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

Enregistré dans:

Sonraí Bibleagrafaíochta
Príomhúdar:	Mukherjee, Sudipta. (Údar)
Údair Eile:	Herbrich, Ralf. (Préface)
Formáid:	E-Book
Teanga:	Anglais
Foilsithe:	Birmingham : Packt Publishing, 2016.
Ábhair:	F♯ (Computer program language). Machine learning. F# (langage de programmation). Apprentissage automatique.
Autres localisations:	Voir dans le Sudoc
Rochtain Ar Líne:	Accès à l'E-book

Míreanna Comhchosúla

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
le: Richert, Willi.
Foilsithe: (2013)

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

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

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

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

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

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

Machine learning for planetary science
le: Helbert, Joern.
Foilsithe: (2022)

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

Machine learning : a bayesian and optimization perspective
le: Theodoridis, Sergios.
Foilsithe: (2015)

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

Optimization for machine learning
le: Sra, Suvrit (1976-....).
Foilsithe: (2011)

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

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

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

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

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

Introduction to statistical machine learning
le: Sugiyama, Masashi.
Foilsithe: (2015)

Introduction au machine learning
le: Azencott, Chloé-Agathe (19..-....).
Foilsithe: (2022)

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

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

Deep learning
le: Kelleher, John D. (1974-....).
Foilsithe: (2019)

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

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

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

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

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

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

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

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

TensorFlow deep learning projects : 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning
le: Massaron, Luca (19..-....).
Foilsithe: (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
le: Koduvely, Hari M..
Foilsithe: (2015)

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

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

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

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

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

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

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

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