Machine learning for developers : uplift your regular applications with the power of statistics, analytics, and machine learning

Machine learning for developers : uplift your regular applications with the power of statistics, analytics, and machine learning

Enregistré dans:

Détails bibliographiques
Auteur principal:	Bonnin, Rodolfo. (Auteur)
Autres auteurs:	Delrieux, Claudio. (Préface)
Support:	E-Book
Langue:	Anglais
Publié:	Birmingham : Packt, 2017.
Sujets:	Apprentissage automatique. Réseaux neuronaux (informatique). Machine learning.
Autres localisations:	Voir dans le Sudoc
Accès en ligne:	Accès à l'E-book

Documents similaires

Deep learning essentials : your hands-on guide to the fundamentals of deep learning and neural network modeling
par: Di, Wei.
Publié: (2018)

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

Adversial robustness for machine learning
par: Chen, Pin-Yu (19..-).

Deep learning avec Keras et TensorFlow : mise en oeuvre et cas concrets
par: Géron, Aurélien (19..-....).

Deep learning with PyTorch : a practical approach to building neural network models using PyTorch
par: Subramanian, Vishnu (19..-....).
Publié: (2018)

Machine learning with R : learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications
par: Lantz, Brett.
Publié: (2013)

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)

Machine learning for planetary science
par: Helbert, Joern.
Publié: (2022)

Python machine learning
par: Lee, Wei-Meng (19..-....).

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

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)

F# for machine learning essentials : get up and running with machine learning with F# in a fun and functional way
par: Mukherjee, Sudipta.
Publié: (2016)

Practical convulational neural networks : implement advanced deep learning models using Python
par: Sewak, Mohit (19..-..).
Publié: (2018)

R machine learning by example : understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully
par: Bali, Raghav (19..-....).
Publié: (2016)

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

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

Mastering .NET machine learning : master the art of machine learning with .NET and gain insight into real-world applications
par: Dixon, Jamie.
Publié: (2016)

Apprentissage machine, clé de l'intelligence artificielle : une introduction pour non-spécialistes
par: Gilleron, Rémi.

Deep learning by example : a hands-on guide to implementing advanced machine learning algorithms and neural networks
par: Menshawy, Ahmed.
Publié: (2018)

Gaussian Processes for Machine Learning
par: Rasmussen, Carl Edward (1969-....).
Publié: (2005)

Introduction to statistical machine learning
par: Sugiyama, Masashi.
Publié: (2015)

Introduction au machine learning
par: Azencott, Chloé-Agathe (19..-....).
Publié: (2022)

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

Machine learning avec Scikit-Learn : mise en oeuvre et cas concrets
par: Géron, Aurélien (19..-....).

Apache Mahout essentials : implement top-notch machine learning algorithms for classification, clustering, and recommendations with Apache Mahout
par: Withanawasam (Jayani).
Publié: (2015)

Feature engineering made easy : identify unique features from your dataset in order to build powerful machine learning systems
par: Ozdemir, Sinan.
Publié: (2018)

Developmental robotics : from babies to robots
par: Cangelosi, Angelo (1967-....).

The Promise of Artificial Intelligence : Reckoning and Judgment
par: Smith, Brian Cantwell.
Publié: (2019)

Deep learning
par: Kelleher, John D. (1974-....).
Publié: (2019)

Apprentissage artificiel : concepts et algorithmes : de Bayes et Hume au Deep Learning
par: Barra, Vincent (19..-....; chercheur en informatique de modélisation).
Publié: (2021)

Machine learning for time series forecasting with Python
par: Lazzeri, Francesca (19..-....).

Big data et machine learning : les concepts et les outils de la data science
par: Lemberger, Pirmin.
Publié: (2019)

Deep learning with TensorFlow : explore neural networks and build intelligent systems with Python
par: Zaccone, Giancarlo.
Publié: (2018)

Python pour le data scientist : des bases du langage au machine learning
par: Jakobowicz, Emmanuel (1980-....).

TensorFlow deep learning projects : 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning
par: Massaron, Luca (19..-....).
Publié: (2018)

Éloge du flou : aux frontières des mathématiques et de l'intelligence artificielle
par: Sangalli, Arturo (1940-....).
Publié: (2001)

Statistics, Data Mining, and Machine Learning in Astronomy : A Practical Python Guide for the Analysis of Survey Data
par: Ivezic, Zeljko (1965-....).
Publié: (2019)

Extraction et gestion des connaissances : EGC'2016 : [16ème conférence francophone sur l'extraction et la gestion des connaissances, Reims, 18-22 janvier 2016]

Extraction et gestion des connaissances : EGC'2015

Les data sciences en 100 questions-réponses
par: Benzaki, Younes.
Publié: (2020)

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