Advertisement

Machine Learning Introduction, Third Edition

  •  5星
  •     浏览量: 0
  •     大小:None
  •      文件类型:None


简介:
《机器学习导论(第三版)》全面介绍了机器学习的基本概念、算法和技术,适用于初学者和有一定基础的学习者。 Introduction to Machine Learning, Third Edition by Ethem Alpaydin was published by The MIT Press in September 2014. ISBN: 978-0-262-028189 (PDF)

全部评论 (0)

还没有任何评论哟~
客服
客服
  • Machine Learning Introduction, Third Edition
    优质
    《机器学习导论(第三版)》全面介绍了机器学习的基本概念、算法和技术,适用于初学者和有一定基础的学习者。 Introduction to Machine Learning, Third Edition by Ethem Alpaydin was published by The MIT Press in September 2014. ISBN: 978-0-262-028189 (PDF)
  • Introduction to Probabilistic Machine Learning
    优质
    《Introduction to Probabilistic Machine Learning》是一本介绍基于概率论的机器学习方法和模型的基础读物,适合初学者入门。书中涵盖了贝叶斯理论、高斯过程等内容,并提供实用示例帮助理解。 Probabilistic Machine Learning-An Introduction 这本书或资料介绍了概率机器学习的基本概念和方法。它为读者提供了一个理解如何在不确定性环境中进行预测和决策的框架,并涵盖了从基础的概率理论到高级的主题模型、贝叶斯非参数等内容。通过该书,读者可以掌握构建基于数据驱动的概率模型的能力,这些模型能够处理复杂的数据结构并应用于各种实际问题中。
  • Statistical Machine Learning Introduction - ANU 2017
    优质
    《统计机器学习导论》是由澳大利亚国立大学(ANU)于2017年开设的一门课程,旨在介绍如何使用统计学原理进行机器学习模型的构建与优化。 ANU COMP4670 2017课程资料 授课教师:Cheng Soon Ong & Christian Walder Machine Learning Research Group Data61 | CSIRO Collage of Engineering and Computer Science, The Australian National University
  • Machine Learning Introduction with Python [2016]
    优质
    《Machine Learning Introduction with Python [2016]》是一本介绍机器学习基础概念及Python实现的经典教程,适合初学者快速入门。书中结合实例讲解算法原理与应用技巧,帮助读者构建坚实的知识体系。 Introduction to Machine Learning with Python (Early Release) is a book written in English that was published in 2016. Many developers who use Python are interested in learning about machine learning and how it can be used practically to solve problems faced by businesses dealing with large volumes of data. This book, Machine Learning with Python, introduces the fundamentals of machine learning while providing a comprehensive practical understanding of the subject. Youll learn key concepts and algorithms related to machine learning, understand when they should be applied, and gain insight into how to use them effectively. The book covers a complete workflow for machine learning: data preprocessing and handling data, training algorithms, evaluating results, and implementing these algorithms in production-level systems.
  • LEARNING SPRING BOOT 3.0: THIRD EDITION
    优质
    《Learning Spring Boot 3.0: Third Edition》是一本深入介绍Spring Boot 3.0框架的教程书籍,适合希望构建高效、可靠微服务应用的开发者阅读。 LEARNING SPRING BOOT 3.0 - THIRD EDITION 这本关于Spring Boot 3.0的第三版书籍专注于讲解如何使用Java开发基于Spring Boot的应用程序。书中详细介绍了Spring Boot 3的新特性和最佳实践,适合希望深入了解和应用最新版本Spring Boot技术的开发者阅读。
  • Wireless and Mobile Systems: Introduction (Third Edition)
    优质
    本书为《无线与移动系统导论》第三版,全面介绍了无线和移动通信系统的原理和技术。适合相关专业学生及工程师阅读参考。 《无线与移动系统导论》(第三版)是一本教材。这本书深入浅出地介绍了当前广泛使用的各种无线通信技术和移动系统的原理、应用以及发展趋势。书中不仅涵盖了基础理论知识,还包含了许多实用案例和技术细节,使读者能够全面了解无线和移动技术的现状及未来前景。
  • Neural Networks and Learning Machines (Third Edition)
    优质
