149 rezultatov
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Deep Learning at Scale(2024) MALL, SUNEETAThis book illustrates complex concepts of full stack deep learning and reinforces them through hands-on exercises to arm you with tools and techniques to scale your project.Vezava: Mehka82,08 € -
Causal Inference in Python(2023) FACURE, MATHEUSIn this book, author Matheus Facure explains the untapped potential of causal inference for estimating impacts and effects.Vezava: Mehka82,08 € -
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Deep Learning(2024) MARTINEZ-RAMON, MANEL (UNIVERSITY OF NEW MEXICO, NM, USA; UNIVERSIDAD CARLOS III DE MADRID, SPAIN),AJITH, MEENU (GEORGIA STATE UNIVERSITY; GEORGIA INSTITUTE OF TECHNOLOGY; EMORY UNIVERSITY, USA),KURUP, ASWATHY RAJENDRA (UNIVERSITY OF MEXICO, MEXICO)Vezava: Trda119,26 € -
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Data Science: The Hard Parts(2023) VAUGHAN, DANIELThis practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. Taken as a whole, the lessons in this book make the difference between an average data scientistVezava: Mehka67,97 € -
Machine Learning for Hackers(2012) CONWAY, DREWNow that storage and collection technologies are cheaper and more precise, methods for extracting relevant information from large datasets is within the reach any experienced programmer willing to crunch data.Vezava: Mehka55,57 €
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Machine Learning Interviews(2023) CHANG, SUSAN SHUIn this guide, data science leader Susan Shu Chang shows you how to tackle the ML hiring process.Vezava: Mehka82,08 €
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Architecting Data and Machine Learning Platforms(2024) TRANQUILLIN, MARCO,LAKSHMANAN, VALLIAPPA,TEKINER, FIRATThis handbook is ideal for learning how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, or multicloud tools like Fivetran, dbt, Snowflake, and Databricks.Vezava: Mehka67,97 €
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Building Recommendation Systems in Python and Jax(2023) BISCHOF, BRYAN,YEE, HECTORIn this practical book, authors Bryan Bischof and Hector Yee illustrate the core concepts and examples to help you create a RecSys for any industry or scale. You'll learn the math, ideas, and implementation details you need to succeed.Vezava: Mehka82,08 €
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Bayesian Reasoning and Machine Learning(2012) BARBER, DAVID (UNIVERSITY COLLEGE LONDON)This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available onlinVezava: Trda113,63 € -
Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data(2013) NATHAN J. KUTZVezava: Mehka79,71 € -
Gaussian Processes for Machine Learning(2005) RASMUSSEN, CARL EDWARD (UNIVERSITY OF CAMBRIDGE),WILLIAMS, CHRISTOPHER K. I. (UNIVERSITY OF EDINBURGH)Vezava: Trda74,97 € -
Superintelligence: Paths, Dangers, Strategies(2014) NICK BOSTROMDATUM IZIDA ZA FF NEZNANVezava:31,25 € -
Statistical Field Theory for Neural Networks(2020) HELIAS, MORITZ,DAHMEN, DAVIDThis book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks.Vezava: Mehka99,21 € -
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