Automated Machine Learning : Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms

Automated Machine Learning : Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms

Enregistré dans:

Détails bibliographiques
Auteur principal:	Masood, Adnan. (Auteur)
Autres auteurs:	Sherif, Ahmed. (Auteur)
Support:	E-Book
Langue:	Anglais
Publié:	Birmingham : Packt Publishing, 2021.
Autres localisations:	Voir dans le Sudoc
Accès en ligne:	Accès à l'E-book

Documents similaires

Learning F# functional data structures and algorithms
par: Masood, Adnan.
Publié: (2015)

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)

Genetic algorithms in search, optimization, and machine learning
par: Goldberg, David Edward, 1953-
Publié: (1989)

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

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

Automated Machine Learning on AWS : Fast-track the development of your production-ready machine learning applications the AWS way
par: Potgieter, Trenton.
Publié: (2022)

Optimization for machine learning
par: Sra, Suvrit (1976-....).
Publié: (2011)

Automated Machine Learning with Microsoft Azure : Build highly accurate and scalable end-to-end AI solutions with Azure AutoML
par: Sawyers, Dennis Michael.
Publié: (2021)

Machine learning algorithms : popular algorithms for data science and machine learning
par: Bonaccorso, Giuseppe.
Publié: (2018)

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 : a bayesian and optimization perspective
par: Theodoridis, Sergios.
Publié: (2015)

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

Advanced Machine Learning : Fundamentals and algorithms
par: Kumar Tyagi, Amit (19..-....).

Machine Learning : Master Supervised and Unsupervised Learning Algorithms with Real Examples
par: Doshi, Ruchi.

Machine Learning for Biometrics : Concepts, Algorithms and Applications
par: Sarangi, Partha Pratim (19..-....).
Publié: (2022)

Machine learning with the Elastic Stack : expert techniques to integrate machine learning with distributed search and analytics
par: Collier, Rich.
Publié: (2019)

Machine Learning in Microservices : Productionizing microservices architecture for machine learning solutions
par: Abouahmed, Mohamed.

Hands-on machine learning on Google cloud platform : implementing smart and efficient analytics using Cloud ML Engine
par: Ciaburro, Giuseppe (19..-....).
Publié: (2018)

Mastering Classification Algorithms for Machine Learning : Learn how to apply Classification algorithms for effective Machine Learning solutions
par: Majumdar, Partha.

Mastering machine learning algorithms : expert techniques to implement popular machine learning algorithms and fine-tune your models
par: Bonaccorso, Giuseppe.
Publié: (2018)

Graph Machine Learning : Take graph data to the next level by applying machine learning techniques and algorithms
par: Stamile, Claudio (19..-....; chercheur en informatique).
Publié: (2021)

The machine learning workshop : get ready to develop your own high-performance machine learning algorithms with scikit-learn
par: Saleh, Hyatt.
Publié: (2020)

Machine Learning and Deep Learning Algorithms : Tools and Techniques Using MATLAB and Python
par: Kumar Pandey, Abhishek.

Mastering machine learning algorithms : expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work
par: Bonaccorso, Giuseppe.
Publié: (2020)

Responsible AI in the Enterprise : Practical AI risk management for explainable, auditable, and safe models with hyperscalers and Azure OpenAI
par: Masood, Adnan.

Introduction to Algorithms for Data Mining and Machine Learning
par: Yang, Xin-She (19..-....).
Publié: (2019)

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

Learning VMware vRealize automation
par: Rajendran, Sriram.
Publié: (2016)

Cloud Native Automation with Google Cloud Build : Easily automate tasks in a fully managed, scalable, and secure platform
par: Bushong, Anthony.
Publié: (2022)

Learning robotic process automation : create software robots and automate business processes with the leading RPA tool - UiPath
par: Tripathi, Alok Mani.
Publié: (2018)

Machine Learning for finance : The practical guide to using data-driven algorithms in banking, insurance, and investments
par: Klaas, Jannes.
Publié: (2019)

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 for cybersecurity cookbook : over 80 recipes on how to implement machine learning algorithms for building security systems using Python
par: Tsukerman, Emmanuel.
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)

Hands-On Machine Learning with IBM Watson : Leverage IBM Watson to Implement Machine Learning Techniques and Algorithms Using Python
par: Miller, James D..
Publié: (2019)

Machine learning : expert techniques to tackle complex machine learning problems using Python
par: Thanaki, Jalaj.
Publié: (2018)

Fun with Machine Learning : Simplify the Data Science process by automating repetitive and complex tasks using AutoML
par: Liborious, Arockia.

Machine learning : theory and applications
par: Govindaraju, Venugopal (1964-....).
Publié: (2013)

Machine learning : the new AI
par: Alpaydin, Ethem.
Publié: (2016)

Hands-On Machine Learning with ML.NET : Getting started with Microsoft ML.NET to implement popular machine learning algorithms in C#
par: Capellman, Jarred.
Publié: (2020)

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