are you sure you're an ethical technologist?: Build your ethical imagination from Formulatedby

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0 Data Science Salon: In your own words: computing customer similarity from text data /slideshow/in-your-own-words-computing-customer-similarity-from-text-data/136614257 inyourownwordscomputingcustomersimilarityfromtextdata-190315183156
In order to attract customers, companies aim to provide an experience that is personalized to their audience. Typically this involves assigning customers to certain “segments” based on their characteristics and creating a personalized experience for each segment. Often the mostly widely available data sources for customers tend to be unstructured text (e.g. social media profiles, websites). Since reading each document is not a scalable approach, practitioners use a set of techniques for extracting information from text loosely referred to as Natural Language Processing (NLP). Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ In this workshop, we will walk through an NLP use-case computing similarity from business website text in Python. We will explore the use of the spaCy and scikit-learn libraries to preprocess and tokenize page text. We will then walk through methods for turning tokenized text into page-level numeric vectors (i.e. TF-IDF weighting and word vectors). Using these vectors, we will make use of several matrix factorization techniques to distill the vectors into a smaller set of features. These steps will familiarize participants with some common steps in NLP pipelines. After each of the above steps, we will explore how the information extracted can be used for customer segmentation based on a similarity measure. Specifically, we will focusing on how each stage affects the similarity between pages. Through this exploration, participants will gain an understanding of how different processing may be appropriate for different NLP use-cases. For example, fitting topic models to document vectors is most useful when there is likely to be a distinct set of topics among the document set. The workshop will conclude with a discussion about how these techniques are currently used in production at ThriveHive. This will provide participants with an example of how they might be able to make use of what we explored in their own work. Any additional time will be devoted to discussing advanced techniques in NLP such as text autoencoders for computing context-sensitive similarity between documents.]]>
In order to attract customers, companies aim to provide an experience that is personalized to their audience. Typically this involves assigning customers to certain “segments” based on their characteristics and creating a personalized experience for each segment. Often the mostly widely available data sources for customers tend to be unstructured text (e.g. social media profiles, websites). Since reading each document is not a scalable approach, practitioners use a set of techniques for extracting information from text loosely referred to as Natural Language Processing (NLP). Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ In this workshop, we will walk through an NLP use-case computing similarity from business website text in Python. We will explore the use of the spaCy and scikit-learn libraries to preprocess and tokenize page text. We will then walk through methods for turning tokenized text into page-level numeric vectors (i.e. TF-IDF weighting and word vectors). Using these vectors, we will make use of several matrix factorization techniques to distill the vectors into a smaller set of features. These steps will familiarize participants with some common steps in NLP pipelines. After each of the above steps, we will explore how the information extracted can be used for customer segmentation based on a similarity measure. Specifically, we will focusing on how each stage affects the similarity between pages. Through this exploration, participants will gain an understanding of how different processing may be appropriate for different NLP use-cases. For example, fitting topic models to document vectors is most useful when there is likely to be a distinct set of topics among the document set. The workshop will conclude with a discussion about how these techniques are currently used in production at ThriveHive. This will provide participants with an example of how they might be able to make use of what we explored in their own work. Any additional time will be devoted to discussing advanced techniques in NLP such as text autoencoders for computing context-sensitive similarity between documents.]]> Fri, 15 Mar 2019 18:31:56 GMT /slideshow/in-your-own-words-computing-customer-similarity-from-text-data/136614257 formulatedby@slideshare.net(formulatedby) Data Science Salon: In your own words: computing customer similarity from text data formulatedby In order to attract customers, companies aim to provide an experience that is personalized to their audience. Typically this involves assigning customers to certain “segments” based on their characteristics and creating a personalized experience for each segment. Often the mostly widely available data sources for customers tend to be unstructured text (e.g. social media profiles, websites). Since reading each document is not a scalable approach, practitioners use a set of techniques for extracting information from text loosely referred to as Natural Language Processing (NLP). Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ In this workshop, we will walk through an NLP use-case computing similarity from business website text in Python. We will explore the use of the spaCy and scikit-learn libraries to preprocess and tokenize page text. We will then walk through methods for turning tokenized text into page-level numeric vectors (i.e. TF-IDF weighting and word vectors). Using these vectors, we will make use of several matrix factorization techniques to distill the vectors into a smaller set of features. These steps will familiarize participants with some common steps in NLP pipelines. After each of the above steps, we will explore how the information extracted can be used for customer segmentation based on a similarity measure. Specifically, we will focusing on how each stage affects the similarity between pages. Through this exploration, participants will gain an understanding of how different processing may be appropriate for different NLP use-cases. For example, fitting topic models to document vectors is most useful when there is likely to be a distinct set of topics among the document set. The workshop will conclude with a discussion about how these techniques are currently used in production at ThriveHive. This will provide participants with an example of how they might be able to make use of what we explored in their own work. Any additional time will be devoted to discussing advanced techniques in NLP such as text autoencoders for computing context-sensitive similarity between documents. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/inyourownwordscomputingcustomersimilarityfromtextdata-190315183156-thumbnail.jpg?width=120&height=120&fit=bounds" /> In order to attract customers, companies aim to provide an experience that is personalized to their audience. Typically this involves assigning customers to certain “segments” based on their characteristics and creating a personalized experience for each segment. Often the mostly widely available data sources for customers tend to be unstructured text (e.g. social media profiles, websites). Since reading each document is not a scalable approach, practitioners use a set of techniques for extracting information from text loosely referred to as Natural Language Processing (NLP). Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ In this workshop, we will walk through an NLP use-case computing similarity from business website text in Python. We will explore the use of the spaCy and scikit-learn libraries to preprocess and tokenize page text. We will then walk through methods for turning tokenized text into page-level numeric vectors (i.e. TF-IDF weighting and word vectors). Using these vectors, we will make use of several matrix factorization techniques to distill the vectors into a smaller set of features. These steps will familiarize participants with some common steps in NLP pipelines. After each of the above steps, we will explore how the information extracted can be used for customer segmentation based on a similarity measure. Specifically, we will focusing on how each stage affects the similarity between pages. Through this exploration, participants will gain an understanding of how different processing may be appropriate for different NLP use-cases. For example, fitting topic models to document vectors is most useful when there is likely to be a distinct set of topics among the document set. The workshop will conclude with a discussion about how these techniques are currently used in production at ThriveHive. This will provide participants with an example of how they might be able to make use of what we explored in their own work. Any additional time will be devoted to discussing advanced techniques in NLP such as text autoencoders for computing context-sensitive similarity between documents.

