Harry Tierney Vídeos
pianista, compositor, guionista
Conmemoraciones 2025 (Muerte: Harry Tierney)
- piano
- Estados Unidos
Última actualización
2024-05-12
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Bartok’s ALLEGRO BARBARO - arranged by John Spence for the Wollongong Con Community Orchestra. Collated from phone self recordings and zoom meeting footage, a reduced online version of the orchestra cautiously embraced this new approach to community music-making. Not quite the same as playing music together, but the musicians did an amazing job learning their parts and recording themselves in isolation during Term 2. Edited by David Rooney. Huge thanks to our ex-conductor, Cameron Zingel of Denmark. Thanks also to support from Timpanist, Greg Knight for wrangling the orchestra Zoom meetings, and to Con staff; Adrian Davis (conductor), John Spence (arranger), Mel Wishart (clarinet) and Luke Tierney (double bass). This project was supported by a Creative Wollongong grant. [some musicians submitted audio only and don’t appear on screen - additional images show a road trip south toward Kiama and the waves battering the Wollongong Breakwater Lighthouse]
Sharon Azrieli Merrill Michel Legrand Tierney Sutton 2022
Provided to YouTube by The Orchard Enterprises Paris Violon · Sharon Azrieli · David Merrill · Michel Legrand · Eddy Marnay Secret Places ℗ 2022 Viva! Diva! US, Inc. Released on: 2022-03-04 Producer: David Merrill Producer: Sharon Azrieli Vocal Producer: Tierney Sutton Music Arranger: Tamir Hendelman Music Publisher: SDRM Auto-generated by YouTube.
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•••)
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- cronología: Compositores (Norteamérica). Intérpretes (Norteamérica).
- Índices (por orden alfabético): T...