F# for machine learning essentials : get up and running with machine learning with F# in a fun and functional way

F# for machine learning essentials : get up and running with machine learning with F# in a fun and functional way

Uloženo v:

Podrobná bibliografie
Hlavní autor:	Mukherjee, Sudipta. (Autor)
Další autoři:	Herbrich, Ralf. (Préface)
Médium:	E-Book
Jazyk:	Anglais
Vydáno:	Birmingham : Packt Publishing, 2016.
Témata:	F♯ (Computer program language). Machine learning. F# (langage de programmation). Apprentissage automatique.
Autres localisations:	Voir dans le Sudoc
On-line přístup:	Accès à l'E-book

Podobné jednotky

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
Autor: Richert, Willi.
Vydáno: (2013)

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

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

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

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

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

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

Machine learning for planetary science
Autor: Helbert, Joern.
Vydáno: (2022)

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

Machine learning : a bayesian and optimization perspective
Autor: Theodoridis, Sergios.
Vydáno: (2015)

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

Optimization for machine learning
Autor: Sra, Suvrit (1976-....).
Vydáno: (2011)

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

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

Deep learning by example : a hands-on guide to implementing advanced machine learning algorithms and neural networks
Autor: Menshawy, Ahmed.
Vydáno: (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
Autor: Bali, Raghav (19..-....).
Vydáno: (2016)

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

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

Introduction to statistical machine learning
Autor: Sugiyama, Masashi.
Vydáno: (2015)

Introduction au machine learning
Autor: Azencott, Chloé-Agathe (19..-....).
Vydáno: (2022)

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

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

Deep learning
Autor: Kelleher, John D. (1974-....).
Vydáno: (2019)

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

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

Python : deeper insights into machine learning : leverage benefits of machine learning techniques using Python
Autor: Raschka, Sebastian.
Vydáno: (2016)

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

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

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

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

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

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

Learning Bayesian models with R : become an expert in Bayesian machine learning methods using R and apply them to solve real-world big data problems
Autor: Koduvely, Hari M..
Vydáno: (2015)

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

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

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

Data science : fondamentaux et études de cas : machine learning avec Python et R
Autor: Biernat, Éric (19..-....).
Vydáno: (2015)

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

Data science par la pratique : fondamentaux avec Python
Autor: Grus, Joel (19..-....).
Vydáno: (2020)

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

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