際際滷shows by User: mlreview / http://www.slideshare.net/images/logo.gif 際際滷shows by User: mlreview / Fri, 29 Jun 2018 21:41:34 GMT 際際滷Share feed for 際際滷shows by User: mlreview Bayesian Non-parametric Models for Data Science using PyMC /slideshow/bayesian-nonparametric-models-for-data-science-using-pymc/103660551 gppymc32018-180629214134
Christopher Fonnesbeck Associate Professor Vanderbilt University Medical Center]]>
Christopher Fonnesbeck Associate Professor Vanderbilt University Medical Center]]> Fri, 29 Jun 2018 21:41:34 GMT /slideshow/bayesian-nonparametric-models-for-data-science-using-pymc/103660551 mlreview@slideshare.net(mlreview) Bayesian Non-parametric Models for Data Science using PyMC mlreview Christopher Fonnesbeck Associate Professor Vanderbilt University Medical Center <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/gppymc32018-180629214134-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Christopher Fonnesbeck Associate Professor Vanderbilt University Medical Center
Bayesian Non-parametric Models for Data Science using PyMC from MLReview
]]> 1738 4 https://cdn.slidesharecdn.com/ss_thumbnails/gppymc32018-180629214134-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Machine Learning and Counterfactual Reasoning for "Personalized" Decision- Making in Healthcare /slideshow/machine-learning-and-counterfactual-reasoning-for-personalized-decision-making-in-healthcare/79501272 uai2017tutorialsariasoleimani-170906204750
Suchi Saria Assistant Professor Computer Science, Applied Math & Stats and Health Policy Institute for Computational Medicine Hossein Soleimani Postdoctoral Fellow Computer Science]]>
Suchi Saria Assistant Professor Computer Science, Applied Math & Stats and Health Policy Institute for Computational Medicine Hossein Soleimani Postdoctoral Fellow Computer Science]]> Wed, 06 Sep 2017 20:47:50 GMT /slideshow/machine-learning-and-counterfactual-reasoning-for-personalized-decision-making-in-healthcare/79501272 mlreview@slideshare.net(mlreview) Machine Learning and Counterfactual Reasoning for "Personalized" Decision- Making in Healthcare mlreview Suchi Saria Assistant Professor Computer Science, Applied Math & Stats and Health Policy Institute for Computational Medicine Hossein Soleimani Postdoctoral Fellow Computer Science <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/uai2017tutorialsariasoleimani-170906204750-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Suchi Saria Assistant Professor Computer Science, Applied Math &amp; Stats and Health Policy Institute for Computational Medicine Hossein Soleimani Postdoctoral Fellow Computer Science
Machine Learning and Counterfactual Reasoning for "Personalized" Decision- Making in Healthcare from MLReview
]]> 937 7 https://cdn.slidesharecdn.com/ss_thumbnails/uai2017tutorialsariasoleimani-170906204750-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Tutorial on Deep Generative Models /slideshow/tutorial-on-deep-generative-models/79501223 deepgenmodelstutorial-170906204536
Shakir Mohamed and Danilo Rezende @shakir_za @deepspiker]]>
Shakir Mohamed and Danilo Rezende @shakir_za @deepspiker]]> Wed, 06 Sep 2017 20:45:36 GMT /slideshow/tutorial-on-deep-generative-models/79501223 mlreview@slideshare.net(mlreview) Tutorial on Deep Generative Models mlreview Shakir Mohamed and Danilo Rezende @shakir_za @deepspiker <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/deepgenmodelstutorial-170906204536-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Shakir Mohamed and Danilo Rezende @shakir_za @deepspiker
Tutorial on Deep Generative Models from MLReview
]]> 6022 12 https://cdn.slidesharecdn.com/ss_thumbnails/deepgenmodelstutorial-170906204536-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 PixelGAN Autoencoders /slideshow/pixelgan-autoencoders-79299642/79299642 deeplearning2017makhzanipixelgan01-170830200725
Alireza Makhzani, Brendan Frey Machine learning Group University of Toronto CIFAR Deep Learning Summer School Montreal, Canada June 29th, 2017]]>
Alireza Makhzani, Brendan Frey Machine learning Group University of Toronto CIFAR Deep Learning Summer School Montreal, Canada June 29th, 2017]]> Wed, 30 Aug 2017 20:07:25 GMT /slideshow/pixelgan-autoencoders-79299642/79299642 mlreview@slideshare.net(mlreview) PixelGAN Autoencoders mlreview Alireza Makhzani, Brendan Frey Machine learning Group University of Toronto CIFAR Deep Learning Summer School Montreal, Canada June 29th, 2017 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/deeplearning2017makhzanipixelgan01-170830200725-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Alireza Makhzani, Brendan Frey Machine learning Group University of Toronto CIFAR Deep Learning Summer School Montreal, Canada June 29th, 2017
PixelGAN Autoencoders from MLReview
