ºÝºÝߣshows by User: WiMLDS_Paris / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: WiMLDS_Paris / Tue, 15 Oct 2024 13:32:09 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: WiMLDS_Paris Low Rank Optimisation, by Irène Waldspurger /slideshow/low-rank-optimisation-by-irene-waldspurger/272433132 presentation-241015133209-4bfbc297
Low-rank optimization, by Irène Waldspurger, CNRS researcher at CEREMADE A low-rank optimization problem consists in identifying a matrix from a few observations, under the assumption that this matrix has low rank. I will give examples of such problems to motivate their analysis. Then, I will explain why they are difficult to solve, and present the algorithms which have been developed in the last fifteen years as well as some open research questions.]]>
Low-rank optimization, by Irène Waldspurger, CNRS researcher at CEREMADE A low-rank optimization problem consists in identifying a matrix from a few observations, under the assumption that this matrix has low rank. I will give examples of such problems to motivate their analysis. Then, I will explain why they are difficult to solve, and present the algorithms which have been developed in the last fifteen years as well as some open research questions.]]> Tue, 15 Oct 2024 13:32:09 GMT /slideshow/low-rank-optimisation-by-irene-waldspurger/272433132 WiMLDS_Paris@slideshare.net(WiMLDS_Paris) Low Rank Optimisation, by Irène Waldspurger WiMLDS_Paris Low-rank optimization, by Irène Waldspurger, CNRS researcher at CEREMADE A low-rank optimization problem consists in identifying a matrix from a few observations, under the assumption that this matrix has low rank. I will give examples of such problems to motivate their analysis. Then, I will explain why they are difficult to solve, and present the algorithms which have been developed in the last fifteen years as well as some open research questions. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentation-241015133209-4bfbc297-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Low-rank optimization, by Irène Waldspurger, CNRS researcher at CEREMADE A low-rank optimization problem consists in identifying a matrix from a few observations, under the assumption that this matrix has low rank. I will give examples of such problems to motivate their analysis. Then, I will explain why they are difficult to solve, and present the algorithms which have been developed in the last fifteen years as well as some open research questions.
Low Rank Optimisation, by Ir竪ne Waldspurger from Paris Women in Machine Learning and Data Science
]]> 21 0 https://cdn.slidesharecdn.com/ss_thumbnails/presentation-241015133209-4bfbc297-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 From Golem to Code: AI and Male Fantasies of Self-Engendering by Isabelle Collet https://fr.slideshare.net/slideshow/from-golem-to-code-ai-and-male-fantasies-of-self-engendering-by-isabelle-collet/272433087 10wim-241015132959-f1da7e7a
Du Golem au code : l'IA et les fantasmes masculins d'autoengendrement, by Isabelle Collet, Associated Professor at University of Geneva, author of "Les oubliées du numérique" L'histoire occidentale est remplie d'une longue série de mythes parlant de créatures artificielles et, dans certains cas, d'humains tentant d'usurper la place de Dieu en se lançant dans le processus de création. Il y a 20 ans, quand j'ai commencé à travailler sur les questions de genre en informatique, je me suis intéressée à créatures artificielles, car, pour moi, l'ordinateur a été rêvé comme faisant partie de cette grande famille. Quand ils ont conçu l'ENIAC, les pères de l'informatique ne cherchaient pas réellement à produire une grosse machine pour calculer, même si c'est ce qu'ils ont réalisé. L'ordinateur des années 1950, qui était pourtant très loin des performances de ChatGPT, était vu comme une étape vers le but ultime de la science : une duplication du cerveau humain. Si je relie ces fantasmes à la question « Genre », c'est parce que tous les créateurs de créatures artificielles sont des hommes et que tous trouvent une solution pour créer un être nouveau sans passer par la reproduction sexuée, c'est-à-dire sans l'aide des femmes.]]>
Du Golem au code : l'IA et les fantasmes masculins d'autoengendrement, by Isabelle Collet, Associated Professor at University of Geneva, author of "Les oubliées du numérique" L'histoire occidentale est remplie d'une longue série de mythes parlant de créatures artificielles et, dans certains cas, d'humains tentant d'usurper la place de Dieu en se lançant dans le processus de création. Il y a 20 ans, quand j'ai commencé à travailler sur les questions de genre en informatique, je me suis intéressée à créatures artificielles, car, pour moi, l'ordinateur a été rêvé comme faisant partie de cette grande famille. Quand ils ont conçu l'ENIAC, les pères de l'informatique ne cherchaient pas réellement à produire une grosse machine pour calculer, même si c'est ce qu'ils ont réalisé. L'ordinateur des années 1950, qui était pourtant très loin des performances de ChatGPT, était vu comme une étape vers le but ultime de la science : une duplication du cerveau humain. Si je relie ces fantasmes à la question « Genre », c'est parce que tous les créateurs de créatures artificielles sont des hommes et que tous trouvent une solution pour créer un être nouveau sans passer par la reproduction sexuée, c'est-à-dire sans l'aide des femmes.]]