106 rezultatov
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Scaling Graph Learning for the Enterprise(2025) MENSHAWY, AHMED,MOHAMED, SAMEH,MASOUD, MARAIM RIZKWith this hands-on guide, applied data scientists, machine learning engineers, and practitioners will learn how to build an E2E graph learning pipeline. You'll explore core challenges at each pipeline stage, from data acquisition and representation to reaVezava: Mehka82,59 € -
Learning GitHub Copilot(2025) LASTER, BRENTSupercharge your coding productivity with generative AI using GitHub Copilot. In this practical guide, author Brent Laster guides you through using generative AI for writing better code faster, generating tests with ease, creating polished documentation aVezava: Mehka72,26 € -
The TensorFlow Workshop(2021) MOOCARME, MATTHEW,BAGCHI, ABHRANSHU,ANTHONY SO,MADDALONE, ANTHONYVezava: Mehka62,59 € -
Designing Large Language Model Applications(2025) PAI, SUHASWith this book, you'll learn the tools, techniques, and playbooks for building useful products that incorporate the power of language models.Vezava: Mehka82,59 € -
Developer's Playbook for Large Language Model Security(2024) WILSON, STEVEThis practical book takes you straight to the heart of these threats. Author Steve Wilson, chief product officer at Exabeam, focuses exclusively on LLMs, eschewing generalized AI security to delve into the unique characteristics and vulnerabilities inhereVezava: Mehka82,59 € -
Causal Inference for Data Science(2025) DE VILLA, ALEIXCausal Inference for Data Science introduces data-centric techniques and methodologies you can use to estimate causal effects. The numerous insightful examples show you how to put causal inference into practice in the real world. The practical techniquesVezava: Trda86,94 € -
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: Mehka82,59 € -
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: Mehka82,59 €
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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: Trda123,05 € -
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: Mehka68,39 € -
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: Mehka56,06 €
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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: Mehka82,59 €
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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: Trda126,00 € -
Gaussian Processes for Machine Learning(2005) RASMUSSEN, CARL EDWARD (UNIVERSITY OF CAMBRIDGE),WILLIAMS, CHRISTOPHER K. I. (UNIVERSITY OF EDINBURGH)Vezava: Trda77,35 €