Data Science Salon: In your own words: computing customer similarity from text data from Formulatedby

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0 Data Science Salon: nterpretable Predictive Models in the Healthcare Domain /slideshow/interpretable-predictive-models-in-the-healthcare-domain/135082671 sridharankamalakannan-190307180308
Predictive models are often used to identify individuals that will likely have escalating health severity in the future and accordingly deliver appropriate interventions. However, for the clinicians and care managers, these predictive models often act as a black-box at an individual level. The reason for this being, typically predictive models use combinations of complicated algorithms that makes it hard to explain the reason behind a predictive model score at an individual level. This talk will focus on model and feature agnostic methodologies and techniques that help uncover the drivers behind a prediction at a personal level in a healthcare setting. Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/]]>
Predictive models are often used to identify individuals that will likely have escalating health severity in the future and accordingly deliver appropriate interventions. However, for the clinicians and care managers, these predictive models often act as a black-box at an individual level. The reason for this being, typically predictive models use combinations of complicated algorithms that makes it hard to explain the reason behind a predictive model score at an individual level. This talk will focus on model and feature agnostic methodologies and techniques that help uncover the drivers behind a prediction at a personal level in a healthcare setting. Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/]]> Thu, 07 Mar 2019 18:03:07 GMT /slideshow/interpretable-predictive-models-in-the-healthcare-domain/135082671 formulatedby@slideshare.net(formulatedby) Data Science Salon: nterpretable Predictive Models in the Healthcare Domain formulatedby Predictive models are often used to identify individuals that will likely have escalating health severity in the future and accordingly deliver appropriate interventions. However, for the clinicians and care managers, these predictive models often act as a black-box at an individual level. The reason for this being, typically predictive models use combinations of complicated algorithms that makes it hard to explain the reason behind a predictive model score at an individual level. This talk will focus on model and feature agnostic methodologies and techniques that help uncover the drivers behind a prediction at a personal level in a healthcare setting. Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sridharankamalakannan-190307180308-thumbnail.jpg?width=120&height=120&fit=bounds" /> Predictive models are often used to identify individuals that will likely have escalating health severity in the future and accordingly deliver appropriate interventions. However, for the clinicians and care managers, these predictive models often act as a black-box at an individual level. The reason for this being, typically predictive models use combinations of complicated algorithms that makes it hard to explain the reason behind a predictive model score at an individual level. This talk will focus on model and feature agnostic methodologies and techniques that help uncover the drivers behind a prediction at a personal level in a healthcare setting. Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/

Data Science Salon: nterpretable Predictive Models in the Healthcare Domain from Formulatedby

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0 Data Science Salon: Applications of Embeddings and Deep Learning at Groupon /slideshow/applications-of-embeddings-and-deep-learning-at-groupon/134742106 bojanbabic-190305185726
Bojan Babick, Senior Software Engineer at Groupon talks about how the Groupon technical team went on a journey to switch from rule-based systems to classical machine learning models with hand-designed features to representation learning and deep learning See the related post here: https://roundtable.datascience.salon/applications-of-embeddings-and-deep-learning-at-groupon Sign up for DSSInsider to see the full video: https://insider.datascience.salon/ Next DSS SEA Event - https://datascience.salon/seattle/]]>
Bojan Babick, Senior Software Engineer at Groupon talks about how the Groupon technical team went on a journey to switch from rule-based systems to classical machine learning models with hand-designed features to representation learning and deep learning See the related post here: https://roundtable.datascience.salon/applications-of-embeddings-and-deep-learning-at-groupon Sign up for DSSInsider to see the full video: https://insider.datascience.salon/ Next DSS SEA Event - https://datascience.salon/seattle/]]> Tue, 05 Mar 2019 18:57:26 GMT /slideshow/applications-of-embeddings-and-deep-learning-at-groupon/134742106 formulatedby@slideshare.net(formulatedby) Data Science Salon: Applications of Embeddings and Deep Learning at Groupon formulatedby Bojan Babick, Senior Software Engineer at Groupon talks about how the Groupon technical team went on a journey to switch from rule-based systems to classical machine learning models with hand-designed features to representation learning and deep learning See the related post here: https://roundtable.datascience.salon/applications-of-embeddings-and-deep-learning-at-groupon Sign up for DSSInsider to see the full video: https://insider.datascience.salon/ Next DSS SEA Event - https://datascience.salon/seattle/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bojanbabic-190305185726-thumbnail.jpg?width=120&height=120&fit=bounds" /> Bojan Babick, Senior Software Engineer at Groupon talks about how the Groupon technical team went on a journey to switch from rule-based systems to classical machine learning models with hand-designed features to representation learning and deep learning See the related post here: https://roundtable.datascience.salon/applications-of-embeddings-and-deep-learning-at-groupon Sign up for DSSInsider to see the full video: https://insider.datascience.salon/ Next DSS SEA Event - https://datascience.salon/seattle/

Data Science Salon: Applications of Embeddings and Deep Learning at Groupon from Formulatedby

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0 Data Science Salon: Kaggle 1st Place in 30 minutes: Putting AutoML to Work with Enterprise Data /slideshow/kaggle-1st-place-in-30-minutes-putting-automl-to-work-with-enterprise-data/99423562 fireflydss-kaggle1st-180529204407
Presented by Hila Lamm, Chief Strategy Officer at Firefly.ai Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ With all the hype around auto machine learning for computer vision, businesses with structured data are left wondering: Is AutoML relevant for enterprise data? Can it alleviate the bottleneck that data science teams are experiencing? Our team was experimenting with different types of enterprise challenges -- from optimizing pricing to credit card fraud detection to retail banking customer behavior -- and was able to automatically build models that produced top-ranking Kaggle results within a few hours. In this session, through customer use cases and under the hood insights, you will learn about the capabilities of AutoML as applied on Firefly. Oh, and we’ll also talk about how we attained a Kaggle 1st place score in just half an hour.]]>
Presented by Hila Lamm, Chief Strategy Officer at Firefly.ai Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ With all the hype around auto machine learning for computer vision, businesses with structured data are left wondering: Is AutoML relevant for enterprise data? Can it alleviate the bottleneck that data science teams are experiencing? Our team was experimenting with different types of enterprise challenges -- from optimizing pricing to credit card fraud detection to retail banking customer behavior -- and was able to automatically build models that produced top-ranking Kaggle results within a few hours. In this session, through customer use cases and under the hood insights, you will learn about the capabilities of AutoML as applied on Firefly. Oh, and we’ll also talk about how we attained a Kaggle 1st place score in just half an hour.]]> Tue, 29 May 2018 20:44:07 GMT /slideshow/kaggle-1st-place-in-30-minutes-putting-automl-to-work-with-enterprise-data/99423562 formulatedby@slideshare.net(formulatedby) Data Science Salon: Kaggle 1st Place in 30 minutes: Putting AutoML to Work with Enterprise Data formulatedby Presented by Hila Lamm, Chief Strategy Officer at Firefly.ai Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ With all the hype around auto machine learning for computer vision, businesses with structured data are left wondering: Is AutoML relevant for enterprise data? Can it alleviate the bottleneck that data science teams are experiencing? Our team was experimenting with different types of enterprise challenges -- from optimizing pricing to credit card fraud detection to retail banking customer behavior -- and was able to automatically build models that produced top-ranking Kaggle results within a few hours. In this session, through customer use cases and under the hood insights, you will learn about the capabilities of AutoML as applied on Firefly. Oh, and we’ll also talk about how we attained a Kaggle 1st place score in just half an hour. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/fireflydss-kaggle1st-180529204407-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presented by Hila Lamm, Chief Strategy Officer at Firefly.ai Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ With all the hype around auto machine learning for computer vision, businesses with structured data are left wondering: Is AutoML relevant for enterprise data? Can it alleviate the bottleneck that data science teams are experiencing? Our team was experimenting with different types of enterprise challenges -- from optimizing pricing to credit card fraud detection to retail banking customer behavior -- and was able to automatically build models that produced top-ranking Kaggle results within a few hours. In this session, through customer use cases and under the hood insights, you will learn about the capabilities of AutoML as applied on Firefly. Oh, and we’ll also talk about how we attained a Kaggle 1st place score in just half an hour.