> Tue, 15 Oct 2024 13:29:59 GMT https://fr.slideshare.net/slideshow/from-golem-to-code-ai-and-male-fantasies-of-self-engendering-by-isabelle-collet/272433087 WiMLDS_Paris@slideshare.net(WiMLDS_Paris) From Golem to Code: AI and Male Fantasies of Self-Engendering by Isabelle Collet WiMLDS_Paris Du Golem au code : l'IA et les fantasmes masculins d'autoengendrement, by Isabelle Collet, Associated Professor at University of Geneva, author of "Les oubliées du numérique" L'histoire occidentale est remplie d'une longue série de mythes parlant de créatures artificielles et, dans certains cas, d'humains tentant d'usurper la place de Dieu en se lançant dans le processus de création. Il y a 20 ans, quand j'ai commencé à travailler sur les questions de genre en informatique, je me suis intéressée à créatures artificielles, car, pour moi, l'ordinateur a été rêvé comme faisant partie de cette grande famille. Quand ils ont conçu l'ENIAC, les pères de l'informatique ne cherchaient pas réellement à produire une grosse machine pour calculer, même si c'est ce qu'ils ont réalisé. L'ordinateur des années 1950, qui était pourtant très loin des performances de ChatGPT, était vu comme une étape vers le but ultime de la science : une duplication du cerveau humain. Si je relie ces fantasmes à la question « Genre », c'est parce que tous les créateurs de créatures artificielles sont des hommes et que tous trouvent une solution pour créer un être nouveau sans passer par la reproduction sexuée, c'est-à-dire sans l'aide des femmes. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/10wim-241015132959-f1da7e7a-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Du Golem au code : l&#39;IA et les fantasmes masculins d&#39;autoengendrement, by Isabelle Collet, Associated Professor at University of Geneva, author of &quot;Les oubliées du numérique&quot; L&#39;histoire occidentale est remplie d&#39;une longue série de mythes parlant de créatures artificielles et, dans certains cas, d&#39;humains tentant d&#39;usurper la place de Dieu en se lançant dans le processus de création. Il y a 20 ans, quand j&#39;ai commencé à travailler sur les questions de genre en informatique, je me suis intéressée à créatures artificielles, car, pour moi, l&#39;ordinateur a été rêvé comme faisant partie de cette grande famille. Quand ils ont conçu l&#39;ENIAC, les pères de l&#39;informatique ne cherchaient pas réellement à produire une grosse machine pour calculer, même si c&#39;est ce qu&#39;ils ont réalisé. L&#39;ordinateur des années 1950, qui était pourtant très loin des performances de ChatGPT, était vu comme une étape vers le but ultime de la science : une duplication du cerveau humain. Si je relie ces fantasmes à la question « Genre », c&#39;est parce que tous les créateurs de créatures artificielles sont des hommes et que tous trouvent une solution pour créer un être nouveau sans passer par la reproduction sexuée, c&#39;est-à-dire sans l&#39;aide des femmes.
from Paris Women in Machine Learning and Data Science
]]> 60 0 https://cdn.slidesharecdn.com/ss_thumbnails/10wim-241015132959-f1da7e7a-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 From Machine Learning Scientist to Full Stack Data Scientist: Lessons learned for ML in production by Anaël Beaugnon /slideshow/from-machine-learning-scientist-to-full-stack-data-scientist-lessons-learned-for-ml-in-production-by-anael-beaugnon/269690095 frommachinelearningscientisttofullstackdatascientist-240614194735-eab9ea39
Anaël Beaugnon did a presentation called "From Machine Learning Scientist to Full Stack Data Scientist: Lessons learned for ML in production" at a joint meetup between WiMLDS Paris and MLOps Paris on june 2024. ]]>
Anaël Beaugnon did a presentation called "From Machine Learning Scientist to Full Stack Data Scientist: Lessons learned for ML in production" at a joint meetup between WiMLDS Paris and MLOps Paris on june 2024. ]]> Fri, 14 Jun 2024 19:47:34 GMT /slideshow/from-machine-learning-scientist-to-full-stack-data-scientist-lessons-learned-for-ml-in-production-by-anael-beaugnon/269690095 WiMLDS_Paris@slideshare.net(WiMLDS_Paris) From Machine Learning Scientist to Full Stack Data Scientist: Lessons learned for ML in production by Anaël Beaugnon WiMLDS_Paris Anaël Beaugnon did a presentation called "From Machine Learning Scientist to Full Stack Data Scientist: Lessons learned for ML in production" at a joint meetup between WiMLDS Paris and MLOps Paris on june 2024. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/frommachinelearningscientisttofullstackdatascientist-240614194735-eab9ea39-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Anaël Beaugnon did a presentation called &quot;From Machine Learning Scientist to Full Stack Data Scientist: Lessons learned for ML in production&quot; at a joint meetup between WiMLDS Paris and MLOps Paris on june 2024.
