Andrew Robert Munn Vidéos
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2024-05-10
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Robert Crowe Crowe Goto Tierney Munn Görner 2019
This presentation was recorded at GOTO Copenhagen 2019. #GOTOcon #GOTOcph (http•••) Robert Crowe - TensorFlow Developer Advocate ORIGINAL TALK TITLE Taking Machine Learning Research to Production: Solving Real Problems ABSTRACT Most of the focus in the ML community is on research, which is exciting and important. Equally important however is bringing that research to production applications to solve real-world problems, but the issues and approaches for doing that are often poorly understood. An ML application in production must address all of the issues of modern software development methodology, as well as issues unique to ML and data science. Often ML applications are developed and trained using tools like notebooks and suffer from inherent limitations in testability, scalability across clusters, training/serving skew, and the modularity and reusability of components. In addition, ML application measurement often emphasizes top level metrics, leading to issues in model fairness as well as predictive performance across user segments. The user experience of any ML application is unique to the model’s performance on that user’s input data, so if the model doesn’t perform well on that particular data segment then the user has a poor experience. We discuss the use of ML pipeline architectures for implementing production ML applications, and in particular we review Google’s experience with TensorFlow Extended (TFX). Google uses TFX for large scale ML applications, and offers an open-source version to the community. TFX scales to very large training sets and very high request volumes, and enables strong software methodology including testability, hot versioning, and deep performance analysis. Robert Crowe is a data scientist and TFX Developer Advocate at Google and will discuss how developers can move their ML [...] Download slides and read the full abstract here: (http•••) RECOMMENDED BOOKS Holden Karau, Trevor Grant, Boris Lublinsky, Richard Liu & Ilan Filonenko • Kubeflow for Machine Learning • (http•••) Phil Winder • Reinforcement Learning • (http•••) Kelleher & Tierney • Data Science (The MIT Press Essential Knowledge series) • (http•••) Lakshmanan, Robinson & Munn • Machine Learning Design Patterns • (http•••) Lakshmanan, Görner & Gillard • Practical Machine Learning for Computer Vision • (http•••) Aurélien Géron • Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow • (http•••) (http•••) (http•••) (http•••) #ML #TensorFlow #TFX #TensorFlowExtended #MachineLearning Looking for a unique learning experience? Attend the next GOTO Conference near you! Get your ticket at (http•••) SUBSCRIBE TO OUR CHANNEL - new videos posted almost daily. (http•••)
Robert Crowe Crowe Goto Tales Tierney Munn Görner 1643 1689 1820 1916 2021
This presentation was recorded at GOTOpia February 2021. #GOTOcon #GOTOpia (http•••) Robert Crowe - TensorFlow Developer Advocate at Google ABSTRACT A machine learning (ML) journey typically starts with trying to understand the world, and looking for data that describes it. This leads to an experimentation phase, where we try to use that data to model the parts of the world that we’re interested in, often because they directly affect our users or our business. Once we have one or more models that deliver good results, it’s time to move those models into production. Deploying advanced machine learning technology to serve customers and/or business needs requires a rigorous approach and production-ready systems. This is especially true for maintaining and improving model performance over the lifetime of a production application. Unfortunately, the issues involved and approaches available are often poorly understood. A ML application in production must address all of the issues of modern software development methodology, as well as issues unique to ML and data science. Often ML applications are developed using tools and systems which suffer from inherent limitations in testability, scalability across clusters, training/serving