Data Science Salon: Kaggle 1st Place in 30 minutes: Putting AutoML to Work with Enterprise Data from Formulatedby

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0 Data Science Salon: Smart Cities /slideshow/smart-cities-99423036/99423036 skreddy-180529203929
Presented by SK Reddy, Chief Product Officer AI at Hexagon Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ Detecting indoor human activity is used for security, patient care, baby monitoring, etc. purposes. Other than having another human being providing the service (i.e. a security guard, a nurse, baby’s mother, etc.), many solutions have been suggested using image processing neural networks that detect patient’s fall, baby walking, door open, etc. Many of these models have achieved higher prediction accuracy rates. But neural networks that use video cameras bring up privacy concerns. Custom made sensors, though solve the problem, are expensive. Researchers have proposed deep learning (DL) models use wifi signals to detect human activity. This is relatively recent research. I would like to discuss on how to design a DL to detect human activity to use Wifi signals that are available from off-the-shelf wifi routers. I will also discuss the architecture of such models, share the implementation problems and evaluate solutions that may address these problems. ]]>
Presented by SK Reddy, Chief Product Officer AI at Hexagon Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ Detecting indoor human activity is used for security, patient care, baby monitoring, etc. purposes. Other than having another human being providing the service (i.e. a security guard, a nurse, baby’s mother, etc.), many solutions have been suggested using image processing neural networks that detect patient’s fall, baby walking, door open, etc. Many of these models have achieved higher prediction accuracy rates. But neural networks that use video cameras bring up privacy concerns. Custom made sensors, though solve the problem, are expensive. Researchers have proposed deep learning (DL) models use wifi signals to detect human activity. This is relatively recent research. I would like to discuss on how to design a DL to detect human activity to use Wifi signals that are available from off-the-shelf wifi routers. I will also discuss the architecture of such models, share the implementation problems and evaluate solutions that may address these problems. ]]> Tue, 29 May 2018 20:39:29 GMT /slideshow/smart-cities-99423036/99423036 formulatedby@slideshare.net(formulatedby) Data Science Salon: Smart Cities formulatedby Presented by SK Reddy, Chief Product Officer AI at Hexagon Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ Detecting indoor human activity is used for security, patient care, baby monitoring, etc. purposes. Other than having another human being providing the service (i.e. a security guard, a nurse, baby’s mother, etc.), many solutions have been suggested using image processing neural networks that detect patient’s fall, baby walking, door open, etc. Many of these models have achieved higher prediction accuracy rates. But neural networks that use video cameras bring up privacy concerns. Custom made sensors, though solve the problem, are expensive. Researchers have proposed deep learning (DL) models use wifi signals to detect human activity. This is relatively recent research. I would like to discuss on how to design a DL to detect human activity to use Wifi signals that are available from off-the-shelf wifi routers. I will also discuss the architecture of such models, share the implementation problems and evaluate solutions that may address these problems. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/skreddy-180529203929-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presented by SK Reddy, Chief Product Officer AI at Hexagon Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ Detecting indoor human activity is used for security, patient care, baby monitoring, etc. purposes. Other than having another human being providing the service (i.e. a security guard, a nurse, baby’s mother, etc.), many solutions have been suggested using image processing neural networks that detect patient’s fall, baby walking, door open, etc. Many of these models have achieved higher prediction accuracy rates. But neural networks that use video cameras bring up privacy concerns. Custom made sensors, though solve the problem, are expensive. Researchers have proposed deep learning (DL) models use wifi signals to detect human activity. This is relatively recent research. I would like to discuss on how to design a DL to detect human activity to use Wifi signals that are available from off-the-shelf wifi routers. I will also discuss the architecture of such models, share the implementation problems and evaluate solutions that may address these problems.

Data Science Salon: Smart Cities from Formulatedby

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0 Data Science Salon: Building a Data Driven Product Mindset /slideshow/building-a-data-driven-product-mindset/99420231 viswanathputtagunta-180529201540
Presented by Viswanath Puttagunta, Chief Technology Officer of Divergence.AI Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ OK, your Analysts and ETL developers pushed the limits of Tableau and traditional data warehouses. May be recently, you've even leveraged some of the Cloud services. You have a good idea of key metrics that are critical to your organization. But you definitely know there's a lot more data lurking within your organization that could be monetized. You look around and are overwhelmed with choices. You want a standard set of tools, but the tools are evolving at a dizzying pace, giving you a classic case of analysis paralysis. Even when you pick a tool, getting licenses and on-boarding seems to be taking forever! Come and learn how to build a ""Data Driven Product Mindset"" within your organization. We will discuss how to build a cross-functional team that has the right basics in AI/ML/Software/DevOps/Admin and can evolve as fast as the evolving tool-set, while providing actionable insights every step of the way. We will delve into the benefits of using Managed & Serverless services in Cloud to make your team nimbler than ever. That team you build will be the most formidable tool your organization will have.]]>
Presented by Viswanath Puttagunta, Chief Technology Officer of Divergence.AI Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ OK, your Analysts and ETL developers pushed the limits of Tableau and traditional data warehouses. May be recently, you've even leveraged some of the Cloud services. You have a good idea of key metrics that are critical to your organization. But you definitely know there's a lot more data lurking within your organization that could be monetized. You look around and are overwhelmed with choices. You want a standard set of tools, but the tools are evolving at a dizzying pace, giving you a classic case of analysis paralysis. Even when you pick a tool, getting licenses and on-boarding seems to be taking forever! Come and learn how to build a ""Data Driven Product Mindset"" within your organization. We will discuss how to build a cross-functional team that has the right basics in AI/ML/Software/DevOps/Admin and can evolve as fast as the evolving tool-set, while providing actionable insights every step of the way. We will delve into the benefits of using Managed & Serverless services in Cloud to make your team nimbler than ever. That team you build will be the most formidable tool your organization will have.]]> Tue, 29 May 2018 20:15:40 GMT /slideshow/building-a-data-driven-product-mindset/99420231 formulatedby@slideshare.net(formulatedby) Data Science Salon: Building a Data Driven Product Mindset formulatedby Presented by Viswanath Puttagunta, Chief Technology Officer of Divergence.AI Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ OK, your Analysts and ETL developers pushed the limits of Tableau and traditional data warehouses. May be recently, you've even leveraged some of the Cloud services. You have a good idea of key metrics that are critical to your organization. But you definitely know there's a lot more data lurking within your organization that could be monetized. You look around and are overwhelmed with choices. You want a standard set of tools, but the tools are evolving at a dizzying pace, giving you a classic case of analysis paralysis. Even when you pick a tool, getting licenses and on-boarding seems to be taking forever! Come and learn how to build a ""Data Driven Product Mindset"" within your organization. We will discuss how to build a cross-functional team that has the right basics in AI/ML/Software/DevOps/Admin and can evolve as fast as the evolving tool-set, while providing actionable insights every step of the way. We will delve into the benefits of using Managed & Serverless services in Cloud to make your team nimbler than ever. That team you build will be the most formidable tool your organization will have. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/viswanathputtagunta-180529201540-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presented by Viswanath Puttagunta, Chief Technology Officer of Divergence.AI Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ OK, your Analysts and ETL developers pushed the limits of Tableau and traditional data warehouses. May be recently, you've even leveraged some of the Cloud services. You have a good idea of key metrics that are critical to your organization. But you definitely know there's a lot more data lurking within your organization that could be monetized. You look around and are overwhelmed with choices. You want a standard set of tools, but the tools are evolving at a dizzying pace, giving you a classic case of analysis paralysis. Even when you pick a tool, getting licenses and on-boarding seems to be taking forever! Come and learn how to build a ""Data Driven Product Mindset"" within your organization. We will discuss how to build a cross-functional team that has the right basics in AI/ML/Software/DevOps/Admin and can evolve as fast as the evolving tool-set, while providing actionable insights every step of the way. We will delve into the benefits of using Managed & Serverless services in Cloud to make your team nimbler than ever. That team you build will be the most formidable tool your organization will have.