From Machine Learning Scientist to Full Stack Data Scientist: Lessons learned for ML in production by Anaè°·l Beaugnon from Paris Women in Machine Learning and Data Science
]]> 22 0 https://cdn.slidesharecdn.com/ss_thumbnails/frommachinelearningscientisttofullstackdatascientist-240614194735-eab9ea39-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 CI CD in the age of machine learning by Sofia Calcagno /slideshow/ci-cd-in-the-age-of-machine-learning-by-sofia-calcagno/269690057 cicdwithml-240614194234-29949b35
Sofia Calcagno presented CI/CD in the age of machine learning during a joint meetup between WiMLDS Paris and MLOps Paris on June 2024. ]]>
Sofia Calcagno presented CI/CD in the age of machine learning during a joint meetup between WiMLDS Paris and MLOps Paris on June 2024. ]]> Fri, 14 Jun 2024 19:42:33 GMT /slideshow/ci-cd-in-the-age-of-machine-learning-by-sofia-calcagno/269690057 WiMLDS_Paris@slideshare.net(WiMLDS_Paris) CI CD in the age of machine learning by Sofia Calcagno WiMLDS_Paris Sofia Calcagno presented CI/CD in the age of machine learning during a joint meetup between WiMLDS Paris and MLOps Paris on June 2024. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cicdwithml-240614194234-29949b35-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Sofia Calcagno presented CI/CD in the age of machine learning during a joint meetup between WiMLDS Paris and MLOps Paris on June 2024.
CI CD in the age of machine learning by Sofia Calcagno from Paris Women in Machine Learning and Data Science
]]> 27 0 https://cdn.slidesharecdn.com/ss_thumbnails/cicdwithml-240614194234-29949b35-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 Sequential and reinforcement learning for demand side management by Margaux Brégère /slideshow/sequential-and-reinforcement-learning-for-demand-side-management-by-margaux-bregere/267764166 2-margauxbregere-240503141339-5c5c8826
As electricity is difficult to store, it is crucial to strictly maintain the balance between production and consumption. The integration of intermittent renewable energies into the production mix has made the management of the balance more complex. However, access to near real-time data and communication with consumers via smart meters suggest demand response. Specifically, sending signals would encourage users to adjust their consumption according to the production of electricity. The algorithms used to select these signals must learn consumer reactions and optimize them while balancing exploration and exploitation. Various sequential or reinforcement learning approaches are being considered.]]>
As electricity is difficult to store, it is crucial to strictly maintain the balance between production and consumption. The integration of intermittent renewable energies into the production mix has made the management of the balance more complex. However, access to near real-time data and communication with consumers via smart meters suggest demand response. Specifically, sending signals would encourage users to adjust their consumption according to the production of electricity. The algorithms used to select these signals must learn consumer reactions and optimize them while balancing exploration and exploitation. Various sequential or reinforcement learning approaches are being considered.]]> Fri, 03 May 2024 14:13:39 GMT /slideshow/sequential-and-reinforcement-learning-for-demand-side-management-by-margaux-bregere/267764166 WiMLDS_Paris@slideshare.net(WiMLDS_Paris) Sequential and reinforcement learning for demand side management by Margaux Brégère WiMLDS_Paris As electricity is difficult to store, it is crucial to strictly maintain the balance between production and consumption. The integration of intermittent renewable energies into the production mix has made the management of the balance more complex. However, access to near real-time data and communication with consumers via smart meters suggest demand response. Specifically, sending signals would encourage users to adjust their consumption according to the production of electricity. The algorithms used to select these signals must learn consumer reactions and optimize them while balancing exploration and exploitation. Various sequential or reinforcement learning approaches are being considered. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2-margauxbregere-240503141339-5c5c8826-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> As electricity is difficult to store, it is crucial to strictly maintain the balance between production and consumption. The integration of intermittent renewable energies into the production mix has made the management of the balance more complex. However, access to near real-time data and communication with consumers via smart meters suggest demand response. Specifically, sending signals would encourage users to adjust their consumption according to the production of electricity. The algorithms used to select these signals must learn consumer reactions and optimize them while balancing exploration and exploitation. Various sequential or reinforcement learning approaches are being considered.
Sequential and reinforcement learning for demand side management by Margaux Br辿g竪re from Paris Women in Machine Learning and Data Science
]]> 13 0 https://cdn.slidesharecdn.com/ss_thumbnails/2-margauxbregere-240503141339-5c5c8826-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 How and why AI should fight cybersexism, by Chloe Daudier /slideshow/how-and-why-ai-should-fight-cybersexism-by-chloe-daudier/267762801 3-chloedaudier-cybersexism-240503133541-af2168da
Online violence amplifies IRL discriminations, and the lack of diversity grows in a vicious circle. Understanding cyber-violence, its forms and mechanisms, can help us fight back. To process massive volumes of data, AI finally comes into play for good.]]>
Online violence amplifies IRL discriminations, and the lack of diversity grows in a vicious circle. Understanding cyber-violence, its forms and mechanisms, can help us fight back. To process massive volumes of data, AI finally comes into play for good.]]> Fri, 03 May 2024 13:35:41 GMT /slideshow/how-and-why-ai-should-fight-cybersexism-by-chloe-daudier/267762801 WiMLDS_Paris@slideshare.net(WiMLDS_Paris) How and why AI should fight cybersexism, by Chloe Daudier WiMLDS_Paris Online violence amplifies IRL discriminations, and the lack of diversity grows in a vicious circle. Understanding cyber-violence, its forms and mechanisms, can help us fight back. To process massive volumes of data, AI finally comes into play for good. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/3-chloedaudier-cybersexism-240503133541-af2168da-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Online violence amplifies IRL discriminations, and the lack of diversity grows in a vicious circle. Understanding cyber-violence, its forms and mechanisms, can help us fight back. To process massive volumes of data, AI finally comes into play for good.
