ºÝºÝߣshows by User: MohamedElGeish / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: MohamedElGeish / Thu, 19 Nov 2020 06:06:27 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: MohamedElGeish The AI Industry in 2020-2030 /slideshow/the-ai-industry-in-2020-2030/239329198 theaiindustryin2020-2030-201119060627
The last 5 years have been transformative in the AI industry, how will the next 10 look like? We've seen an explosion in IoT devices and the data flowing through them — ubiquitous computing is here to stay. How would this change the ecosystem with respect to hardware, solution development, testing, copyrights, privacy, etc.? And finally, a prediction of what all of this means for businesses, current and new, in light of advancements in deep learning.]]>
The last 5 years have been transformative in the AI industry, how will the next 10 look like? We've seen an explosion in IoT devices and the data flowing through them — ubiquitous computing is here to stay. How would this change the ecosystem with respect to hardware, solution development, testing, copyrights, privacy, etc.? And finally, a prediction of what all of this means for businesses, current and new, in light of advancements in deep learning.]]> Thu, 19 Nov 2020 06:06:27 GMT /slideshow/the-ai-industry-in-2020-2030/239329198 MohamedElGeish@slideshare.net(MohamedElGeish) The AI Industry in 2020-2030 MohamedElGeish The last 5 years have been transformative in the AI industry, how will the next 10 look like? We've seen an explosion in IoT devices and the data flowing through them — ubiquitous computing is here to stay. How would this change the ecosystem with respect to hardware, solution development, testing, copyrights, privacy, etc.? And finally, a prediction of what all of this means for businesses, current and new, in light of advancements in deep learning. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/theaiindustryin2020-2030-201119060627-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The last 5 years have been transformative in the AI industry, how will the next 10 look like? We&#39;ve seen an explosion in IoT devices and the data flowing through them — ubiquitous computing is here to stay. How would this change the ecosystem with respect to hardware, solution development, testing, copyrights, privacy, etc.? And finally, a prediction of what all of this means for businesses, current and new, in light of advancements in deep learning.
The AI Industry in 2020-2030 from Mohamed El-Geish
]]> 57 0 https://cdn.slidesharecdn.com/ss_thumbnails/theaiindustryin2020-2030-201119060627-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 Semi-Supervised Keyword Spotting in Arabic Speech Using Self-Training Ensembles /slideshow/semisupervised-keyword-spotting-in-arabic-speech-using-selftraining-ensembles/70486478 keywordspottinginarabicspeech-161228044736
Arabic speech recognition suffers from the scarcity of properly labeled data. In this project, we introduce a pipeline that performs semi-supervised segmentation of audio then— after hand-labeling a small dataset—feeds labeled segments to a supervised learning framework to select, through many rounds of hyperparameter optimization, an ensemble of models to infer labels for a larger dataset; using which we improved the keyword spotter’s F1 score from 75.85% (using a baseline model) to 90.91% on a ground-truth test set. We picked the keyword na`am (yes) to spot; we defined the system’s input as an audio file of an utterance and the output as a binary label: keyword or filler.]]>
Arabic speech recognition suffers from the scarcity of properly labeled data. In this project, we introduce a pipeline that performs semi-supervised segmentation of audio then— after hand-labeling a small dataset—feeds labeled segments to a supervised learning framework to select, through many rounds of hyperparameter optimization, an ensemble of models to infer labels for a larger dataset; using which we improved the keyword spotter’s F1 score from 75.85% (using a baseline model) to 90.91% on a ground-truth test set. We picked the keyword na`am (yes) to spot; we defined the system’s input as an audio file of an utterance and the output as a binary label: keyword or filler.]]> Wed, 28 Dec 2016 04:47:36 GMT /slideshow/semisupervised-keyword-spotting-in-arabic-speech-using-selftraining-ensembles/70486478 MohamedElGeish@slideshare.net(MohamedElGeish) Semi-Supervised Keyword Spotting in Arabic Speech Using Self-Training Ensembles MohamedElGeish Arabic speech recognition suffers from the scarcity of properly labeled data. In this project, we introduce a pipeline that performs semi-supervised segmentation of audio then— after hand-labeling a small dataset—feeds labeled segments to a supervised learning framework to select, through many rounds of hyperparameter optimization, an ensemble of models to infer labels for a larger dataset; using which we improved the keyword spotter’s F1 score from 75.85% (using a baseline model) to 90.91% on a ground-truth test set. We picked the keyword na`am (yes) to spot; we defined the system’s input as an audio file of an utterance and the output as a binary label: keyword or filler. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/keywordspottinginarabicspeech-161228044736-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Arabic speech recognition suffers from the scarcity of properly labeled data. In this project, we introduce a pipeline that performs semi-supervised segmentation of audio then— after hand-labeling a small dataset—feeds labeled segments to a supervised learning framework to select, through many rounds of hyperparameter optimization, an ensemble of models to infer labels for a larger dataset; using which we improved the keyword spotter’s F1 score from 75.85% (using a baseline model) to 90.91% on a ground-truth test set. We picked the keyword na`am (yes) to spot; we defined the system’s input as an audio file of an utterance and the output as a binary label: keyword or filler.