Data Science Salon: Building a Data Driven Product Mindset from Formulatedby

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0 Data Science Salon: Introduction to Machine Learning - Marketing Use Case /slideshow/introduction-to-machine-learning-marketing-use-case/88078280 datasciencesalonmiami-introtoml-180215232330
By Greg Werner Co-Founder at Cup of Data ]]>
By Greg Werner Co-Founder at Cup of Data ]]> Thu, 15 Feb 2018 23:23:30 GMT /slideshow/introduction-to-machine-learning-marketing-use-case/88078280 formulatedby@slideshare.net(formulatedby) Data Science Salon: Introduction to Machine Learning - Marketing Use Case formulatedby By Greg Werner Co-Founder at Cup of Data <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/datasciencesalonmiami-introtoml-180215232330-thumbnail.jpg?width=120&height=120&fit=bounds" /> By Greg Werner Co-Founder at Cup of Data

Data Science Salon: Introduction to Machine Learning - Marketing Use Case from Formulatedby

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0 Data Science Salon: Adopting Machine Learning to Drive Revenue and Market Share /slideshow/adopting-machine-learning-to-drive-revenue-and-market-share/87880292 davidfrigeri-180212204450
The race is on to gain strategic and proprietary insights into changes in customer preferences before your competitors. This workshop will cover how and why machine learning is the tool for marketers to drive revenue and increase market share. The adoption of machine learning does not happen overnight. We will discuss the Five Es of machine learning maturity – Educating, Exploring, Engaging, Executing and Expanding. Hear real-world examples of using machine learning to accelerate revenue, identify new customers and introduce new products based on machine learning capabilities. Next DSS MIA Event - https://datascience.salon/miami/]]>
The race is on to gain strategic and proprietary insights into changes in customer preferences before your competitors. This workshop will cover how and why machine learning is the tool for marketers to drive revenue and increase market share. The adoption of machine learning does not happen overnight. We will discuss the Five Es of machine learning maturity – Educating, Exploring, Engaging, Executing and Expanding. Hear real-world examples of using machine learning to accelerate revenue, identify new customers and introduce new products based on machine learning capabilities. Next DSS MIA Event - https://datascience.salon/miami/]]> Mon, 12 Feb 2018 20:44:50 GMT /slideshow/adopting-machine-learning-to-drive-revenue-and-market-share/87880292 formulatedby@slideshare.net(formulatedby) Data Science Salon: Adopting Machine Learning to Drive Revenue and Market Share formulatedby The race is on to gain strategic and proprietary insights into changes in customer preferences before your competitors. This workshop will cover how and why machine learning is the tool for marketers to drive revenue and increase market share. The adoption of machine learning does not happen overnight. We will discuss the Five Es of machine learning maturity – Educating, Exploring, Engaging, Executing and Expanding. Hear real-world examples of using machine learning to accelerate revenue, identify new customers and introduce new products based on machine learning capabilities. Next DSS MIA Event - https://datascience.salon/miami/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/davidfrigeri-180212204450-thumbnail.jpg?width=120&height=120&fit=bounds" /> The race is on to gain strategic and proprietary insights into changes in customer preferences before your competitors. This workshop will cover how and why machine learning is the tool for marketers to drive revenue and increase market share. The adoption of machine learning does not happen overnight. We will discuss the Five Es of machine learning maturity – Educating, Exploring, Engaging, Executing and Expanding. Hear real-world examples of using machine learning to accelerate revenue, identify new customers and introduce new products based on machine learning capabilities. Next DSS MIA Event - https://datascience.salon/miami/

Data Science Salon: Adopting Machine Learning to Drive Revenue and Market Share from Formulatedby

]]> 659 2 https://cdn.slidesharecdn.com/ss_thumbnails/davidfrigeri-180212204450-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Data Science Salon: Data visualization and Analysis in the Florida Panthers Hockey Organization /slideshow/data-visualization-and-analysis-in-the-florida-panthers-hockey-organization/87867953 brianmacdonald-180212175904
We will give an overview of how data visualization and data analysis are used within the Florida Panthers organization, around the National Hockey League, and in the sports industry in general, in a variety of different contexts. We discuss how analytics can be used to assist an NHL team’s front office, coaching staff, and scouting department. We also discuss the kinds of data we encounter on the business side of the organization in departments like sales and marketing, as well as the kinds of questions the league offices try to answer with the help of data. Next DSS MIA Event - https://datascience.salon/miami/]]>
We will give an overview of how data visualization and data analysis are used within the Florida Panthers organization, around the National Hockey League, and in the sports industry in general, in a variety of different contexts. We discuss how analytics can be used to assist an NHL team’s front office, coaching staff, and scouting department. We also discuss the kinds of data we encounter on the business side of the organization in departments like sales and marketing, as well as the kinds of questions the league offices try to answer with the help of data. Next DSS MIA Event - https://datascience.salon/miami/]]> Mon, 12 Feb 2018 17:59:04 GMT /slideshow/data-visualization-and-analysis-in-the-florida-panthers-hockey-organization/87867953 formulatedby@slideshare.net(formulatedby) Data Science Salon: Data visualization and Analysis in the Florida Panthers Hockey Organization formulatedby We will give an overview of how data visualization and data analysis are used within the Florida Panthers organization, around the National Hockey League, and in the sports industry in general, in a variety of different contexts. We discuss how analytics can be used to assist an NHL team’s front office, coaching staff, and scouting department. We also discuss the kinds of data we encounter on the business side of the organization in departments like sales and marketing, as well as the kinds of questions the league offices try to answer with the help of data. Next DSS MIA Event - https://datascience.salon/miami/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/brianmacdonald-180212175904-thumbnail.jpg?width=120&height=120&fit=bounds" /> We will give an overview of how data visualization and data analysis are used within the Florida Panthers organization, around the National Hockey League, and in the sports industry in general, in a variety of different contexts. We discuss how analytics can be used to assist an NHL team’s front office, coaching staff, and scouting department. We also discuss the kinds of data we encounter on the business side of the organization in departments like sales and marketing, as well as the kinds of questions the league offices try to answer with the help of data. Next DSS MIA Event - https://datascience.salon/miami/

