116 rezultatov
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Building Machine Learning Powered Applications(2020) AMEISEN, EMMANUELLearn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product.Vezava: Mehka68,39 € -
Deep Learning Projects Using TensorFlow 2(2020) SILAPARASETTY, VINITAWork through engaging and practical deep learning projects using TensorFlow 2.0.Vezava: Mehka63,98 € -
Machine Learning(2014) MARSLAND, STEPHEN (MASSEY UNIVERSITY, PALMERSTON NORTH, NEW ZEALAND)Vezava: Trda134,56 € -
Machine Learning: Concepts, Tools And Data Visualization(2021) KANG, MINSOO (EULJI UNIVERSITY, KOREA),CHOI, EUNSOO (ALL4LAND INC., KOREA)Vezava: Mehka73,83 € -
Embedded Analytics(2023) FARMER, DONALD,HORBURY, JIMThe adoption of data analytics has remained remarkably static - perhaps reaching no more than thirty percent of potential users. This book explores the most important techniques for taking that adoption further: embedding analytics into the workflow of ouVezava: Mehka47,24 € -
Building Knowledge Graphs(2023) BARRASA, JESUS,WEBBER, JIMUsing hands-on examples, this practical book shows data scientists and data practitioners how to build their own custom knowledge graphs. Authors Jesus Barrasa and Jim Webber from Neo4j illustrate patterns commonly used for building knowledge graphs thatVezava: Mehka92,91 € -
Practical Linear Algebra for Data Science(2022) COHEN, MIKE XThis practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applicationVezava: Mehka82,59 € -
Machine Learning Design Patterns(2020) LAKSHMANAN, VALLIAPPAThe design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify theVezava: Mehka68,39 € -
Designing Machine Learning Systems(2022) HUYEN, CHIPIn this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements.Vezava: Mehka68,39 € -
Graph-Powered Analytics and Machine Learning with TigerGraph(2023) LEE, PH.D., VICTOR,NGUYEN, PHUC KIEN,CHANG, XINYUThis practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using TigerGraph, one of the leading graph database models available.Vezava: Mehka68,39 € -
Probabilistic Graphical Models(2009) KOLLER, DAPHNE (STANFORD UNIVERSITY),FRIEDMAN, NIR (HEBREW UNIVERSITY)Vezava: Trda154,35 € -
Data Science, Analytics and Machine Learning with R(2023) FAVERO, LUIZ PAULO (ECONOMICS, BUSINESS ADMINISTRATION AND ACCOUNTING COLLEGE OF THE UNIVERSITY OF SAO PAULO, BRAZIL/ FACULDADE DE ECONOMIA, ADMINISTRACAO E CONTABILIDADE, UNIVERSIDADE DE SAO PAULO, BRAZIL),BELFIORE, PATRICIA (ASSOCIATE PROFESSOR, FEDERAL UNIVERSITY OF ABC (UFABC)/ FEDERAL UNIVERSITY OF ABC, BRAZIL),DE FREITAS SOUZA, RAFAEL (ECONOMICS, BUSINESS ADMINISTRATION AND ACCOUNTING COLLEGE OF RIBEIRAO PRETO, UNIVERSITY OF SAO PAULO, BRAZIL)Vezava: Mehka174,80 €