Semi-Supervised Keyword Spotting in Arabic Speech Using Self-Training Ensembles from Mohamed El-Geish
]]> 814 6 https://cdn.slidesharecdn.com/ss_thumbnails/keywordspottinginarabicspeech-161228044736-thumbnail.jpg?width=120&height=120&fit=bounds document 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Prediction of Reaction towards Textual Posts in Social Networks /slideshow/socialnetworkspostreactionprediction/64954121 b943fb3d-e2bb-4d15-be84-b16b49978e04-160812232400
Posting on social networks could be a gratifying or a terrifying experience depending on the reaction the post and its author —by association— receive from the readers. To better understand what makes a post popular, this project inquires into the factors that determine the number of likes, comments, and shares a textual post gets on LinkedIn; and finds a predictor function that can estimate those quantitative social gestures.]]>
Posting on social networks could be a gratifying or a terrifying experience depending on the reaction the post and its author —by association— receive from the readers. To better understand what makes a post popular, this project inquires into the factors that determine the number of likes, comments, and shares a textual post gets on LinkedIn; and finds a predictor function that can estimate those quantitative social gestures.]]> Fri, 12 Aug 2016 23:24:00 GMT /slideshow/socialnetworkspostreactionprediction/64954121 MohamedElGeish@slideshare.net(MohamedElGeish) Prediction of Reaction towards Textual Posts in Social Networks MohamedElGeish Posting on social networks could be a gratifying or a terrifying experience depending on the reaction the post and its author —by association— receive from the readers. To better understand what makes a post popular, this project inquires into the factors that determine the number of likes, comments, and shares a textual post gets on LinkedIn; and finds a predictor function that can estimate those quantitative social gestures. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/b943fb3d-e2bb-4d15-be84-b16b49978e04-160812232400-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Posting on social networks could be a gratifying or a terrifying experience depending on the reaction the post and its author —by association— receive from the readers. To better understand what makes a post popular, this project inquires into the factors that determine the number of likes, comments, and shares a textual post gets on LinkedIn; and finds a predictor function that can estimate those quantitative social gestures.
Prediction of Reaction towards Textual Posts in Social Networks from Mohamed El-Geish
]]> 478 4 https://cdn.slidesharecdn.com/ss_thumbnails/b943fb3d-e2bb-4d15-be84-b16b49978e04-160812232400-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Harvesting the Power of Samza in LinkedIn's Feed /slideshow/harvesting-the-power-of-samza-in-linkedins-feed/56289531 meetuptalk-151218231407
LinkedIn's Feed is the entry point for hundreds of millions of members who seek to stay informed about their professional interests. The feed strives to provide relevant content to members that's also new and fresh. How does the feed solve this problem at scale? What role does Samza play in this? Join us to find out.]]>
LinkedIn's Feed is the entry point for hundreds of millions of members who seek to stay informed about their professional interests. The feed strives to provide relevant content to members that's also new and fresh. How does the feed solve this problem at scale? What role does Samza play in this? Join us to find out.]]> Fri, 18 Dec 2015 23:14:06 GMT /slideshow/harvesting-the-power-of-samza-in-linkedins-feed/56289531 MohamedElGeish@slideshare.net(MohamedElGeish) Harvesting the Power of Samza in LinkedIn's Feed MohamedElGeish LinkedIn's Feed is the entry point for hundreds of millions of members who seek to stay informed about their professional interests. The feed strives to provide relevant content to members that's also new and fresh. How does the feed solve this problem at scale? What role does Samza play in this? Join us to find out. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/meetuptalk-151218231407-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> LinkedIn&#39;s Feed is the entry point for hundreds of millions of members who seek to stay informed about their professional interests. The feed strives to provide relevant content to members that&#39;s also new and fresh. How does the feed solve this problem at scale? What role does Samza play in this? Join us to find out.
Harvesting the Power of Samza in LinkedIn's Feed from Mohamed El-Geish
]]> 1225 5 https://cdn.slidesharecdn.com/ss_thumbnails/meetuptalk-151218231407-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-MohamedElGeish-48x48.jpg?cb=1671750938 I'm passionate about machine learning, big data, debugging, software craftsmanship, teaching, coaching, building engineering organizations that solve challenging problems effectively, helping others with their aspirations, and writing about the above (see more about the Computing with Data book here: https://computingwithdata.com). My personal mission is to empower people around the world to communicate securely and more efficiently using services that provide great experiences. elgeish.com https://cdn.slidesharecdn.com/ss_thumbnails/theaiindustryin2020-2030-201119060627-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/the-ai-industry-in-2020-2030/239329198 The AI Industry in 202... https://cdn.slidesharecdn.com/ss_thumbnails/keywordspottinginarabicspeech-161228044736-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/semisupervised-keyword-spotting-in-arabic-speech-using-selftraining-ensembles/70486478 Semi-Supervised Keywor... https://cdn.slidesharecdn.com/ss_thumbnails/b943fb3d-e2bb-4d15-be84-b16b49978e04-160812232400-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/socialnetworkspostreactionprediction/64954121 Prediction of Reaction...