Data Science Salon: Data visualization and Analysis in the Florida Panthers Hockey Organization from Formulatedby

]]> 729 13 https://cdn.slidesharecdn.com/ss_thumbnails/brianmacdonald-180212175904-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Data Science Salon: Machine Learning for Personalized Cancer Vaccines /slideshow/machine-learning-for-personalized-cancer-vaccines-87867944/87867944 alexrubinsteyn-180212175855
Presented by Alex Rubinsteyn Next DSS MIA Event - https://datascience.salon/miami/ A short introduction to cancer immunotherapy followed by several machine learning problems which arise from designing personalized cancer vaccines.]]>
Presented by Alex Rubinsteyn Next DSS MIA Event - https://datascience.salon/miami/ A short introduction to cancer immunotherapy followed by several machine learning problems which arise from designing personalized cancer vaccines.]]> Mon, 12 Feb 2018 17:58:55 GMT /slideshow/machine-learning-for-personalized-cancer-vaccines-87867944/87867944 formulatedby@slideshare.net(formulatedby) Data Science Salon: Machine Learning for Personalized Cancer Vaccines formulatedby Presented by Alex Rubinsteyn Next DSS MIA Event - https://datascience.salon/miami/ A short introduction to cancer immunotherapy followed by several machine learning problems which arise from designing personalized cancer vaccines. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/alexrubinsteyn-180212175855-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presented by Alex Rubinsteyn Next DSS MIA Event - https://datascience.salon/miami/ A short introduction to cancer immunotherapy followed by several machine learning problems which arise from designing personalized cancer vaccines.

Data Science Salon: Machine Learning for Personalized Cancer Vaccines from Formulatedby

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0 Data Science Salon: Building a Data Science Culture /slideshow/building-a-data-science-culture-87867927/87867927 catalinaarango-180212175845
Catalina is a Data Scientist with a specialty in building out scalable data solutions for startups. Next DSS MIA Event - https://datascience.salon/miami/ There's a huge hype around the power of data science across industries. However, not all companies have been able to successfully build out their data science capabilities, and some are just starting to think about doing so. Just as each business is unique, each data science endeavor is unique. In this talk, we explore both the non-negotiables in building a data science culture and how to tailor your data science initiatives to match your business needs at different stages of your journey towards reaping the benefits of a data science culture.]]>
Catalina is a Data Scientist with a specialty in building out scalable data solutions for startups. Next DSS MIA Event - https://datascience.salon/miami/ There's a huge hype around the power of data science across industries. However, not all companies have been able to successfully build out their data science capabilities, and some are just starting to think about doing so. Just as each business is unique, each data science endeavor is unique. In this talk, we explore both the non-negotiables in building a data science culture and how to tailor your data science initiatives to match your business needs at different stages of your journey towards reaping the benefits of a data science culture.]]> Mon, 12 Feb 2018 17:58:45 GMT /slideshow/building-a-data-science-culture-87867927/87867927 formulatedby@slideshare.net(formulatedby) Data Science Salon: Building a Data Science Culture formulatedby Catalina is a Data Scientist with a specialty in building out scalable data solutions for startups. Next DSS MIA Event - https://datascience.salon/miami/ There's a huge hype around the power of data science across industries. However, not all companies have been able to successfully build out their data science capabilities, and some are just starting to think about doing so. Just as each business is unique, each data science endeavor is unique. In this talk, we explore both the non-negotiables in building a data science culture and how to tailor your data science initiatives to match your business needs at different stages of your journey towards reaping the benefits of a data science culture. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/catalinaarango-180212175845-thumbnail.jpg?width=120&height=120&fit=bounds" /> Catalina is a Data Scientist with a specialty in building out scalable data solutions for startups. Next DSS MIA Event - https://datascience.salon/miami/ There's a huge hype around the power of data science across industries. However, not all companies have been able to successfully build out their data science capabilities, and some are just starting to think about doing so. Just as each business is unique, each data science endeavor is unique. In this talk, we explore both the non-negotiables in building a data science culture and how to tailor your data science initiatives to match your business needs at different stages of your journey towards reaping the benefits of a data science culture.

Data Science Salon: Building a Data Science Culture from Formulatedby

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0 Data Science Salon: Digital Transformation: The Data Science Catalyst /slideshow/digital-transformation-the-data-science-catalyst/87867923 marcfridson-180212175842
Carnival is the world's largest travel leisure company, with a combined fleet of over 100 vessels across 10 cruise line brands and growing. We analyze social channels (Facebook, Twitter, Instagram), web analytics and booking data to predict customer behavior and develop marketing strategies. This session will discuss the challenges of mining all of this data and some of the Machine Learning techniques we use to segment our customers (e.g. Clustering) and predicting the value of a customer (e.g. Regression). Next DSS MIA Event - https://datascience.salon/miami/ Presented by MANCHON (KEVIN) U Senior Director, Head of Marketing Analytics & Data Science at Carnival Cruise Line and MARC FRIDSON, former Principal Data Scientist at Carnival Cruise Line.]]>
Carnival is the world's largest travel leisure company, with a combined fleet of over 100 vessels across 10 cruise line brands and growing. We analyze social channels (Facebook, Twitter, Instagram), web analytics and booking data to predict customer behavior and develop marketing strategies. This session will discuss the challenges of mining all of this data and some of the Machine Learning techniques we use to segment our customers (e.g. Clustering) and predicting the value of a customer (e.g. Regression). Next DSS MIA Event - https://datascience.salon/miami/ Presented by MANCHON (KEVIN) U Senior Director, Head of Marketing Analytics & Data Science at Carnival Cruise Line and MARC FRIDSON, former Principal Data Scientist at Carnival Cruise Line.]]> Mon, 12 Feb 2018 17:58:42 GMT /slideshow/digital-transformation-the-data-science-catalyst/87867923 formulatedby@slideshare.net(formulatedby) Data Science Salon: Digital Transformation: The Data Science Catalyst formulatedby Carnival is the world's largest travel leisure company, with a combined fleet of over 100 vessels across 10 cruise line brands and growing. We analyze social channels (Facebook, Twitter, Instagram), web analytics and booking data to predict customer behavior and develop marketing strategies. This session will discuss the challenges of mining all of this data and some of the Machine Learning techniques we use to segment our customers (e.g. Clustering) and predicting the value of a customer (e.g. Regression). Next DSS MIA Event - https://datascience.salon/miami/ Presented by MANCHON (KEVIN) U Senior Director, Head of Marketing Analytics & Data Science at Carnival Cruise Line and MARC FRIDSON, former Principal Data Scientist at Carnival Cruise Line. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/marcfridson-180212175842-thumbnail.jpg?width=120&height=120&fit=bounds" /> Carnival is the world's largest travel leisure company, with a combined fleet of over 100 vessels across 10 cruise line brands and growing. We analyze social channels (Facebook, Twitter, Instagram), web analytics and booking data to predict customer behavior and develop marketing strategies. This session will discuss the challenges of mining all of this data and some of the Machine Learning techniques we use to segment our customers (e.g. Clustering) and predicting the value of a customer (e.g. Regression). Next DSS MIA Event - https://datascience.salon/miami/ Presented by MANCHON (KEVIN) U Senior Director, Head of Marketing Analytics & Data Science at Carnival Cruise Line and MARC FRIDSON, former Principal Data Scientist at Carnival Cruise Line.