How and why AI should fight cybersexism, by Chloe Daudier from Paris Women in Machine Learning and Data Science
]]> 13 0 https://cdn.slidesharecdn.com/ss_thumbnails/3-chloedaudier-cybersexism-240503133541-af2168da-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 Anomaly detection and data imputation within time series /slideshow/anomaly-detection-and-data-imputation-within-time-series/267730958 1-djohraiberraken-240502125447-07183e12
In the energy sector, the use of temporal data stands as a pivotal topic. At GRDF, we have developed several methods to effectively handle such data. This presentation will specifically delve into our approaches for anomaly detection and data imputation within time series, leveraging transformers and adversarial training techniques.]]>
In the energy sector, the use of temporal data stands as a pivotal topic. At GRDF, we have developed several methods to effectively handle such data. This presentation will specifically delve into our approaches for anomaly detection and data imputation within time series, leveraging transformers and adversarial training techniques.]]> Thu, 02 May 2024 12:54:46 GMT /slideshow/anomaly-detection-and-data-imputation-within-time-series/267730958 WiMLDS_Paris@slideshare.net(WiMLDS_Paris) Anomaly detection and data imputation within time series WiMLDS_Paris In the energy sector, the use of temporal data stands as a pivotal topic. At GRDF, we have developed several methods to effectively handle such data. This presentation will specifically delve into our approaches for anomaly detection and data imputation within time series, leveraging transformers and adversarial training techniques. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/1-djohraiberraken-240502125447-07183e12-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In the energy sector, the use of temporal data stands as a pivotal topic. At GRDF, we have developed several methods to effectively handle such data. This presentation will specifically delve into our approaches for anomaly detection and data imputation within time series, leveraging transformers and adversarial training techniques.
Anomaly detection and data imputation within time series from Paris Women in Machine Learning and Data Science
]]> 59 0 https://cdn.slidesharecdn.com/ss_thumbnails/1-djohraiberraken-240502125447-07183e12-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 Managing international tech teams, by Natasha Dimban /slideshow/managing-international-tech-teams-by-natasha-dimban/266281566 managinginternationaltechteams-240212224418-6fa3ffe6
Natasha shares her experience to delve into the complexities, challenges, and strategies associated with effectively leading tech teams dispersed across borders.]]>
Natasha shares her experience to delve into the complexities, challenges, and strategies associated with effectively leading tech teams dispersed across borders.]]> Mon, 12 Feb 2024 22:44:17 GMT /slideshow/managing-international-tech-teams-by-natasha-dimban/266281566 WiMLDS_Paris@slideshare.net(WiMLDS_Paris) Managing international tech teams, by Natasha Dimban WiMLDS_Paris Natasha shares her experience to delve into the complexities, challenges, and strategies associated with effectively leading tech teams dispersed across borders. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/managinginternationaltechteams-240212224418-6fa3ffe6-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Natasha shares her experience to delve into the complexities, challenges, and strategies associated with effectively leading tech teams dispersed across borders.
