Scikit-learn cookbook

Scikit-learn cookbook

Enregistré dans:

Détails bibliographiques
Auteur principal:	Avila, Julian (19..-....). (Auteur)
Autres auteurs:	Hauck, Trent (19..-....). (Auteur)
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

Scikit-learn : machine learning simplified
par: Garreta, Raúl.
Publié: (2017)

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

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

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

Machine learning with scikit-learn quick start guide : classification, regression, and clustering techniques in Python
par: Jolly, Kevin.
Publié: (2018)

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)

Hands-On Gradient Boosting with XGBoost and scikit-learn : Perform accessible machine learning and extreme gradient boosting with Python
par: Wade, Corey.
Publié: (2020)

Mastering predictive analytics with scikit-learn and TensorFlow : implement machine learning techniques to build advanced predictive models using Python
par: Fontaine, Alan.
Publié: (2018)

Python deep learning cookbook
par: Bakker, Indra Den.
Publié: (2017)

Practical Machine Learning Cookbook
par: Tripathi, Atul.
Publié: (2017)

Python machine learning cookbook : over 100 recipes to progress from smart data analytics to deep learning using real-world datasets
par: Ciaburro, Giuseppe (19..-....).
Publié: (2019)

Machine learning with R cookbook : analyze data and build predictive models
par: Bhatia, AshishSingh.
Publié: (2017)

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

Java Deep Learning Cookbook : Train neural networks for classiﬁcation, NLP, and reinforcement learning using Deeplearning4j
par: Raj, Rahul.
Publié: (2019)

Ensemble machine learning cookbook : over 35 practical recipes to explore ensemble machine learning techniques using Python
par: Sarkar, Dipayan.
Publié: (2019)

Python Data Cleaning Cookbook : Prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn, and OpenAI
par: Walker, Michael.

Machine learning for cybersecurity cookbook : over 80 recipes on how to implement machine learning algorithms for building security systems using Python
par: Tsukerman, Emmanuel.
Publié: (2019)

Keras deep learning cookbook : over 30 recipes for implementing deep neural networks in Python
par: Dua, Rajdeep.
Publié: (2018)

Beginning with Machine Learning : The Ultimate Introduction to Machine Learning, Deep Learning, Scikit-learn, and TensorFlow (English Edition)
par: Dua, Amit.

Deep Learning with MXNet Cookbook : Discover an extensive collection of recipes for creating and implementing AI models on MXNet
par: Torres, Andrés P..

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)

TensorFlow Machine Learning Cookbook : Over 60 recipes to build intelligent machine learning systems with the power of Python
par: Mcclure, Nick.
Publié: (2018)

Apache Spark deep learning cookbook : over 80 recipes that streamline deep learning in a distributed environment with Apache Spark
par: Sherif, Ahmed.
Publié: (2018)

Apache Spark 2.x machine learning cookbook
par: Amirghodsi, Siamak.
Publié: (2017)

TensorFlow 1.x Deep Learning Cookbook
par: Gulli, Antonio.
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)

R deep learning cookbook : solve complex neural net problems with TensorFlow, H2O and MXNet
par: Prakash, PKS..
Publié: (2017)

Machine Learning Cookbook with Python : Create ML and Data Analytics Projects Using Some Amazing Open Datasets (English Edition)
par: Guha, Rehan.

Hands-on unsupervised learning with Python : implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more
par: Bonaccorso, Giuseppe.
Publié: (2019)

Deep Learning with R Cookbook : Over 45 unique recipes to delve into neural network techniques using R 3.5.x
par: Gupta, Swarna.
Publié: (2020)

Hands-On Ensemble Learning with Python : Build highly optimized ensemble machine learning models using scikit-learn and Keras
par: Kyriakides, George.
Publié: (2019)

Deep Learning for Time Series Cookbook : Use PyTorch and Python recipes for forecasting, classification, and anomaly detection
par: Cerqueira, Vitor.

Machine Learning Using TensorFlow Cookbook : Over 60 recipes on machine learning using deep learning solutions from Kaggle Masters and Google Developer Experts
par: Audevart, Alexia.
Publié: (2021)

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)

Applied deep learning with Python : use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions
par: Galea, Alex.
Publié: (2018)

Learn AI with Python : Explore Machine Learning and Deep Learning techniques for Building Smart AI Systems Using Scikit-Learn, NLTK, NeuroLab, and Keras
par: Leekha, Gaurav.

TensorFlow 2 Reinforcement Learning Cookbook : Over 50 recipes to help you build, train, and deploy learning agents for real-world applications
par: Palanisamy, Praveen.
Publié: (2021)

Designing machine learning systems with Python
par: Julian, David.
Publié: (2016)

Essentials of Deep Learning and AI : Experience Unsupervised Learning, Autoencoders, Feature Engineering, and Time Series Analysis with TensorFlow, Keras, and scikit-learn
par: Soppin, Shashidhar.

Python Machine Learning By Example : Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn
par: Liu, Yuxi (Hayden).
Publié: (2020)

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