The Machine Learning Glossary
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| Author | : Andrew D. Chapman |
| Publisher | : The Autodidact’s Toolkit |
| Total Pages | : 283 |
| Release | : 2024-01-03 |
| Genre | : Computers |
| ISBN | : |
This book is intended to serve as your comprehensive guide and ally in the dynamic and ever-evolving world of Machine Learning (ML) and Artificial Intelligence (AI). This book is designed to serve as both a critical reference guide for those familiar with the fields and an enlightening educational resource for newcomers. As the realms of ML and AI continue to expand and influence every aspect of our lives, the necessity for a clear understanding of the terminology and concepts has never been more pronounced. Our glossary tackles this need head-on, offering detailed insights into 140 key terms that form the backbone of these technologies. Each entry in this glossary is meticulously structured to provide not only a definition but also the context and explanation that brings the term to life. We delve into real-world applications to showcase how these concepts are driving innovation and transforming industries. Furthermore, we discuss each term's significance to the broader landscapes of AI and ML, providing a lens through which to appreciate their impact and potential. But knowledge doesn't stop at understanding; it challenges us to look forward. Hence, we also explore the hurdles and debates surrounding these terms, offering a balanced view of the current challenges and the potential solutions on the horizon. Whether you're a seasoned professional seeking a quick reference, a student embarking on your learning journey, or simply a curious mind attracted to the field, The Machine Learning Glossary is designed to be your go-to resource. It's not just about learning; it's about continuously coming back for insights as you grow and as the field evolves. Practical, detailed, and accessible, this book promises to be an invaluable asset that you'll return to time and again, as a trusted companion in your ongoing exploration of machine learning and artificial intelligence. Welcome to the journey of understanding, application, and continuous learning. Welcome to The Machine Learning Glossary.
| Author | : Christoph Molnar |
| Publisher | : Lulu.com |
| Total Pages | : 320 |
| Release | : 2020 |
| Genre | : Computers |
| ISBN | : 0244768528 |
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
| Author | : Claude Sammut |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 1061 |
| Release | : 2011-03-28 |
| Genre | : Computers |
| ISBN | : 0387307680 |
This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.
| Author | : Mariette Awad |
| Publisher | : Apress |
| Total Pages | : 263 |
| Release | : 2015-04-27 |
| Genre | : Computers |
| ISBN | : 1430259906 |
Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions. Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms. Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning.
| Author | : Andrea Mechelli |
| Publisher | : Academic Press |
| Total Pages | : 412 |
| Release | : 2019-11-14 |
| Genre | : Medical |
| ISBN | : 0128157402 |
Machine Learning is an area of artificial intelligence involving the development of algorithms to discover trends and patterns in existing data; this information can then be used to make predictions on new data. A growing number of researchers and clinicians are using machine learning methods to develop and validate tools for assisting the diagnosis and treatment of patients with brain disorders. Machine Learning: Methods and Applications to Brain Disorders provides an up-to-date overview of how these methods can be applied to brain disorders, including both psychiatric and neurological disease. This book is written for a non-technical audience, such as neuroscientists, psychologists, psychiatrists, neurologists and health care practitioners. - Provides a non-technical introduction to machine learning and applications to brain disorders - Includes a detailed description of the most commonly used machine learning algorithms as well as some novel and promising approaches - Covers the main methodological challenges in the application of machine learning to brain disorders - Provides a step-by-step tutorial for implementing a machine learning pipeline to neuroimaging data in Python
| Author | : Giovanni Seni |
| Publisher | : Morgan & Claypool Publishers |
| Total Pages | : 127 |
| Release | : 2010 |
| Genre | : Computers |
| ISBN | : 1608452840 |
"Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges -- from investment timing to drug discovery, and fraud detection to recommendation systems -- where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization -- today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods -- bagging, random forests, and boosting -- to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity."--Publisher's website.
| Author | : Stephen P. Boyd |
| Publisher | : Cambridge University Press |
| Total Pages | : 744 |
| Release | : 2004-03-08 |
| Genre | : Business & Economics |
| ISBN | : 9780521833783 |
Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods. The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.
| Author | : Mohamed Sabri |
| Publisher | : BPB Publications |
| Total Pages | : 418 |
| Release | : 2021-06-24 |
| Genre | : Computers |
| ISBN | : 9390684978 |
Discover one of the most complete dictionaries in data science. KEY FEATURES ● Simplified understanding of complex concepts, terms, terminologies, and techniques. ● Combined glossary of machine learning, mathematics, and statistics. ● Chronologically arranged A-Z keywords with brief description. DESCRIPTION This pocket guide is a must for all data professionals in their day-to-day work processes. This book brings a comprehensive pack of glossaries of machine learning, deep learning, mathematics, and statistics. The extensive list of glossaries comprises concepts, processes, algorithms, data structures, techniques, and many more. Each of these terms is explained in the simplest words possible. This pocket guide will help you to stay up to date of the most essential terms and references used in the process of data analysis and machine learning. WHAT YOU WILL LEARN ● Get absolute clarity on every concept, process, and algorithm used in the process of data science operations. ● Keep yourself technically strong and sound-minded during data science meetings. ● Strengthen your knowledge in the field of Big data and business intelligence. WHO THIS BOOK IS FOR This book is for data professionals, data scientists, students, or those who are new to the field who wish to stay on top of industry jargon and terminologies used in the field of data science. TABLE OF CONTENTS 1. Chapter one: A 2. Chapter two: B 3. Chapter three: C 4. Chapter four: D 5. Chapter five: E 6. Chapter six: F 7. Chapter seven: G 8. Chapter eight: H 9. Chapter nine: I 10. Chapter ten: J 11. Chapter 11: K 12. Chapter 12: L 13. Chapter 13: M 14. Chapter 14: N 15. Chapter 15: O 16. Chapter 16: P 17. Chapter 17: Q 18. Chapter 18: R 19. Chapter 19 : S 20. Chapter 20 : T 21. Chapter 21 : U 22. Chapter 22 : V 23. Chapter 23: W 24. Chapter 24: X 25. Chapter 25: Y 26. Chapter 26 : Z
| Author | : Shai Shalev-Shwartz |
| Publisher | : Cambridge University Press |
| Total Pages | : 415 |
| Release | : 2014-05-19 |
| Genre | : Computers |
| ISBN | : 1107057132 |
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
| Author | : Sebastian Raschka |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 775 |
| Release | : 2022-02-25 |
| Genre | : Computers |
| ISBN | : 1801816387 |
This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.
