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Auteur Yuxi (Hayden) LIU |
Documents disponibles écrits par cet auteur (3)
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Titre : Python Machine Learning By Example Type de document : e-book Auteurs : Yuxi (Hayden) LIU Editeur : PACKT PUBLISHING Année de publication : 2020 ISBN/ISSN/EAN : 9781800209718 Note générale : copyrighted Langues : Anglais (eng) Résumé : A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniquesKey FeaturesDive into machine learning algorithms to solve the complex challenges faced by data scientists todayExplore cutting edge content reflecting deep learning and reinforcement learning developmentsUse updated Python libraries such as TensorFlow, PyTorch, and scikit-learn to track machine learning projects end-to-endBook DescriptionPython Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML).With six new chapters, on topics including movie recommendation engine development with Naive Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements.At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries.Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP.By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.What you will learnUnderstand the important concepts in ML and data scienceUse Python to explore the world of data mining and analyticsScale up model training using varied data complexities with Apache SparkDelve deep into text analysis and NLP using Python libraries such NLTK and GensimSelect and build an ML model and evaluate and optimize its performanceImplement ML algorithms from scratch in Python, TensorFlow 2, PyTorch, and scikit-learnWho this book is forIf you're a machine learning enthusiast, data analyst, or data engineer highly passionate about machine learning and want to begin working on machine learning assignments, this book is for you.Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial, although this is not necessary. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88906449 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=526041
Titre : Hands-On Deep Learning Architectures with Python Type de document : e-book Auteurs : Yuxi (Hayden) LIU Editeur : PACKT PUBLISHING Année de publication : 2019 ISBN/ISSN/EAN : 9781788998086 Note générale : copyrighted Langues : Anglais (eng) Résumé : Concepts, tools, and techniques to explore deep learning architectures and methodologies Key Features Explore advanced deep learning architectures using various datasets and frameworks Implement deep architectures for neural network models such as CNN, RNN, GAN, and many more Discover design patterns and different challenges for various deep learning architectures Book Description Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems. Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations. By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world. What you will learn Implement CNNs, RNNs, and other commonly used architectures with Python Explore architectures such as VGGNet, AlexNet, and GoogLeNet Build deep learning architectures for AI applications such as face and image recognition, fraud detection, and many more Understand the architectures and applications of Boltzmann machines and autoencoders with concrete examples Master artificial intelligence and neural network concepts and apply them to your architecture Understand deep learning architectures for mobile and embedded systems Who this book is for If you're a data scientist, machine learning developer/engineer, or deep learning practitioner, or are curious about AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88869963 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=485401
Titre : Research Handbook of International Talent Management Type de document : e-book Auteurs : Yuxi (Hayden) LIU, Auteur Année de publication : 2019 Importance : 461 p. ISBN/ISSN/EAN : 978-1-78643-710-5 Langues : Anglais (eng) Mots-clés : Management
COMPETENCE ; ECONOMIE DU SAVOIR ; MANAGEMENT INTERNATIONALRésumé : International talent management has become a critically important topic for scholarly discussion, in policy debates, and among the business community. Despite this, however, research into talent management tends to lack theoretical underpinnings, especially from an international, multidisciplinary, and comparative perspective. This Research Handbook fills this gap, bringing together a range of leading researchers, scholars, and thinkers to debate and advance the conceptualization and understanding of this multifaceted subject. Nombre d'accès : 1 En ligne : http://www.vlebooks.com/vleweb/product/openreader?id=Neoma&accId=9169105&isbn=97 [...] Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=489263
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