]]> 581 4 https://cdn.slidesharecdn.com/ss_thumbnails/deeplearning2017makhzanipixelgan01-170830200725-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Representing and comparing probabilities: Part 2 /slideshow/representing-and-comparing-probabilities-part-2/78783060 uai172-170812080642
Arthur Gretton Gatsby Computational Neuroscience Unit, University College London UAI, 2017]]>
Arthur Gretton Gatsby Computational Neuroscience Unit, University College London UAI, 2017]]> Sat, 12 Aug 2017 08:06:42 GMT /slideshow/representing-and-comparing-probabilities-part-2/78783060 mlreview@slideshare.net(mlreview) Representing and comparing probabilities: Part 2 mlreview Arthur Gretton Gatsby Computational Neuroscience Unit, University College London UAI, 2017 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/uai172-170812080642-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Arthur Gretton Gatsby Computational Neuroscience Unit, University College London UAI, 2017
Representing and comparing probabilities: Part 2 from MLReview
]]> 175 5 https://cdn.slidesharecdn.com/ss_thumbnails/uai172-170812080642-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Representing and comparing probabilities /slideshow/representing-and-comparing-probabilities/78783026 uai17-170812080457
Arthur Gretton Gatsby Computational Neuroscience Unit, University College London UAI, 2017]]>
Arthur Gretton Gatsby Computational Neuroscience Unit, University College London UAI, 2017]]> Sat, 12 Aug 2017 08:04:57 GMT /slideshow/representing-and-comparing-probabilities/78783026 mlreview@slideshare.net(mlreview) Representing and comparing probabilities mlreview Arthur Gretton Gatsby Computational Neuroscience Unit, University College London UAI, 2017 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/uai17-170812080457-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Arthur Gretton Gatsby Computational Neuroscience Unit, University College London UAI, 2017
Representing and comparing probabilities from MLReview
]]> 296 6 https://cdn.slidesharecdn.com/ss_thumbnails/uai17-170812080457-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 OPTIMIZATION AS A MODEL FOR FEW-SHOT LEARNING /slideshow/optimization-as-a-model-for-fewshot-learning/78782411 metalearning-170812072548
Hugo Larochelle Work done at Twitter Google Brain ]]>
Hugo Larochelle Work done at Twitter Google Brain ]]> Sat, 12 Aug 2017 07:25:48 GMT /slideshow/optimization-as-a-model-for-fewshot-learning/78782411 mlreview@slideshare.net(mlreview) OPTIMIZATION AS A MODEL FOR FEW-SHOT LEARNING mlreview Hugo Larochelle Work done at Twitter Google Brain <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/metalearning-170812072548-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Hugo Larochelle Work done at Twitter Google Brain
OPTIMIZATION AS A MODEL FOR FEW-SHOT LEARNING from MLReview
]]> 1657 8 https://cdn.slidesharecdn.com/ss_thumbnails/metalearning-170812072548-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Theoretical Neuroscience and Deep Learning Theory /slideshow/theoretical-neuroscience-and-deep-learning-theory/78577281 ganguli-theoreticalneuroscienceanddeeplearning-170804192153
Surya Ganguli Dept. of Applied Physics, Neurobiology, and Electrical Engineering Stanford University]]>
Surya Ganguli Dept. of Applied Physics, Neurobiology, and Electrical Engineering Stanford University]]> Fri, 04 Aug 2017 19:21:53 GMT /slideshow/theoretical-neuroscience-and-deep-learning-theory/78577281 mlreview@slideshare.net(mlreview) Theoretical Neuroscience and Deep Learning Theory mlreview Surya Ganguli Dept. of Applied Physics, Neurobiology, and Electrical Engineering Stanford University <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ganguli-theoreticalneuroscienceanddeeplearning-170804192153-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Surya Ganguli Dept. of Applied Physics, Neurobiology, and Electrical Engineering Stanford University
Theoretical Neuroscience and Deep Learning Theory from MLReview
]]> 796 9 https://cdn.slidesharecdn.com/ss_thumbnails/ganguli-theoreticalneuroscienceanddeeplearning-170804192153-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 2017 Tutorial - Deep Learning for Dialogue Systems /slideshow/2017-tutorial-deep-learning-for-dialogue-systems/78424023 deepdialoguetutorial-170731184955