skew, and the modularity and reusability of components. In addition, ML application measurement often emphasizes top level metrics, leading to issues in model fairness as well as predictive performance across user segments. In this talk, Robert will discuss the use of ML pipeline architectures for implementing production ML applications, and in particular we review Google’s experience with TensorFlow Extended (TFX), as well as the advantages of containerizing pipeline architectures using platforms such as Kubeflow. Google uses TFX for large scale ML applications, and offers an open-source version to the community. TFX scales to very large training sets and very high request volumes, and enables strong software methodology [...] TIMECODES 00:00 Intro 02:15 Production ML 05:41 We need MLOps 06:21 Continuous integration, deployment and testing 07:29 MLOps level 0: Manual Process 09:02 Experiment 12:11 Tales from the trenches 13:02 TensorFlow Extended (TFX) 14:28 TFX production components 16:43 What is a TFX component? 18:20 TFX orchestration 19:16 Difference between TFX & Kubeflow pipelines 23:00 Distributed pipeline processing: Apache Beam 25:28 TFX standard components 25:53 Components: ExampleGen, StatisticsGen & SchemaGen 28:17 Components: ExampleValidator, Transform & Trainer 31:45 Components: Tuner, Evaluator & InfraValidator 32:51 Components: Pusher & BulkInferrer 33:37 TFX pipeline nodes 34:43 TRFX custom components 36:09 Very high level architecture 37:03 Outro Download slides and read the full abstract here: (http•••) RECOMMENDED BOOKS Holden Karau, Trevor Grant, Boris Lublinsky, Richard Liu & Ilan Filonenko • Kubeflow for Machine Learning • (http•••) Phil Winder • Reinforcement Learning • (http•••) Kelleher & Tierney • Data Science (The MIT Press Essential Knowledge series) • (http•••) Lakshmanan, Robinson & Munn • Machine Learning Design Patterns • (http•••) Lakshmanan, Görner & Gillard • Practical Machine Learning for Computer Vision • (http•••) Aurélien Géron • Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow • (http•••) (http•••) (http•••) (http•••) #MachineLearning #ML #TensorFlow #TF #TFX #TensorFlowExtended #Kubeflow #AI #ArtificialIntelligence #DataScience #MLOps #CI #ContinuousIntegration #Testing #Orchestration #ApacheBeam #ExampleGen #StatisticsGen #SchemaGen Looking for a unique learning experience? Attend the next GOTO conference near you! Get your ticket at (http•••)h SUBSCRIBE TO OUR CHANNEL - new videos posted almost daily. (http•••)
Huguette Tourangeau Henri Duparc Claude Debussy Handel Charles Gounod Jules Massenet Jacques Offenbach Munn Amato Richard Bonynge 1553 1822 1976
The mezzo-soprano sings the music of Duparc, Debussy, Handel, Gounod, Massenet and Offenbach in this recital from 1976 with pianist Sandra Munn. Henri Duparc - L’invitation au Voyage 0:00 - Élégie 4:00 - La vie antérieure 6:48 Claude Debussy: ("Trois Chansons de Bilitis") - La flûte de Pan 10:51 - La chevelure 13:13 - Le tombeau des Naïades 15:53 G.F. Handel: (recitative and aria from "Rodelinda") - Pompe vane di morte …. Dove sei, amato bene 18:22 Charles Gounod: - Le soir 25:43 Jules Massenet: - Nuit d'Espagne 29:43 - Pensée d'automne 32:55 ENCORE Jacques Offenbach: (from "La Périchole") - O mon cher amant 37:10 And here is a link to her recording of Massenet mélodies with Richard Bonynge at the piano: (http•••)
Arden Munn Burton Arden Ohman Orchestra 1931 1932
Over the years it was “Penthouse Serenade” by which the song most commonly came to be known. Disc courtesy of The Rick Colom Collection, from the original 78rpm: Victor 22910 - When We’re Alone (Penthouse Serenade) (Jason-Burton) by Victor Arden-Phil Ohman and Their Orchestra, vocal by Frank Munn, recorded in NYC January 14, 1932 THE 1932 HITS ARCHIVE - a collection of commercial recordings and songs that proved popular during the calendar year 1932 (some were recorded in 1931) via sales, sheet music, and radio exposure...plus some others that have gained increased recognition or have been shown to have had an impact during the decades that followed.
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- chronologie: Artistes lyriques.
- Index (par ordre alphabétique): M...