Data Science Salon: Digital Transformation: The Data Science Catalyst from Formulatedby

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0 Data Science Salon: Quit Wasting Time – Case Studies in Production Machine Learning /formulatedby/quit-wasting-time-case-studies-in-production-machine-learning yashasvaidya-180212175832
Presented by Yashas Vaidya, Sr Data Scientist at DataIku Next DSS MIA Event - https://datascience.salon/miami/ The steps to taking a machine learning model to production. Modern architectures and technologies for building production machine learning. An overview of the talent and processes for creating and maintaining production machine learning.]]>
Presented by Yashas Vaidya, Sr Data Scientist at DataIku Next DSS MIA Event - https://datascience.salon/miami/ The steps to taking a machine learning model to production. Modern architectures and technologies for building production machine learning. An overview of the talent and processes for creating and maintaining production machine learning.]]> Mon, 12 Feb 2018 17:58:32 GMT /formulatedby/quit-wasting-time-case-studies-in-production-machine-learning formulatedby@slideshare.net(formulatedby) Data Science Salon: Quit Wasting Time – Case Studies in Production Machine Learning formulatedby Presented by Yashas Vaidya, Sr Data Scientist at DataIku Next DSS MIA Event - https://datascience.salon/miami/ The steps to taking a machine learning model to production. Modern architectures and technologies for building production machine learning. An overview of the talent and processes for creating and maintaining production machine learning. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/yashasvaidya-180212175832-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presented by Yashas Vaidya, Sr Data Scientist at DataIku Next DSS MIA Event - https://datascience.salon/miami/ The steps to taking a machine learning model to production. Modern architectures and technologies for building production machine learning. An overview of the talent and processes for creating and maintaining production machine learning.

Data Science Salon: Quit Wasting Time – Case Studies in Production Machine Learning from Formulatedby

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0 Data Science Salon: Enabling self-service predictive analytics at Bidtellect /slideshow/enabling-selfservice-predictive-analytics-at-bidtellect-a-case-study-87867854/87867854 michaelconway-180212175750
Having previously worked at both Millennial Media and AOL, Michael Conway brought his expertise to Bidtellect tasked with transforming the business to a self-service SaaS-based content distribution platform, enabling the company to grow 10-fold. Next DSS MIA Event - https://datascience.salon/miami/ During the 30-minute presentation, Michael will provide background information about Bidtellect and how data is an integral component of the business managing their premium native inventory across their supply ecosystem with over 5 billion native auctions per day. As Bidtellect embraces big data, Michael will share the challenges and successes he and his team have experienced along the way. In addition, Steve Sarsfield, Vertica Senior Product Marketing Manager, will be available to discuss how specific technologies (SQL, Python, R and embedded algorithms) can be combined in an MPP environment to achieve big data analytics success.]]>
Having previously worked at both Millennial Media and AOL, Michael Conway brought his expertise to Bidtellect tasked with transforming the business to a self-service SaaS-based content distribution platform, enabling the company to grow 10-fold. Next DSS MIA Event - https://datascience.salon/miami/ During the 30-minute presentation, Michael will provide background information about Bidtellect and how data is an integral component of the business managing their premium native inventory across their supply ecosystem with over 5 billion native auctions per day. As Bidtellect embraces big data, Michael will share the challenges and successes he and his team have experienced along the way. In addition, Steve Sarsfield, Vertica Senior Product Marketing Manager, will be available to discuss how specific technologies (SQL, Python, R and embedded algorithms) can be combined in an MPP environment to achieve big data analytics success.]]> Mon, 12 Feb 2018 17:57:50 GMT /slideshow/enabling-selfservice-predictive-analytics-at-bidtellect-a-case-study-87867854/87867854 formulatedby@slideshare.net(formulatedby) Data Science Salon: Enabling self-service predictive analytics at Bidtellect formulatedby Having previously worked at both Millennial Media and AOL, Michael Conway brought his expertise to Bidtellect tasked with transforming the business to a self-service SaaS-based content distribution platform, enabling the company to grow 10-fold. Next DSS MIA Event - https://datascience.salon/miami/ During the 30-minute presentation, Michael will provide background information about Bidtellect and how data is an integral component of the business managing their premium native inventory across their supply ecosystem with over 5 billion native auctions per day. As Bidtellect embraces big data, Michael will share the challenges and successes he and his team have experienced along the way. In addition, Steve Sarsfield, Vertica Senior Product Marketing Manager, will be available to discuss how specific technologies (SQL, Python, R and embedded algorithms) can be combined in an MPP environment to achieve big data analytics success. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/michaelconway-180212175750-thumbnail.jpg?width=120&height=120&fit=bounds" /> Having previously worked at both Millennial Media and AOL, Michael Conway brought his expertise to Bidtellect tasked with transforming the business to a self-service SaaS-based content distribution platform, enabling the company to grow 10-fold. Next DSS MIA Event - https://datascience.salon/miami/ During the 30-minute presentation, Michael will provide background information about Bidtellect and how data is an integral component of the business managing their premium native inventory across their supply ecosystem with over 5 billion native auctions per day. As Bidtellect embraces big data, Michael will share the challenges and successes he and his team have experienced along the way. In addition, Steve Sarsfield, Vertica Senior Product Marketing Manager, will be available to discuss how specific technologies (SQL, Python, R and embedded algorithms) can be combined in an MPP environment to achieve big data analytics success.

Data Science Salon: Enabling self-service predictive analytics at Bidtellect from Formulatedby

