Machine learning with Core ML : an iOS developer's guide to implementing machine learning in mobile apps

Machine learning with Core ML : an iOS developer's guide to implementing machine learning in mobile apps

Enregistré dans:

Détails bibliographiques
Auteur principal:	Newnham, Joshua. (Auteur)
Support:	E-Book
Langue:	Anglais
Publié:	Birmingham : Packt Publishing, 2018.
Autres localisations:	Voir dans le Sudoc
Accès en ligne:	Accès à l'E-book

Documents similaires

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)

Machine Learning for iOS Developers
par: Mishra, Abhishek.

Machine learning projects for mobile applications : build Android and iOS applications using TensorFlow Lite and Core ML
par: Ng, Karthikeyan.
Publié: (2018)

Machine Learning with BigQuery ML : Create, execute, and improve machine learning models in BigQuery using standard SQL queries
par: Marrandino, Alessandro.
Publié: (2021)

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

Learn Unity ML-Agents - fundamentals of Unity Machine Learning : ncorporate new powerful ML algorithms such as Deep Reinforcement Learning for games
par: Lanham, Micheal.
Publié: (2018)

Machine learning with Swift : artificial intelligence for iOS
par: Sosnovshchenko, Alexander.
Publié: (2018)

Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter : Build scalable real-world projects to implement end-to-end neural networks on Android and iOS
par: Singh, Anubhav.
Publié: (2020)

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

Microservices for Machine Learning : Design, implement, and manage high-performance ML systems with microservices
par: Ranjan, Rohit.

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

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

Machine Learning with Amazon SageMaker Cookbook : 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments
par: Lat, Joshua Arvin.
Publié: (2021)

TinyML Cookbook : Combine machine learning with microcontrollers to solve real-world problems
par: Iodice, Gian Marco.

Hands-on TinyML : Harness the power of Machine Learning on the edge devices (English Edition)
par: Banerjee, Rohan.

Statistics for Machine Learning : Implement Statistical methods used in Machine Learning using Python
par: Singh, Himanshu.

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

Machine learning : end-to-end guide for Java developers
par: Reese, Richard M..
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)

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

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

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

Learning iOS UI development
par: D'areglia, Yari.
Publié: (2015)

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

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.

Practical Machine Learning on Databricks : Seamlessly transition ML models and MLOps on Databricks
par: Sinha, Debu.

Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits : A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
par: Amr, Tarek (19..-....).
Publié: (2020)

Learn iOS Application Development : Take Your Mobile App Development Skills to the Next Level with Swift and Xcode
par: Misra, Rudra S..

Machine Learning with Scala Quick Start Guide : Leverage popular machine learning algorithms and techniques and implement them in Scala
par: Karim, Rezaul Md..
Publié: (2019)

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

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

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

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

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

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.

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