Machine Learning at Scale with H2O : A practical guide to building and deploying machine learning models on enterprise systems

Machine Learning at Scale with H2O : A practical guide to building and deploying machine learning models on enterprise systems

Enregistré dans:

Détails bibliographiques
Auteur principal:	Keys, Gregory. (Auteur)
Autres auteurs:	Whiting, David. (Auteur)
Support:	E-Book
Langue:	Anglais
Publié:	Birmingham : Packt Publishing, 2022.
Autres localisations:	Voir dans le Sudoc
Accès en ligne:	Accès à l'E-book

Documents similaires

Large scale machine learning with Python
par: Sjardin, Bastiaan.
Publié: (2016)

Practical Automated Machine Learning Using H2O.ai : Discover the power of automated machine learning, from experimentation through to deployment to production
par: Ajgaonkar, Salil.
Publié: (2022)

Machine Learning for Beginners : Build and deploy Machine Learning systems using Python (English Edition) Ed. 2
par: Bhasin, Harsh (19..-....; informaticien).

Machine Learning Engineering on AWS : Build, scale, and secure machine learning systems and MLOps pipelines in production
par: Lat, Joshua Arvin.
Publié: (2022)

Machine Learning Automation with TPOT : Build, validate, and deploy fully automated machine learning models with Python
par: Radecic, Dario.

Pragmatic Machine Learning with Python : Learn How to Deploy Machine Learning Models in Production
par: Nag, Avishek.

Machine learning for Mobile : practical guide to building intelligent mobile applications powered by machine learning
par: Gopalakrishnan, Revathi.
Publié: (2018)

Practical Machine Learning with R : Define, build, and evaluate machine learning models for real-world applications
par: Jeyaraman, Brindha Priyadarshini.
Publié: (2019)

Machine Learning in Biotechnology and Life Sciences : Build machine learning models using Python and deploy them on the cloud
par: Alkhalifa, Saleh.
Publié: (2022)

Building Machine Learning Systems with Python : master the art of machine learning with Python and build effective machine learning systems with this intensive hands-on guide
par: Richert, Willi.
Publié: (2013)

Machine learning in Java : helpful techniques to design, build, and deploy powerful machine learning applications in Java
par: Bhatia, AshishSingh.
Publié: (2018)

Large-Scale Kernel Machines
par: Bottou, Léon (1965-....).

Practical Machine Learning
par: Gollapudi, Sunila.
Publié: (2016)

Simplified Machine Learning : The essential building blocks for Machine Learning expertise
par: Sharma, Pooja.

Hands-on machine learning with Azure : build powerful models with cognitive machine learning and artificial intelligence
Publié: (2018)

Building Machine Learning Systems Using Python : Practice to Train Predictive Models and Analyze Machine Learning Results with Real Use-Cases
par: Chopra, Deepti (19..-....).

Mastering Azure Machine Learning : Execute large-scale end-to-end machine learning with Azure
par: Körner, Christoph.
Publié: (2022)

Machine Learning on Kubernetes : A practical handbook for building and using a complete open source machine learning platform on Kubernetes
par: Masood, Faisal.
Publié: (2022)

Machine Learning Engineering with Python : Manage the lifecycle of machine learning models using MLOps with practical examples
par: Mcmahon, Andrew P..

Machine Learning with R : Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data
par: Lantz, Brett.

Hands-on automated machine learning : a beginner's guide to building automated machine learning systems using AutoML and Python
par: Das, Sibanjan.
Publié: (2018)

Building Machine Learning Systems with Python : Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow
par: Coelho, Luis Pedro.
Publié: (2018)

Practical machine learning in R
par: Nwanganga, Fred (19..-....).

Practical Machine Learning Cookbook
par: Tripathi, Atul.
Publié: (2017)

Machine learning
par: Mueller, John Paul (1958-....).

Machine Learning with PyTorch and Scikit-Learn : Develop machine learning and deep learning models with Python
par: Raschka, Sebastian.
Publié: (2022)

Build, train, and deploy end-to-end machine learning and deep learning pipelines
par: Kolodiazhnyi, Kirill.
Publié: (2020)

Python Machine Learning Projects : Learn how to build Machine Learning projects from scratch
par: Vora, Deepali R..

Python Machine Learning : Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2
par: Raschka, Sebastian.
Publié: (2019)

Machine Learning for Finance : Beginner's guide to explore machine learning in banking and finance
par: Singla, Saurav.

Machine Learning with Qlik Sense : Utilize different machine learning models in practical use cases by leveraging Qlik Sense
par: Ranta, Hannu.

Scala machine learning projects : build real-world machine learning and deep learning projects with Scala
par: Karim, Rezaul Md..
Publié: (2018)

Machine Learning Algorithms : Reference guide for popular algorithms for data science and machine learning
par: Bonaccorso, Bonaccorso.
Publié: (2017)

Serverless Machine Learning with Amazon Redshift ML : Create, train, and deploy machine learning models using familiar SQL commands
par: Panda, Debu.

Mastering machine learning with scikit-learn : learning to implement and evaluate machine learning solutions with scikit-learn
par: Hackeling, Gavin.
Publié: (2017)

Operationalizing Machine Learning Pipelines : Building Reusable and Reproducible Machine Learning Pipelines Using MLOps
par: Pandey, Vishwajyoti.
Publié: (2022)

Mastering Azure Machine Learning : Perform large-scale end-to-end advanced machine learning on the cloud with Microsoft Azure ML
par: Körner, Christoph.
Publié: (2020)

Machine learning with R cookbook : analyze data and build predictive models
par: Bhatia, AshishSingh.
Publié: (2017)

Machine Learning: Make Your Own Recommender System : Build Your Recommender System with Machine Learning Insights
par: Theobald, Oliver.

Machine Learning with LightGBM and Python : A practitioner's guide to developing production-ready machine learning systems
par: Wyk, Andrich Van.

Université catholique de l'Ouest - Bibliothèques - 3 place André Leroy - BP10808 - 49008 ANGERS Cedex 1