�ݺ�ߣshows by User: rosentep

�ݺ�ߣshows by User: rosentep / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: rosentep / Fri, 12 Jul 2019 16:53:35 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: rosentep Model remodeling with modern deep learning frameworks /rosentep/model-remodeling-with-modern-deep-learning-frameworks-155196029 modelremodelingwithmoderndeeplearningframeworks-190712165335
SciPy 2019 Talk Video: https://youtu.be/OoGaFn3aaMU Conference Link: https://www.scipy2019.scipy.org/confschedule Abstract: While modern deep learning frameworks have revolutionized the ability for non-experts to train deep learning models, they have also democratized a host of other innovations which extend beyond the niche of deep learning. In this talk, I will explore some models and domains that are not commonly thought of as “machine learning” problems and show how PyTorch allows one to build more complex and scalable models than ever before. This represents an opportunity to revisit existing models, which I will do by showing how to implement them with PyTorch and integrate them into the rest of the PyData ecosystem.]]>
SciPy 2019 Talk Video: https://youtu.be/OoGaFn3aaMU Conference Link: https://www.scipy2019.scipy.org/confschedule Abstract: While modern deep learning frameworks have revolutionized the ability for non-experts to train deep learning models, they have also democratized a host of other innovations which extend beyond the niche of deep learning. In this talk, I will explore some models and domains that are not commonly thought of as “machine learning” problems and show how PyTorch allows one to build more complex and scalable models than ever before. This represents an opportunity to revisit existing models, which I will do by showing how to implement them with PyTorch and integrate them into the rest of the PyData ecosystem.]]> Fri, 12 Jul 2019 16:53:35 GMT /rosentep/model-remodeling-with-modern-deep-learning-frameworks-155196029 rosentep@slideshare.net(rosentep) Model remodeling with modern deep learning frameworks rosentep SciPy 2019 Talk Video: https://youtu.be/OoGaFn3aaMU Conference Link: https://www.scipy2019.scipy.org/confschedule Abstract: While modern deep learning frameworks have revolutionized the ability for non-experts to train deep learning models, they have also democratized a host of other innovations which extend beyond the niche of deep learning. In this talk, I will explore some models and domains that are not commonly thought of as “machine learning” problems and show how PyTorch allows one to build more complex and scalable models than ever before. This represents an opportunity to revisit existing models, which I will do by showing how to implement them with PyTorch and integrate them into the rest of the PyData ecosystem. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/modelremodelingwithmoderndeeplearningframeworks-190712165335-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> SciPy 2019 Talk Video: https://youtu.be/OoGaFn3aaMU Conference Link: https://www.scipy2019.scipy.org/confschedule Abstract: While modern deep learning frameworks have revolutionized the ability for non-experts to train deep learning models, they have also democratized a host of other innovations which extend beyond the niche of deep learning. In this talk, I will explore some models and domains that are not commonly thought of as “machine learning” problems and show how PyTorch allows one to build more complex and scalable models than ever before. This represents an opportunity to revisit existing models, which I will do by showing how to implement them with PyTorch and integrate them into the rest of the PyData ecosystem.

Model remodeling with modern deep learning frameworks from rosentep

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0 Scaling Personalization via Machine-Learned Assortment Optimization /slideshow/scaling-personalization-via-machinelearned-assortment-optimization/125195012 2-181206182844
From DataEngConf NYC 2018 https://www.datacouncil.ai/speaker/scaling-personalization-via-machine-learned-assortment-optimization -- Machine learning has revolutionized the capability of businesses to create personalized experiences via real-time, individual predictions and recommendations. But what happens when one must make thousands of decisions for thousands of individuals at the same time? At Dia&Co, a plus-size women’s styling service, we recently faced such an obstacle when building out a brand new product line for the business. This talk will explore how we combined modern machine learning with classical operations research techniques to scale personalization in the face of constraints inherent to a retail business. The basics of operations research will be introduced before demonstrating how to solve a simple version of our real-world problem using all open source libraries. I will then reveal the gory details of productionizing this work, from testing to gracefully handling failures of convergence. Finally, I will cover the journey from the coldest of starts, with zero data, to synthesizing machine learning with the operations research problem.]]>
From DataEngConf NYC 2018 https://www.datacouncil.ai/speaker/scaling-personalization-via-machine-learned-assortment-optimization -- Machine learning has revolutionized the capability of businesses to create personalized experiences via real-time, individual predictions and recommendations. But what happens when one must make thousands of decisions for thousands of individuals at the same time? At Dia&Co, a plus-size women’s styling service, we recently faced such an obstacle when building out a brand new product line for the business. This talk will explore how we combined modern machine learning with classical operations research techniques to scale personalization in the face of constraints inherent to a retail business. The basics of operations research will be introduced before demonstrating how to solve a simple version of our real-world problem using all open source libraries. I will then reveal the gory details of productionizing this work, from testing to gracefully handling failures of convergence. Finally, I will cover the journey from the coldest of starts, with zero data, to synthesizing machine learning with the operations research problem.]]> Thu, 06 Dec 2018 18:28:44 GMT /slideshow/scaling-personalization-via-machinelearned-assortment-optimization/125195012 rosentep@slideshare.net(rosentep) Scaling Personalization via Machine-Learned Assortment Optimization rosentep From DataEngConf NYC 2018 https://www.datacouncil.ai/speaker/scaling-personalization-via-machine-learned-assortment-optimization -- Machine learning has revolutionized the capability of businesses to create personalized experiences via real-time, individual predictions and recommendations. But what happens when one must make thousands of decisions for thousands of individuals at the same time? At Dia&Co, a plus-size women’s styling service, we recently faced such an obstacle when building out a brand new product line for the business. This talk will explore how we combined modern machine learning with classical operations research techniques to scale personalization in the face of constraints inherent to a retail business. The basics of operations research will be introduced before demonstrating how to solve a simple version of our real-world problem using all open source libraries. I will then reveal the gory details of productionizing this work, from testing to gracefully handling failures of convergence. Finally, I will cover the journey from the coldest of starts, with zero data, to synthesizing machine learning with the operations research problem. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2-181206182844-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> From DataEngConf NYC 2018 https://www.datacouncil.ai/speaker/scaling-personalization-via-machine-learned-assortment-optimization -- Machine learning has revolutionized the capability of businesses to create personalized experiences via real-time, individual predictions and recommendations. But what happens when one must make thousands of decisions for thousands of individuals at the same time? At Dia&Co, a plus-size women’s styling service, we recently faced such an obstacle when building out a brand new product line for the business. This talk will explore how we combined modern machine learning with classical operations research techniques to scale personalization in the face of constraints inherent to a retail business. The basics of operations research will be introduced before demonstrating how to solve a simple version of our real-world problem using all open source libraries. I will then reveal the gory details of productionizing this work, from testing to gracefully handling failures of convergence. Finally, I will cover the journey from the coldest of starts, with zero data, to synthesizing machine learning with the operations research problem.

Scaling Personalization via Machine-Learned Assortment Optimization from rosentep

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0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/modelremodelingwithmoderndeeplearningframeworks-190712165335-thumbnail.jpg?width=320&height=320&fit=bounds rosentep/model-remodeling-with-modern-deep-learning-frameworks-155196029 Model remodeling with ... https://cdn.slidesharecdn.com/ss_thumbnails/2-181206182844-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/scaling-personalization-via-machinelearned-assortment-optimization/125195012 Scaling Personalizatio...