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0 Data Science Salon: MCL Clustering of Sparse Graphs /slideshow/mcl-clustering-of-sparse-graphs-87867848/87867848 anthanassioskintsakis-180212175746
The increasing need for clustering in several scientific domains has inevitably driven the creation of innovative algorithms, each designed to perform more efficiently in certain applications. More specifically, in many applications the data entities involved can be portrayed effectively by a graph as a collection of nodes and edges. One of the most established algorithms for graph clustering problems is the Markov Cluster Algorithm (MCL). Next DSS MIA Event - https://datascience.salon/miami/ When dealing with large and complex datasets, the underlying graphs can easily reach proportions that independent computing systems are inadequate to deal with. Additionally, the graphs encountered are typically sparse: the number of edges is far smaller than might be possible in a fully-connected graph. Consequently, there is a concrete need for algorithms that are designed to handle sparse graph clustering utilizing distributed computing resources. Our motivation was the development of a distributed architecture, able to accommodate large and sparse graphs, to actualize the MCL and R-MCL algorithm. The Apache Spark framework was chosen due to its ability to utilize distributed resources and its proven track record. Although Spark is a framework capable of handling massive datasets, it currently does not provide rich support for computation with sparse matrices and sparse graphs. Hence, methods have been implemented to enable the exploitation of sparse adjacency matrices in distributed sparse matrix multiplication, a critical component of MCL. The proposed solution can handle arbitrarily large inputs, provide almost linear speed-up with the addition of computational resources and output results directly comparable to the non-distributed reference MCL implementation.]]>
The increasing need for clustering in several scientific domains has inevitably driven the creation of innovative algorithms, each designed to perform more efficiently in certain applications. More specifically, in many applications the data entities involved can be portrayed effectively by a graph as a collection of nodes and edges. One of the most established algorithms for graph clustering problems is the Markov Cluster Algorithm (MCL). Next DSS MIA Event - https://datascience.salon/miami/ When dealing with large and complex datasets, the underlying graphs can easily reach proportions that independent computing systems are inadequate to deal with. Additionally, the graphs encountered are typically sparse: the number of edges is far smaller than might be possible in a fully-connected graph. Consequently, there is a concrete need for algorithms that are designed to handle sparse graph clustering utilizing distributed computing resources. Our motivation was the development of a distributed architecture, able to accommodate large and sparse graphs, to actualize the MCL and R-MCL algorithm. The Apache Spark framework was chosen due to its ability to utilize distributed resources and its proven track record. Although Spark is a framework capable of handling massive datasets, it currently does not provide rich support for computation with sparse matrices and sparse graphs. Hence, methods have been implemented to enable the exploitation of sparse adjacency matrices in distributed sparse matrix multiplication, a critical component of MCL. The proposed solution can handle arbitrarily large inputs, provide almost linear speed-up with the addition of computational resources and output results directly comparable to the non-distributed reference MCL implementation.]]> Mon, 12 Feb 2018 17:57:46 GMT /slideshow/mcl-clustering-of-sparse-graphs-87867848/87867848 formulatedby@slideshare.net(formulatedby) Data Science Salon: MCL Clustering of Sparse Graphs formulatedby The increasing need for clustering in several scientific domains has inevitably driven the creation of innovative algorithms, each designed to perform more efficiently in certain applications. More specifically, in many applications the data entities involved can be portrayed effectively by a graph as a collection of nodes and edges. One of the most established algorithms for graph clustering problems is the Markov Cluster Algorithm (MCL). Next DSS MIA Event - https://datascience.salon/miami/ When dealing with large and complex datasets, the underlying graphs can easily reach proportions that independent computing systems are inadequate to deal with. Additionally, the graphs encountered are typically sparse: the number of edges is far smaller than might be possible in a fully-connected graph. Consequently, there is a concrete need for algorithms that are designed to handle sparse graph clustering utilizing distributed computing resources. Our motivation was the development of a distributed architecture, able to accommodate large and sparse graphs, to actualize the MCL and R-MCL algorithm. The Apache Spark framework was chosen due to its ability to utilize distributed resources and its proven track record. Although Spark is a framework capable of handling massive datasets, it currently does not provide rich support for computation with sparse matrices and sparse graphs. Hence, methods have been implemented to enable the exploitation of sparse adjacency matrices in distributed sparse matrix multiplication, a critical component of MCL. The proposed solution can handle arbitrarily large inputs, provide almost linear speed-up with the addition of computational resources and output results directly comparable to the non-distributed reference MCL implementation. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/anthanassioskintsakis-180212175746-thumbnail.jpg?width=120&height=120&fit=bounds" /> The increasing need for clustering in several scientific domains has inevitably driven the creation of innovative algorithms, each designed to perform more efficiently in certain applications. More specifically, in many applications the data entities involved can be portrayed effectively by a graph as a collection of nodes and edges. One of the most established algorithms for graph clustering problems is the Markov Cluster Algorithm (MCL). Next DSS MIA Event - https://datascience.salon/miami/ When dealing with large and complex datasets, the underlying graphs can easily reach proportions that independent computing systems are inadequate to deal with. Additionally, the graphs encountered are typically sparse: the number of edges is far smaller than might be possible in a fully-connected graph. Consequently, there is a concrete need for algorithms that are designed to handle sparse graph clustering utilizing distributed computing resources. Our motivation was the development of a distributed architecture, able to accommodate large and sparse graphs, to actualize the MCL and R-MCL algorithm. The Apache Spark framework was chosen due to its ability to utilize distributed resources and its proven track record. Although Spark is a framework capable of handling massive datasets, it currently does not provide rich support for computation with sparse matrices and sparse graphs. Hence, methods have been implemented to enable the exploitation of sparse adjacency matrices in distributed sparse matrix multiplication, a critical component of MCL. The proposed solution can handle arbitrarily large inputs, provide almost linear speed-up with the addition of computational resources and output results directly comparable to the non-distributed reference MCL implementation.

Data Science Salon: MCL Clustering of Sparse Graphs from Formulatedby

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0 Data Science Salon: Applying Machine Learning to Modernize Business Processes /slideshow/applying-machine-learning-to-modernize-business-processes-87867681/87867681 mattmadden-180212175526
Next DSS MIA Event - https://datascience.salon/miami/ For most data scientist building models is hard work, but deploying them into production and impacting business processes can be even harder. In fact, research shows that only about 10% of data science models get deployed into production, and those that do can take between 6 to 9 months to be deployed. This session will highlight the challenges that data scientist and organizations alike face when trying to deploy machine learning models and how to overcome these challenges. It will examine several use cases where models built in R and Python have been able to deliver impactful results across several industries.]]>
Next DSS MIA Event - https://datascience.salon/miami/ For most data scientist building models is hard work, but deploying them into production and impacting business processes can be even harder. In fact, research shows that only about 10% of data science models get deployed into production, and those that do can take between 6 to 9 months to be deployed. This session will highlight the challenges that data scientist and organizations alike face when trying to deploy machine learning models and how to overcome these challenges. It will examine several use cases where models built in R and Python have been able to deliver impactful results across several industries.]]> Mon, 12 Feb 2018 17:55:26 GMT /slideshow/applying-machine-learning-to-modernize-business-processes-87867681/87867681 formulatedby@slideshare.net(formulatedby) Data Science Salon: Applying Machine Learning to Modernize Business Processes formulatedby Next DSS MIA Event - https://datascience.salon/miami/ For most data scientist building models is hard work, but deploying them into production and impacting business processes can be even harder. In fact, research shows that only about 10% of data science models get deployed into production, and those that do can take between 6 to 9 months to be deployed. This session will highlight the challenges that data scientist and organizations alike face when trying to deploy machine learning models and how to overcome these challenges. It will examine several use cases where models built in R and Python have been able to deliver impactful results across several industries. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mattmadden-180212175526-thumbnail.jpg?width=120&height=120&fit=bounds" /> Next DSS MIA Event - https://datascience.salon/miami/ For most data scientist building models is hard work, but deploying them into production and impacting business processes can be even harder. In fact, research shows that only about 10% of data science models get deployed into production, and those that do can take between 6 to 9 months to be deployed. This session will highlight the challenges that data scientist and organizations alike face when trying to deploy machine learning models and how to overcome these challenges. It will examine several use cases where models built in R and Python have been able to deliver impactful results across several industries.

