Machine learning : end-to-end guide for Java developers

Machine learning : end-to-end guide for Java developers

Enregistré dans:

Détails bibliographiques
Auteur principal:	Reese, Richard M.. (Auteur)
Autres auteurs:	Reese, Jennifer L.. (Auteur), Kaluža, Boštjan.
Support:	E-Book
Langue:	Anglais
Publié:	Birmingham : Packt Publishing, 2017.
Autres localisations:	Voir dans le Sudoc
Accès en ligne:	Accès à l'E-book

Documents similaires

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

Machine Learning Engineering with MLflow : Manage the end-to-end machine learning life cycle with MLflow
par: Lauchande, Natu.
Publié: (2021)

Go machine learning projects : eight projects demonstrating end-to-end machine learning and predictive analytics applications in Go
par: Chew, Xuanyi.
Publié: (2018)

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

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)

Learning network programming with Java
par: Reese, Richard M..
Publié: (2015)

Learning Java functional programming
par: Reese, Richard M..
Publié: (2015)

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

Mastering Java machine learning
par: Kamath, Dr. Uday.
Publié: (2017)

Machine Learning : Hands-On for Developers and Technical Professionals
par: Bell, Jason.

Machine learning with Core ML : an iOS developer's guide to implementing machine learning in mobile apps
par: Newnham, Joshua.
Publié: (2018)

Natural language processing with Java : techniques for building machine learning and neural network models for NLP
par: Reese, Richard M..
Publié: (2018)

Machine learning for developers : uplift your regular applications with the power of statistics, analytics, and machine learning
par: Bonnin, Rodolfo.
Publié: (2017)

Machine learning fundamentals : use Python and scikit-learn to get up and running with the hottest developments in machine learning
par: Saleh, Hyatt.
Publié: (2018)

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

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

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 with Python : Design and Develop Machine Learning and Deep Learning Technique using real world code examples
par: Vijayvargia, Abhishek.

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

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

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

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

Machine Learning for iOS Developers
par: Mishra, Abhishek.

Hands-on machine learning with JavaScript : solve complex computational web problems using machine learning
par: Kanber, Burak (19..-....; ingénieur).
Publié: (2018)

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

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

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

Supervised Machine Learning with Python : Develop rich Python coding practices while exploring supervised machine learning
par: Smith, Taylor.
Publié: (2019)

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

Synthetic Data for Machine Learning : Revolutionize your approach to machine learning with this comprehensive conceptual guide
par: Kerim, Abdulrahman.

Python machine learning Blueprints : put your machine learning concepts to the test by developing real-world smart projects
par: Combs, Alexander.
Publié: (2019)

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

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

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

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

Machine Learning with Python : Unlocking AI Potential with Python and Machine Learning
par: Theobald, Oliver.

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

Machine learning with Spark
par: Dua, Rajdeep.
Publié: (2017)

Statistics for machine learning
par: Dangeti, Pratap.
Publié: (2017)

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