In the past decade, goal-oriented spoken dialogue systems (SDS) have been the most promi-nent component in todays virtual personal assistants (VPAs). Among these VPAs, Microsofts Cortana, Apples Siri, Amazon Alexa, Google Assistant, and Facebooks M, have incorporated SDS modules in various devices, which allow users to speak naturally in order to 鍖nish tasks more e鍖ciently. The traditional conversational systems have rather complex and/or modular pipelines. The advance of deep learning technologies has recently risen the applicatins of neural models to dialogue modeling. Nevertheless, applying deep learning technologies for building robust and scalable dialogue systems is still a challenging task and an open research area as it requires deeper understanding of the classic pipelines as well as detailed knowledge on the benchmark of the models of the prior work and the recent state-of-the-art work. Thus, this tutorial is designed to focus on an overview of the dialogue system development while describing most recent research for building dialogue systems, and summarizing the challenges. We target an audience of students and practitioners who have some deep learning background and want to get more familiar with conversational dialog systems.]]>
In the past decade, goal-oriented spoken dialogue systems (SDS) have been the most promi-nent component in todays virtual personal assistants (VPAs). Among these VPAs, Microsofts Cortana, Apples Siri, Amazon Alexa, Google Assistant, and Facebooks M, have incorporated SDS modules in various devices, which allow users to speak naturally in order to 鍖nish tasks more e鍖ciently. The traditional conversational systems have rather complex and/or modular pipelines. The advance of deep learning technologies has recently risen the applicatins of neural models to dialogue modeling. Nevertheless, applying deep learning technologies for building robust and scalable dialogue systems is still a challenging task and an open research area as it requires deeper understanding of the classic pipelines as well as detailed knowledge on the benchmark of the models of the prior work and the recent state-of-the-art work. Thus, this tutorial is designed to focus on an overview of the dialogue system development while describing most recent research for building dialogue systems, and summarizing the challenges. We target an audience of students and practitioners who have some deep learning background and want to get more familiar with conversational dialog systems.]]> Mon, 31 Jul 2017 18:49:55 GMT /slideshow/2017-tutorial-deep-learning-for-dialogue-systems/78424023 mlreview@slideshare.net(mlreview) 2017 Tutorial - Deep Learning for Dialogue Systems mlreview In the past decade, goal-oriented spoken dialogue systems (SDS) have been the most promi-nent component in todays virtual personal assistants (VPAs). Among these VPAs, Microsofts Cortana, Apples Siri, Amazon Alexa, Google Assistant, and Facebooks M, have incorporated SDS modules in various devices, which allow users to speak naturally in order to 鍖nish tasks more e鍖ciently. The traditional conversational systems have rather complex and/or modular pipelines. The advance of deep learning technologies has recently risen the applicatins of neural models to dialogue modeling. Nevertheless, applying deep learning technologies for building robust and scalable dialogue systems is still a challenging task and an open research area as it requires deeper understanding of the classic pipelines as well as detailed knowledge on the benchmark of the models of the prior work and the recent state-of-the-art work. Thus, this tutorial is designed to focus on an overview of the dialogue system development while describing most recent research for building dialogue systems, and summarizing the challenges. We target an audience of students and practitioners who have some deep learning background and want to get more familiar with conversational dialog systems. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/deepdialoguetutorial-170731184955-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In the past decade, goal-oriented spoken dialogue systems (SDS) have been the most promi-nent component in todays virtual personal assistants (VPAs). Among these VPAs, Microsofts Cortana, Apples Siri, Amazon Alexa, Google Assistant, and Facebooks M, have incorporated SDS modules in various devices, which allow users to speak naturally in order to 鍖nish tasks more e鍖ciently. The traditional conversational systems have rather complex and/or modular pipelines. The advance of deep learning technologies has recently risen the applicatins of neural models to dialogue modeling. Nevertheless, applying deep learning technologies for building robust and scalable dialogue systems is still a challenging task and an open research area as it requires deeper understanding of the classic pipelines as well as detailed knowledge on the benchmark of the models of the prior work and the recent state-of-the-art work. Thus, this tutorial is designed to focus on an overview of the dialogue system development while describing most recent research for building dialogue systems, and summarizing the challenges. We target an audience of students and practitioners who have some deep learning background and want to get more familiar with conversational dialog systems.