Managing international tech teams, by Natasha Dimban from Paris Women in Machine Learning and Data Science
]]> 20 0 https://cdn.slidesharecdn.com/ss_thumbnails/managinginternationaltechteams-240212224418-6fa3ffe6-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 Optimizing GenAI apps, by N. El Mawass and Maria Knorps /slideshow/optimizing-genai-apps-by-n-el-mawass-and-maria-knorps/266260992 wimldsparis2024nourmaria-240211131134-27904074
Nour and Maria present the work they did at Tweag, Modus Create innovation arm, where the GenAI team developed an evaluation framework for Retrieval-Augmented Generation (RAG) systems. RAG systems provide an easy and low-cost way to extend the knowledge of Large Language Models (LLMs) but measuring their performance is not an easy task. The presentation will review existing evaluation frameworks, ranging from those based on the traditional ML approach of using groundtruth datasets, including Tweag's, to those that use LLMs to compute evaluation metrics. It will also delve into the practical implementation of Tweag's chatbot over two distinct documents datasets and provide insights on chunking, embedding and how open source and commercial LLMs compare.]]>
Nour and Maria present the work they did at Tweag, Modus Create innovation arm, where the GenAI team developed an evaluation framework for Retrieval-Augmented Generation (RAG) systems. RAG systems provide an easy and low-cost way to extend the knowledge of Large Language Models (LLMs) but measuring their performance is not an easy task. The presentation will review existing evaluation frameworks, ranging from those based on the traditional ML approach of using groundtruth datasets, including Tweag's, to those that use LLMs to compute evaluation metrics. It will also delve into the practical implementation of Tweag's chatbot over two distinct documents datasets and provide insights on chunking, embedding and how open source and commercial LLMs compare.]]> Sun, 11 Feb 2024 13:11:34 GMT /slideshow/optimizing-genai-apps-by-n-el-mawass-and-maria-knorps/266260992 WiMLDS_Paris@slideshare.net(WiMLDS_Paris) Optimizing GenAI apps, by N. El Mawass and Maria Knorps WiMLDS_Paris Nour and Maria present the work they did at Tweag, Modus Create innovation arm, where the GenAI team developed an evaluation framework for Retrieval-Augmented Generation (RAG) systems. RAG systems provide an easy and low-cost way to extend the knowledge of Large Language Models (LLMs) but measuring their performance is not an easy task. The presentation will review existing evaluation frameworks, ranging from those based on the traditional ML approach of using groundtruth datasets, including Tweag's, to those that use LLMs to compute evaluation metrics. It will also delve into the practical implementation of Tweag's chatbot over two distinct documents datasets and provide insights on chunking, embedding and how open source and commercial LLMs compare. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/wimldsparis2024nourmaria-240211131134-27904074-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Nour and Maria present the work they did at Tweag, Modus Create innovation arm, where the GenAI team developed an evaluation framework for Retrieval-Augmented Generation (RAG) systems. RAG systems provide an easy and low-cost way to extend the knowledge of Large Language Models (LLMs) but measuring their performance is not an easy task. The presentation will review existing evaluation frameworks, ranging from those based on the traditional ML approach of using groundtruth datasets, including Tweag&#39;s, to those that use LLMs to compute evaluation metrics. It will also delve into the practical implementation of Tweag&#39;s chatbot over two distinct documents datasets and provide insights on chunking, embedding and how open source and commercial LLMs compare.
Optimizing GenAI apps, by N. El Mawass and Maria Knorps from Paris Women in Machine Learning and Data Science
]]> 134 0 https://cdn.slidesharecdn.com/ss_thumbnails/wimldsparis2024nourmaria-240211131134-27904074-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 Perspectives, by M. Pannegeon https://fr.slideshare.net/slideshow/perspectives-by-m-pannegeon/266260948 20240207wimllltsupport-240211130815-671d1d5e
A journey in Longlife training by Morgane Pannegeon]]>
A journey in Longlife training by Morgane Pannegeon]]> Sun, 11 Feb 2024 13:08:15 GMT https://fr.slideshare.net/slideshow/perspectives-by-m-pannegeon/266260948 WiMLDS_Paris@slideshare.net(WiMLDS_Paris) Perspectives, by M. Pannegeon WiMLDS_Paris A journey in Longlife training by Morgane Pannegeon <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20240207wimllltsupport-240211130815-671d1d5e-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> A journey in Longlife training by Morgane Pannegeon
from Paris Women in Machine Learning and Data Science
]]> 14 0 https://cdn.slidesharecdn.com/ss_thumbnails/20240207wimllltsupport-240211130815-671d1d5e-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 Evaluation strategies for dealing with partially labelled or unlabelled data /WiMLDS_Paris/evaluation-strategies-for-dealing-with-partially-labelled-or-unlabelled-data slides-speaker1-231209153659-23c75752
Sharone Dayan, Machine Learning Engineer and Daria Stefic, Data Scientist, both from Contentsquare, delve into evaluation strategies for dealing with partially labelled or unlabelled data.]]>
Sharone Dayan, Machine Learning Engineer and Daria Stefic, Data Scientist, both from Contentsquare, delve into evaluation strategies for dealing with partially labelled or unlabelled data.]]> Sat, 09 Dec 2023 15:36:59 GMT /WiMLDS_Paris/evaluation-strategies-for-dealing-with-partially-labelled-or-unlabelled-data WiMLDS_Paris@slideshare.net(WiMLDS_Paris) Evaluation strategies for dealing with partially labelled or unlabelled data WiMLDS_Paris Sharone Dayan, Machine Learning Engineer and Daria Stefic, Data Scientist, both from Contentsquare, delve into evaluation strategies for dealing with partially labelled or unlabelled data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slides-speaker1-231209153659-23c75752-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Sharone Dayan, Machine Learning Engineer and Daria Stefic, Data Scientist, both from Contentsquare, delve into evaluation strategies for dealing with partially labelled or unlabelled data.