    《Neural Networks and Learning Machines》(第三版)全面介绍了人工神经网络理论与学习算法,适用于研究人员、工程师及高年级学生。 《神经网络与学习机器》第三版(英文版)是一本关于类神经理论的经典著作。
  • Machine Learning Tutorials (2nd Edition).pdf
    优质
    《Machine Learning Tutorials (2nd Edition)》是一本全面介绍机器学习概念和实践的教程,第二版更新了最新算法和技术。 本书涵盖了CoreML、Vision框架以及图像与序列分类器、自然语言处理等内容,帮助你在Apple和iOS设备上开始机器学习之旅。 想要知道一个秘密吗?其实学好机器学习并不难。事实证明,你不需要来自著名大学的博士学位或数学背景就能进行机器学习。如果你已经掌握了编程技巧,那么你可以轻松地掌握机器学习——保证! 本书将带你了解在iOS和Apple设备上的机器学习入门知识以及其提供的优势与限制。接下来的内容中,我们将深入探讨每个主题,直到你能熟练运用这些工具来提升你的软件开发能力。 目前苹果提供了一系列高级框架(包括自然语言处理、语音识别及视觉识别等),通过简单的API提供了先进的机器学习功能作为iOS工具的一部分。无论你想要将语音转换为文本、识别语言或语法结构、在照片中检测人脸或是追踪视频中的移动物体,这些框架都能满足你的需求。 在这本书里,你会学到如何使用这些工具和框架来让应用程序变得更智能,并且还会了解背后的工作原理——为什么这项技术如此令人惊叹。无论是对Apple还是iOS开发者来说,如果你有兴趣学习训练模型、编码图像识别系统、理解自然语言处理工作方式以及构建序列分类器等知识,这本书都非常适合你。
  • Introduction to Reinforcement Learning (2nd Edition)
    优质
    本书为读者提供了强化学习的全面介绍,涵盖了理论基础、算法设计及应用实例,适合初学者和有经验的研究者参考。第二版更新了最新研究成果和技术进展。 《强化学习:增强学习》第二版的PDF版本适合自学使用。
  • Machine Learning: An Algorithmic Perspective, 2nd Edition
    优质
    本书《机器学习:算法视角(第2版)》系统地介绍了机器学习的核心理论与实用技术,通过丰富的实例和算法解析,帮助读者深入理解并掌握机器学习的精髓。 Title: Machine Learning: An Algorithmic Perspective, 2nd Edition Author: Stephen Marsland Length: 457 pages Edition: 2 Language: English Publisher: Chapman and Hall/CRCPublication Date: October 8, 2014 ISBN-10: 1466583282 ISBN-13: 9781466583283 This book offers a practical approach for students with limited statistical knowledge to understand machine learning algorithms. Since the first edition was published, there have been significant developments in the field of machine learning, particularly concerning the statistical interpretation of these algorithms. The second edition includes two new chapters on deep belief networks and Gaussian processes. It also reorganizes content for a more natural flow and revises material on support vector machines with an implementation provided for experimentation. Additional topics covered include random forests, perceptron convergence theorem, accuracy methods, conjugate gradient optimization for multi-layer perceptrons, Kalman filters, particle filters, and improved Python code. The book is suitable as both an introductory one-semester course textbook and a more advanced study guide. It encourages students to practice with the provided examples and includes detailed problems in each chapter. All of the example code used throughout the text can be accessed on the authors website. Table of Contents: 1. Introduction 2. Preliminaries 3. Neurons, Neural Networks, and Linear Discriminants 4. The Multi-layer Perceptron 5. Radial Basis Functions and Splines 6. Dimensionality Reduction 7. Probabilistic Learning 8. Support Vector Machines 9. Optimization and Search 10. Evolutionary Learning 11. Reinforcement Learning 12. Learning with Trees 13. Decision by Committee: Ensemble Learning 14. Unsupervised Learning 15. Markov Chain Monte Carlo (MCMC) Methods 16. Graphical Models 17. Symmetric Weights and Deep Belief Networks 18. Gaussian Processes