Détail de l'auteur
Auteur Giuseppe BONACCORSO |
Documents disponibles écrits par cet auteur (3)
Titre : Mastering Machine Learning Algorithms Type de document : e-book Auteurs : Giuseppe BONACCORSO Editeur : PACKT PUBLISHING Année de publication : 2020 ISBN/ISSN/EAN : 9781838820299 Note générale : copyrighted Langues : Anglais (eng) Résumé : Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems Key Features Updated to include new algorithms and techniques Code updated to Python 3.8 & TensorFlow 2.x New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applications Book Description Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios. What you will learn Understand the characteristics of a machine learning algorithm Implement algorithms from supervised, semi-supervised, unsupervised, and RL domains Learn how regression works in time-series analysis and risk prediction Create, model, and train complex probabilistic models Cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work – train, optimize, and validate them Work with autoencoders, Hebbian networks, and GANs Who this book is for This book is for data science professionals who want to delve into complex ML algorithms to understand how various machine learning models can be built. Knowledge of Python programming is required. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88880132 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=501772
Titre : Hands-On Unsupervised Learning with Python Type de document : e-book Auteurs : Giuseppe BONACCORSO Editeur : PACKT PUBLISHING Année de publication : 2019 ISBN/ISSN/EAN : 9781789348279 Note générale : copyrighted Langues : Anglais (eng) Résumé : Discover the skill-sets required to implement various approaches to Machine Learning with Python
Key Features
- Explore unsupervised learning with clustering, autoencoders, restricted Boltzmann machines, and more
- Build your own neural network models using modern Python libraries
- Practical examples show you how to implement different machine learning and deep learning techniques
Book Description
Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python.
This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images.
By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges.
What you will learn
- Use cluster algorithms to identify and optimize natural groups of data
- Explore advanced non-linear and hierarchical clustering in action
- Soft label assignments for fuzzy c-means and Gaussian mixture models
- Detect anomalies through density estimation
- Perform principal component analysis using neural network models
- Create unsupervised models using GANs
Who this book is for
This book is intended for statisticians, data scientists, machine learning developers, and deep learning practitioners who want to build smart applications by implementing key building block unsupervised learning, and master all the new techniques and algorithms offered in machine learning and deep learning using real-world examples. Some prior knowledge of machine learning concepts and statistics is desirable.
Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88866869 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=484617
Titre : Python: Advanced Guide to Artificial Intelligence Type de document : e-book Auteurs : Giuseppe BONACCORSO Editeur : PACKT PUBLISHING Année de publication : 2018 ISBN/ISSN/EAN : 9781789957211 Note générale : copyrighted Langues : Anglais (eng) Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88865487 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=484137
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