2017 Tutorial - Deep Learning for Dialogue Systems from MLReview
]]> 1765 8 https://cdn.slidesharecdn.com/ss_thumbnails/deepdialoguetutorial-170731184955-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Deep Learning for Semantic Composition /slideshow/deep-learning-for-semantic-composition/78423516 acl17tutorial-170731183346
Xiaodan Zhu & Edward Grefenstette National Research Council Canada Queens University zhu2048@gmail.com DeepMind etg@google.com July 30th, 2017]]>
Xiaodan Zhu & Edward Grefenstette National Research Council Canada Queens University zhu2048@gmail.com DeepMind etg@google.com July 30th, 2017]]> Mon, 31 Jul 2017 18:33:46 GMT /slideshow/deep-learning-for-semantic-composition/78423516 mlreview@slideshare.net(mlreview) Deep Learning for Semantic Composition mlreview Xiaodan Zhu & Edward Grefenstette National Research Council Canada Queens University zhu2048@gmail.com DeepMind etg@google.com July 30th, 2017 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/acl17tutorial-170731183346-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Xiaodan Zhu &amp; Edward Grefenstette National Research Council Canada Queens University zhu2048@gmail.com DeepMind etg@google.com July 30th, 2017
Deep Learning for Semantic Composition from MLReview
]]> 703 9 https://cdn.slidesharecdn.com/ss_thumbnails/acl17tutorial-170731183346-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Near human performance in question answering? /slideshow/near-human-performance-in-question-answering/78357020 on-squad-170728231155
Yoav Goldberg Bar Ilan University]]>
Yoav Goldberg Bar Ilan University]]> Fri, 28 Jul 2017 23:11:55 GMT /slideshow/near-human-performance-in-question-answering/78357020 mlreview@slideshare.net(mlreview) Near human performance in question answering? mlreview Yoav Goldberg Bar Ilan University <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/on-squad-170728231155-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Yoav Goldberg Bar Ilan University
Near human performance in question answering? from MLReview
]]> 214 2 https://cdn.slidesharecdn.com/ss_thumbnails/on-squad-170728231155-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Tutorial on Theory and Application of Generative Adversarial Networks /slideshow/tutorial-on-theory-and-application-of-generative-adversarial-networks/78323356 gantutorial-170727200351
Description Generative adversarial network (GAN) has recently emerged as a promising generative modeling approach. It consists of a generative network and a discriminative network. Through the competition between the two networks, it learns to model the data distribution. In addition to modeling the image/video distribution in computer vision problems, the framework finds use in defining visual concept using examples. To a large extent, it eliminates the need of hand-crafting objective functions for various computer vision problems. In this tutorial, we will present an overview of generative adversarial network research. We will cover several recent theoretical studies as well as training techniques and will also cover several vision applications of generative adversarial networks.]]>
Description Generative adversarial network (GAN) has recently emerged as a promising generative modeling approach. It consists of a generative network and a discriminative network. Through the competition between the two networks, it learns to model the data distribution. In addition to modeling the image/video distribution in computer vision problems, the framework finds use in defining visual concept using examples. To a large extent, it eliminates the need of hand-crafting objective functions for various computer vision problems. In this tutorial, we will present an overview of generative adversarial network research. We will cover several recent theoretical studies as well as training techniques and will also cover several vision applications of generative adversarial networks.]]