Evaluation strategies for dealing with partially labelled or unlabelled data from Paris Women in Machine Learning and Data Science
]]> 26 0 https://cdn.slidesharecdn.com/ss_thumbnails/slides-speaker1-231209153659-23c75752-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 Combinatorial Optimisation with Policy Adaptation using latent Space Search, by Shikha Surana /slideshow/combinatorial-optimisation-with-policy-adaptation-using-latent-space-search-by-shikha-surana/264481426 slides-speaker2-231209153349-3c85e390
Shikha presents the COMPASS framework, detailed in a paper recently submitted and accepted at NeurIPS 2023.]]>
Shikha presents the COMPASS framework, detailed in a paper recently submitted and accepted at NeurIPS 2023.]]> Sat, 09 Dec 2023 15:33:49 GMT /slideshow/combinatorial-optimisation-with-policy-adaptation-using-latent-space-search-by-shikha-surana/264481426 WiMLDS_Paris@slideshare.net(WiMLDS_Paris) Combinatorial Optimisation with Policy Adaptation using latent Space Search, by Shikha Surana WiMLDS_Paris Shikha presents the COMPASS framework, detailed in a paper recently submitted and accepted at NeurIPS 2023. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slides-speaker2-231209153349-3c85e390-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Shikha presents the COMPASS framework, detailed in a paper recently submitted and accepted at NeurIPS 2023.
Combinatorial Optimisation with Policy Adaptation using latent Space Search, by Shikha Surana from Paris Women in Machine Learning and Data Science
]]> 25 0 https://cdn.slidesharecdn.com/ss_thumbnails/slides-speaker2-231209153349-3c85e390-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 An age-old question, by Caroline Jean-Pierre /slideshow/an-ageold-question-by-caroline-jeanpierre/264481182 20231130-slides-speaker3-231209152655-58c01f23
Caroline Jean-Pierre took the stage to shed light on a pressing issue within the tech industry: ageism.]]>
Caroline Jean-Pierre took the stage to shed light on a pressing issue within the tech industry: ageism.]]> Sat, 09 Dec 2023 15:26:55 GMT /slideshow/an-ageold-question-by-caroline-jeanpierre/264481182 WiMLDS_Paris@slideshare.net(WiMLDS_Paris) An age-old question, by Caroline Jean-Pierre WiMLDS_Paris Caroline Jean-Pierre took the stage to shed light on a pressing issue within the tech industry: ageism. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20231130-slides-speaker3-231209152655-58c01f23-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Caroline Jean-Pierre took the stage to shed light on a pressing issue within the tech industry: ageism.
An age-old question, by Caroline Jean-Pierre from Paris Women in Machine Learning and Data Science
]]> 11 0 https://cdn.slidesharecdn.com/ss_thumbnails/20231130-slides-speaker3-231209152655-58c01f23-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 Applying Churn Prediction Approaches to the Telecom Industry, by Joëlle Lautré /slideshow/applying-churn-prediction-approaches-to-the-telecom-industry-by-jolle-lautr/261686687 20230927wimldschurnpredictionapproachestelecomjlautrepdf1-231002140255-f0fed373
Churn prediction, one among many use cases Joelle's team addresses, is the central focus of Joëlle's presentation.]]>
Churn prediction, one among many use cases Joelle's team addresses, is the central focus of Joëlle's presentation.]]> Mon, 02 Oct 2023 14:02:54 GMT /slideshow/applying-churn-prediction-approaches-to-the-telecom-industry-by-jolle-lautr/261686687 WiMLDS_Paris@slideshare.net(WiMLDS_Paris) Applying Churn Prediction Approaches to the Telecom Industry, by Joëlle Lautré WiMLDS_Paris Churn prediction, one among many use cases Joelle's team addresses, is the central focus of Joëlle's presentation. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20230927wimldschurnpredictionapproachestelecomjlautrepdf1-231002140255-f0fed373-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Churn prediction, one among many use cases Joelle&#39;s team addresses, is the central focus of Joëlle&#39;s presentation.
Applying Churn Prediction Approaches to the Telecom Industry, by Jo谷lle Lautr辿 from Paris Women in Machine Learning and Data Science
]]> 57 0 https://cdn.slidesharecdn.com/ss_thumbnails/20230927wimldschurnpredictionapproachestelecomjlautrepdf1-231002140255-f0fed373-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 How to supervise a thesis in NLP in the ChatGPT era? By Laure Soulier /slideshow/how-to-supervise-a-thesis-in-nlp-in-the-chatgpt-era-by-laure-soulier/261662783 wimlds-27sept2023-231001200729-e54f53fa
Laure talked about a very hot topic in the community at the moment with the ChatGPT phenomenon: how to supervise a PhD thesis in NLP in the age of Large Language Models (LLMs)?]]>
Laure talked about a very hot topic in the community at the moment with the ChatGPT phenomenon: how to supervise a PhD thesis in NLP in the age of Large Language Models (LLMs)?]]> Sun, 01 Oct 2023 20:07:29 GMT /slideshow/how-to-supervise-a-thesis-in-nlp-in-the-chatgpt-era-by-laure-soulier/261662783 WiMLDS_Paris@slideshare.net(WiMLDS_Paris) How to supervise a thesis in NLP in the ChatGPT era? By Laure Soulier WiMLDS_Paris Laure talked about a very hot topic in the community at the moment with the ChatGPT phenomenon: how to supervise a PhD thesis in NLP in the age of Large Language Models (LLMs)? <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/wimlds-27sept2023-231001200729-e54f53fa-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Laure talked about a very hot topic in the community at the moment with the ChatGPT phenomenon: how to supervise a PhD thesis in NLP in the age of Large Language Models (LLMs)?