Data Science Salon: Applying Machine Learning to Modernize Business Processes from Formulatedby

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0 Data Science Salon: Deep Learning as a Product @ Scribd /slideshow/deep-learning-as-a-product-scribd-84313810/84313810 kevinperko-171217195925
Presented by Kevin Perko, Head of Data Science at Scribd Next DSS NYC Event 👉 https://datascience.salon/newyork/ Next DSS LA Event 👉 https://datascience.salon/la/ Kevin will cover his experience using deep learning, going from scratch to deploying models in production to improve the product experience. He goes in-depth in terms of how we started deep learning from scratch, including navigating the maze of frameworks and hyper-parameters to optimize. Kevin will discuss pitfalls of using other people's algorithms and make a call for more rigor in publishing data science blog posts. Kevin closes with how his failure turned into an open source contribution and the work in moving from dev to production.]]>
Presented by Kevin Perko, Head of Data Science at Scribd Next DSS NYC Event 👉 https://datascience.salon/newyork/ Next DSS LA Event 👉 https://datascience.salon/la/ Kevin will cover his experience using deep learning, going from scratch to deploying models in production to improve the product experience. He goes in-depth in terms of how we started deep learning from scratch, including navigating the maze of frameworks and hyper-parameters to optimize. Kevin will discuss pitfalls of using other people's algorithms and make a call for more rigor in publishing data science blog posts. Kevin closes with how his failure turned into an open source contribution and the work in moving from dev to production.]]> Sun, 17 Dec 2017 19:59:25 GMT /slideshow/deep-learning-as-a-product-scribd-84313810/84313810 formulatedby@slideshare.net(formulatedby) Data Science Salon: Deep Learning as a Product @ Scribd formulatedby Presented by Kevin Perko, Head of Data Science at Scribd Next DSS NYC Event 👉 https://datascience.salon/newyork/ Next DSS LA Event 👉 https://datascience.salon/la/ Kevin will cover his experience using deep learning, going from scratch to deploying models in production to improve the product experience. He goes in-depth in terms of how we started deep learning from scratch, including navigating the maze of frameworks and hyper-parameters to optimize. Kevin will discuss pitfalls of using other people's algorithms and make a call for more rigor in publishing data science blog posts. Kevin closes with how his failure turned into an open source contribution and the work in moving from dev to production. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/kevinperko-171217195925-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presented by Kevin Perko, Head of Data Science at Scribd Next DSS NYC Event 👉 https://datascience.salon/newyork/ Next DSS LA Event 👉 https://datascience.salon/la/ Kevin will cover his experience using deep learning, going from scratch to deploying models in production to improve the product experience. He goes in-depth in terms of how we started deep learning from scratch, including navigating the maze of frameworks and hyper-parameters to optimize. Kevin will discuss pitfalls of using other people's algorithms and make a call for more rigor in publishing data science blog posts. Kevin closes with how his failure turned into an open source contribution and the work in moving from dev to production.

Data Science Salon: Deep Learning as a Product @ Scribd from Formulatedby

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0 Data Science Salon: Building smart AI: How Deep Learning Can Get You Into Deep Trouble /slideshow/building-smart-ai-how-deep-learning-can-get-you-into-deep-trouble/84169302 michaelhousman-171215155456
Presented by Michael Housman Chief Data Scientist at RapportBoost.AI Next DSS NYC Event 👉 https://datascience.salon/newyork/ Next DSS LA Event 👉 https://datascience.salon/la/ Recent advances in deep learning have fueled tremendous excitement about the potential for artificial intelligence to solve countless problems. But there are some perils and pitfalls endemic to these new techniques, particularly because they ignore two essential components of the scientific method: (1) understanding the how; and (2) explaining the why. Dr. Michael Housman offers up a two specific examples from his own career as a data scientist to show how a naive application of deep learning algorithms can lead data scientists to the wrong conclusion and offers up some guidance for avoiding these mistakes.]]>
Presented by Michael Housman Chief Data Scientist at RapportBoost.AI Next DSS NYC Event 👉 https://datascience.salon/newyork/ Next DSS LA Event 👉 https://datascience.salon/la/ Recent advances in deep learning have fueled tremendous excitement about the potential for artificial intelligence to solve countless problems. But there are some perils and pitfalls endemic to these new techniques, particularly because they ignore two essential components of the scientific method: (1) understanding the how; and (2) explaining the why. Dr. Michael Housman offers up a two specific examples from his own career as a data scientist to show how a naive application of deep learning algorithms can lead data scientists to the wrong conclusion and offers up some guidance for avoiding these mistakes.]]> Fri, 15 Dec 2017 15:54:56 GMT /slideshow/building-smart-ai-how-deep-learning-can-get-you-into-deep-trouble/84169302 formulatedby@slideshare.net(formulatedby) Data Science Salon: Building smart AI: How Deep Learning Can Get You Into Deep Trouble formulatedby Presented by Michael Housman Chief Data Scientist at RapportBoost.AI Next DSS NYC Event 👉 https://datascience.salon/newyork/ Next DSS LA Event 👉 https://datascience.salon/la/ Recent advances in deep learning have fueled tremendous excitement about the potential for artificial intelligence to solve countless problems. But there are some perils and pitfalls endemic to these new techniques, particularly because they ignore two essential components of the scientific method: (1) understanding the how; and (2) explaining the why. Dr. Michael Housman offers up a two specific examples from his own career as a data scientist to show how a naive application of deep learning algorithms can lead data scientists to the wrong conclusion and offers up some guidance for avoiding these mistakes. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/michaelhousman-171215155456-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presented by Michael Housman Chief Data Scientist at RapportBoost.AI Next DSS NYC Event 👉 https://datascience.salon/newyork/ Next DSS LA Event 👉 https://datascience.salon/la/ Recent advances in deep learning have fueled tremendous excitement about the potential for artificial intelligence to solve countless problems. But there are some perils and pitfalls endemic to these new techniques, particularly because they ignore two essential components of the scientific method: (1) understanding the how; and (2) explaining the why. Dr. Michael Housman offers up a two specific examples from his own career as a data scientist to show how a naive application of deep learning algorithms can lead data scientists to the wrong conclusion and offers up some guidance for avoiding these mistakes.

Data Science Salon: Building smart AI: How Deep Learning Can Get You Into Deep Trouble from Formulatedby

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0 https://cdn.slidesharecdn.com/profile-photo-formulatedby-48x48.jpg?cb=1618231940 Experiential Marketing Agency Specializing in Building Data Science Communities & Host of @DataSciSalon Interests: #datascience #dataviz #marketingscience formulated.by/ https://cdn.slidesharecdn.com/ss_thumbnails/dsspilosatalkv51-190315184729-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/an-experiment-on-data-science-algorithms-enabled-by-a-pilosa-index/136616633 Data Science Salon: An... https://cdn.slidesharecdn.com/ss_thumbnails/are-you-sure-youre-an-ethical-technologist-190315184536-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/are-you-sure-youre-an-ethical-technologist-build-your-ethical-imagination/136616343 Data Science Salon: Ar... https://cdn.slidesharecdn.com/ss_thumbnails/inyourownwordscomputingcustomersimilarityfromtextdata-190315183156-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/in-your-own-words-computing-customer-similarity-from-text-data/136614257 Data Science Salon: In...