> Thu, 27 Jul 2017 20:03:51 GMT /slideshow/tutorial-on-theory-and-application-of-generative-adversarial-networks/78323356 mlreview@slideshare.net(mlreview) Tutorial on Theory and Application of Generative Adversarial Networks mlreview Description Generative adversarial network (GAN) has recently emerged as a promising generative modeling approach. It consists of a generative network and a discriminative network. Through the competition between the two networks, it learns to model the data distribution. In addition to modeling the image/video distribution in computer vision problems, the framework finds use in defining visual concept using examples. To a large extent, it eliminates the need of hand-crafting objective functions for various computer vision problems. In this tutorial, we will present an overview of generative adversarial network research. We will cover several recent theoretical studies as well as training techniques and will also cover several vision applications of generative adversarial networks. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/gantutorial-170727200351-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Description Generative adversarial network (GAN) has recently emerged as a promising generative modeling approach. It consists of a generative network and a discriminative network. Through the competition between the two networks, it learns to model the data distribution. In addition to modeling the image/video distribution in computer vision problems, the framework finds use in defining visual concept using examples. To a large extent, it eliminates the need of hand-crafting objective functions for various computer vision problems. In this tutorial, we will present an overview of generative adversarial network research. We will cover several recent theoretical studies as well as training techniques and will also cover several vision applications of generative adversarial networks.
Tutorial on Theory and Application of Generative Adversarial Networks from MLReview
]]> 3915 14 https://cdn.slidesharecdn.com/ss_thumbnails/gantutorial-170727200351-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Real-time Edge-aware Image Processing with the Bilateral Grid /slideshow/realtime-edgeaware-image-processing-with-the-bilateral-grid/78054871 rteaipbg-170719184437
Jiawen Chen, Sylvain Paris, Fr辿do Durand Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology]]>
Jiawen Chen, Sylvain Paris, Fr辿do Durand Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology]]> Wed, 19 Jul 2017 18:44:37 GMT /slideshow/realtime-edgeaware-image-processing-with-the-bilateral-grid/78054871 mlreview@slideshare.net(mlreview) Real-time Edge-aware Image Processing with the Bilateral Grid mlreview Jiawen Chen, Sylvain Paris, Fr辿do Durand Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/rteaipbg-170719184437-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Jiawen Chen, Sylvain Paris, Fr辿do Durand Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology
Real-time Edge-aware Image Processing with the Bilateral Grid from MLReview
]]> 743 2 https://cdn.slidesharecdn.com/ss_thumbnails/rteaipbg-170719184437-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Yoav Goldberg: Word Embeddings What, How and Whither /slideshow/yoav-goldberg-word-embeddings-what-how-and-whither/77621756 cvsc20151-170707201019
Yoav Goldberg: Word Embeddings What, How and Whither]]>
Yoav Goldberg: Word Embeddings What, How and Whither]]> Fri, 07 Jul 2017 20:10:19 GMT /slideshow/yoav-goldberg-word-embeddings-what-how-and-whither/77621756 mlreview@slideshare.net(mlreview) Yoav Goldberg: Word Embeddings What, How and Whither mlreview Yoav Goldberg: Word Embeddings What, How and Whither <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cvsc20151-170707201019-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Yoav Goldberg: Word Embeddings What, How and Whither
Yoav Goldberg: Word Embeddings What, How and Whither from MLReview
]]> 1017 8 https://cdn.slidesharecdn.com/ss_thumbnails/cvsc20151-170707201019-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-mlreview-48x48.jpg?cb=1556705295 We share credible papers, books, videos and software related to Machine Learning. #machinelearning #computervision #deeplearning #nlp mlreview.com https://cdn.slidesharecdn.com/ss_thumbnails/gppymc32018-180629214134-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/bayesian-nonparametric-models-for-data-science-using-pymc/103660551 Bayesian Non-parametr... https://cdn.slidesharecdn.com/ss_thumbnails/uai2017tutorialsariasoleimani-170906204750-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/machine-learning-and-counterfactual-reasoning-for-personalized-decision-making-in-healthcare/79501272 Machine Learning and... https://cdn.slidesharecdn.com/ss_thumbnails/deepgenmodelstutorial-170906204536-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/tutorial-on-deep-generative-models/79501223 Tutorial on Deep Gene...