How to supervise a thesis in NLP in the ChatGPT era? By Laure Soulier from Paris Women in Machine Learning and Data Science
]]> 70 0 https://cdn.slidesharecdn.com/ss_thumbnails/wimlds-27sept2023-231001200729-e54f53fa-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 Global Ambitions Local Realities, by Anna Abreu /slideshow/global-ambitions-local-realities-by-anna-abreu/261662765 wids-globalambitionslocalrealities-231001200301-b603114e
Anna Abreu, a Data Analyst at Amazon guide our international community on the topic of finding a job in France as a foreigner.]]>
Anna Abreu, a Data Analyst at Amazon guide our international community on the topic of finding a job in France as a foreigner.]]> Sun, 01 Oct 2023 20:03:01 GMT /slideshow/global-ambitions-local-realities-by-anna-abreu/261662765 WiMLDS_Paris@slideshare.net(WiMLDS_Paris) Global Ambitions Local Realities, by Anna Abreu WiMLDS_Paris Anna Abreu, a Data Analyst at Amazon guide our international community on the topic of finding a job in France as a foreigner. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/wids-globalambitionslocalrealities-231001200301-b603114e-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Anna Abreu, a Data Analyst at Amazon guide our international community on the topic of finding a job in France as a foreigner.
Global Ambitions Local Realities, by Anna Abreu from Paris Women in Machine Learning and Data Science
]]> 53 0 https://cdn.slidesharecdn.com/ss_thumbnails/wids-globalambitionslocalrealities-231001200301-b603114e-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 Plug-and-Play methods for inverse problems in imagine, by Julie Delon /slideshow/plugandplay-methods-for-inverse-problems-in-imagine-by-julie-delon/258582304 202306wimldspnp-230623150644-cc60a99a
Julie Delon speaks about imaging science and presents the Plug and Play approach to address inverse problems related to image and video restoration.]]>
Julie Delon speaks about imaging science and presents the Plug and Play approach to address inverse problems related to image and video restoration.]]> Fri, 23 Jun 2023 15:06:44 GMT /slideshow/plugandplay-methods-for-inverse-problems-in-imagine-by-julie-delon/258582304 WiMLDS_Paris@slideshare.net(WiMLDS_Paris) Plug-and-Play methods for inverse problems in imagine, by Julie Delon WiMLDS_Paris Julie Delon speaks about imaging science and presents the Plug and Play approach to address inverse problems related to image and video restoration. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/202306wimldspnp-230623150644-cc60a99a-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Julie Delon speaks about imaging science and presents the Plug and Play approach to address inverse problems related to image and video restoration.
Plug-and-Play methods for inverse problems in imagine, by Julie Delon from Paris Women in Machine Learning and Data Science
]]> 55 0 https://cdn.slidesharecdn.com/ss_thumbnails/202306wimldspnp-230623150644-cc60a99a-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 Sales Forecasting as a Data Product by Francesca Iannuzzi /slideshow/sales-forecasting-as-a-data-product-by-francesca-iannuzzi/258563175 fiannuzzi-230622144046-904f37a1
Abstract: Francesca describes with a lot of humour a 3 year journey to develop a full sales forecasting product.]]>
Abstract: Francesca describes with a lot of humour a 3 year journey to develop a full sales forecasting product.]]> Thu, 22 Jun 2023 14:40:46 GMT /slideshow/sales-forecasting-as-a-data-product-by-francesca-iannuzzi/258563175 WiMLDS_Paris@slideshare.net(WiMLDS_Paris) Sales Forecasting as a Data Product by Francesca Iannuzzi WiMLDS_Paris Abstract: Francesca describes with a lot of humour a 3 year journey to develop a full sales forecasting product. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/fiannuzzi-230622144046-904f37a1-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Abstract: Francesca describes with a lot of humour a 3 year journey to develop a full sales forecasting product.
Sales Forecasting as a Data Product by Francesca Iannuzzi from Paris Women in Machine Learning and Data Science
]]> 33 0 https://cdn.slidesharecdn.com/ss_thumbnails/fiannuzzi-230622144046-904f37a1-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 Identifying and mitigating bias in machine learning, by Ruta Binkyte /slideshow/identifying-and-mitigating-bias-in-machine-learning-by-ruta-binkyte/258562832 fairairutabinkyte-230622141747-cd3e5ac0
Asbtract: Ruta Binkyte, doctoral researcher at Inria Saclay, Ecole Polytechnique presents an interesting topic: identifying and mitigating bias in machine learning.]]>
Asbtract: Ruta Binkyte, doctoral researcher at Inria Saclay, Ecole Polytechnique presents an interesting topic: identifying and mitigating bias in machine learning.]]> Thu, 22 Jun 2023 14:17:47 GMT /slideshow/identifying-and-mitigating-bias-in-machine-learning-by-ruta-binkyte/258562832 WiMLDS_Paris@slideshare.net(WiMLDS_Paris) Identifying and mitigating bias in machine learning, by Ruta Binkyte WiMLDS_Paris Asbtract: Ruta Binkyte, doctoral researcher at Inria Saclay, Ecole Polytechnique presents an interesting topic: identifying and mitigating bias in machine learning. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/fairairutabinkyte-230622141747-cd3e5ac0-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Asbtract: Ruta Binkyte, doctoral researcher at Inria Saclay, Ecole Polytechnique presents an interesting topic: identifying and mitigating bias in machine learning.
