143 rezultatov
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Explainable AI with Python(2021) GIANFAGNA, LEONIDA,DI CECCO, ANTONIOModel-agnostic methods for XAI are shown to produce explanations without relying on ML models internals that are “opaque.” Using examples from Computer Vision, the authors then look at explainable models for Deep Learning and prospective methods for theVezava: Mehka94,49 € -
Foundations of Probabilistic Programming(2020)This book provides an overview of the theoretical underpinnings of modern probabilistic programming and presents applications in e.g., machine learning, security, and approximate computing. Comprehensive survey chapters make the material accessible to graVezava: Trda101,50 € -
Essentials of Pattern Recognition(2020) WU, JIANXIN (NANJING UNIVERSITY, CHINA)Introduces fundamental concepts, major models, and popular applications of pattern recognition for a one-semester undergraduate course. The text focuses on a relatively small number of core concepts with an abundance of illustrations and examples and provVezava: Trda99,75 € -
Machine Learning in Asset Pricing(2021) NAGEL, STEFANA groundbreaking, authoritative introduction to how machine learning can be applied to asset pricingInvestors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such dataVezava: Trda71,40 € -
Essential Math for Data Science(2022) NIELD, THOMASTo succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebraVezava: Mehka68,39 € -
Machine Learning and Wireless Communications(2022)How can machine learning help the design of future communication networks? How can future wireless networks meet the demands of emerging machine learning applications? Discover the interactions between two of the most impactful technologies of our age inVezava: Trda134,57 € -
Scaling up Machine Learning(2018)In many practical situations it is impossible to run existing machine learning methods on a single computer, because either the data is too large or the speed and throughput requirements are too demanding. Researchers and practitioners will find here a vaVezava: Mehka82,25 € -
Introduction to Natural Language Processing(2019) EISENSTEIN, JACOB (ASSISTANT PROFESSOR, GOOGLE)Vezava: Trda107,10 € -
Machine Learning with Neural Networks(2021) MEHLIG, BERNHARD (GOTEBORGS UNIVERSITET, SWEDEN)This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. Fundamental physical and mathematical principles of the topic are described alongside current applications in sVezava: Trda73,50 € -
Managing Machine Learning Projects(2023) THOMPSON, SIMONFor anyone interested in better management of machine learning projects from idea to production. Managing Machine Learning Projects is a comprehensive guide that does not require any technical skills. This edition will help you discover battle-tested dataVezava: Trda75,46 € -
Machine Learning with Python Cookbook(2023) GALLATIN, KYLE,ALBON, CHRISThis practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work.Vezava: Mehka82,59 € -
Implementing MLOps in the Enterprise(2023) HAVIV, YARON,GIFT, NOAHThis practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moviVezava: Mehka82,59 €
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Low-Code AI(2023) STRIPLING, GWENDOLYNE,ABEL, MICHAELThis hands-on guide presents three problem-focused ways to learn ML: no code using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. You'll learn key ML concepts by using real-world datasets with realistic problems.Vezava: Mehka82,59 €
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Deep Reinforcement Learning(2022) PLAAT, ASKEDeep reinforcement learning has attracted considerable attention recently. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how subjects’ desired behavior can beVezava: Mehka68,89 € -
Applied Machine Learning and AI for Engineers(2022) PROSISE, JEFFWhile many introductory guides to AI are calculus books in disguise, this one mostly eschews the math. Instead, author Jeff Prosise helps engineers and software developers build an intuitive understanding of AI to solve business problems.Vezava: Mehka82,59 € -
Probabilistic Machine Learning for Finance and Investing(2023) KANUNGO, DEEPAK K.By moving away from flawed statistical methodologies, you'll move toward an intuitive view of probability as a mathematically rigorous statistical framework that quantifies uncertainty holistically and successfully. This book shows you how.Vezava: Mehka82,59 €
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Learning Tensorflow.js(2021) LABORDE, GANTIn this guide, author Gant Laborde--Google Developer Expert in machine learning and the web--provides a hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience that includes data scientists, engineers, web developers, stuVezava: Mehka58,07 €