Identifying and mitigating bias in machine learning, by Ruta Binkyte from Paris Women in Machine Learning and Data Science
]]> 89 0 https://cdn.slidesharecdn.com/ss_thumbnails/fairairutabinkyte-230622141747-cd3e5ac0-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 “Turning your ML algorithms into full web apps in no time with Python" by Marine Gosselin, Developer Advocate @taipy_io /slideshow/turning-your-ml-algorithms-into-full-web-apps-in-no-time-with-python-by-marine-gosselin-developer-advocate-taipyio/257158395 turningyourdataaialgorithmsintofullwebappsinnotimewithtaipy-230404111916-e0e6b0c3
Abstract: Who hasn't heard of the "Pilot Syndrome"? 85% of Data Science Pilots remain pilots and do not make it to the production stage. Let's build a production-ready and end-user-friendly Data Science application. 100% python and 100% open source. Phase 1 | Building the GUI: create an interactive and powerful interface in a few lines of code Phase 2 | Integrated back end: Manage your models and pipelines and create scenarios the smart way]]>
Abstract: Who hasn't heard of the "Pilot Syndrome"? 85% of Data Science Pilots remain pilots and do not make it to the production stage. Let's build a production-ready and end-user-friendly Data Science application. 100% python and 100% open source. Phase 1 | Building the GUI: create an interactive and powerful interface in a few lines of code Phase 2 | Integrated back end: Manage your models and pipelines and create scenarios the smart way]]> Tue, 04 Apr 2023 11:19:16 GMT /slideshow/turning-your-ml-algorithms-into-full-web-apps-in-no-time-with-python-by-marine-gosselin-developer-advocate-taipyio/257158395 WiMLDS_Paris@slideshare.net(WiMLDS_Paris) “Turning your ML algorithms into full web apps in no time with Python" by Marine Gosselin, Developer Advocate @taipy_io WiMLDS_Paris Abstract: Who hasn't heard of the "Pilot Syndrome"? 85% of Data Science Pilots remain pilots and do not make it to the production stage. Let's build a production-ready and end-user-friendly Data Science application. 100% python and 100% open source. Phase 1 | Building the GUI: create an interactive and powerful interface in a few lines of code Phase 2 | Integrated back end: Manage your models and pipelines and create scenarios the smart way <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/turningyourdataaialgorithmsintofullwebappsinnotimewithtaipy-230404111916-e0e6b0c3-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Abstract: Who hasn&#39;t heard of the &quot;Pilot Syndrome&quot;? 85% of Data Science Pilots remain pilots and do not make it to the production stage. Let&#39;s build a production-ready and end-user-friendly Data Science application. 100% python and 100% open source. Phase 1 | Building the GUI: create an interactive and powerful interface in a few lines of code Phase 2 | Integrated back end: Manage your models and pipelines and create scenarios the smart way
“Turning your ML algorithms into full web apps in no time with Python" by Marine Gosselin, Developer Advocate @taipy_io from Paris Women in Machine Learning and Data Science
]]> 130 0 https://cdn.slidesharecdn.com/ss_thumbnails/turningyourdataaialgorithmsintofullwebappsinnotimewithtaipy-230404111916-e0e6b0c3-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-WiMLDS_Paris-48x48.jpg?cb=1728998946 The Women in Machine Learning & Data Science (WiML&DS) Conference aims to inspire and educate data scientists, regardless of gender, and support women in the field. ​This technical conference provides an opportunity to hear about the latest data science related research in a number of domains, learn how leading-edge companies are leveraging data science for success, and connect with potential mentors, collaborators, and others in the field. twitter.com/WiMLDS_Paris https://cdn.slidesharecdn.com/ss_thumbnails/presentation-241015133209-4bfbc297-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/low-rank-optimisation-by-irene-waldspurger/272433132 Low Rank Optimisation,... https://cdn.slidesharecdn.com/ss_thumbnails/10wim-241015132959-f1da7e7a-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/from-golem-to-code-ai-and-male-fantasies-of-self-engendering-by-isabelle-collet/272433087 From Golem to Code: AI... https://cdn.slidesharecdn.com/ss_thumbnails/frommachinelearningscientisttofullstackdatascientist-240614194735-eab9ea39-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/from-machine-learning-scientist-to-full-stack-data-scientist-lessons-learned-for-ml-in-production-by-anael-beaugnon/269690095 From Machine Learning ...