An Introduction to Machine Learning, Gopinath Rebala; Ajay Ravi; Sanjay Churiwala
Автор: Kevin Murphy Название: Machine Learning ISBN: 0262018020 ISBN-13(EAN): 9780262018029 Издательство: MIT Press Рейтинг: Цена: 124150.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.
The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package -- PMTK (probabilistic modeling toolkit) -- that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Автор: Boyd Stephen Название: Introduction to Applied Linear Algebra ISBN: 1316518965 ISBN-13(EAN): 9781316518960 Издательство: Cambridge Academ Рейтинг: Цена: 45410.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: A groundbreaking introductory textbook covering the linear algebra methods needed for data science and engineering applications. It combines straightforward explanations with numerous practical examples and exercises from data science, machine learning and artificial intelligence, signal and image processing, navigation, control, and finance.
Автор: Blyth Stephen Название: An Introduction to Quantitative Finance ISBN: 0199666598 ISBN-13(EAN): 9780199666591 Издательство: Oxford Academ Рейтинг: Цена: 48040.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The quantitative nature of complex financial transactions makes them a fascinating subject area for mathematicians of all types. This book gives an insight into financial engineering while building on introductory probability courses by detailing one of the most fascinating applications of the subject.
Автор: Raschka, Sebastian Mirjalili, Vahid Название: Python machine learning - ISBN: 1787125939 ISBN-13(EAN): 9781787125933 Издательство: Неизвестно Цена: 53940.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This second edition of Python Machine Learning by Sebastian Raschka is for developers and data scientists looking for a practical approach to machine learning and deep learning. In this updated edition, you`ll explore the machine learning process using Python and the latest open source technologies, including scikit-learn and TensorFlow 1.x.
Автор: Pyrhonen Название: Electrical Machine Drives Control - An Introduction ISBN: 1119260450 ISBN-13(EAN): 9781119260455 Издательство: Wiley Рейтинг: Цена: 104490.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This comprehensive text examines existing and emerging electrical drive technologies. The authors clearly define the most basic electrical drive concepts and go on to explain the most important details while maintaining a solid connection to the theory and design of the associated electrical machines.
If you're looking for a way to become an expert in machine learning, then keep reading...
Machine learning is an incredibly dense topic. It's hard to imagine condensing it into an easily readable and digestible format. However, this book aims to do exactly that.
Throughout the course of this book, we're going to be covering numerous different aspects of machine learning, such as:
The different types of learning algorithm that you can expect to encounter
The numerous applications of machine learning
The future of machine learning
What neural networks and deep learning are
The best practices for picking up machine learning
What languages and libraries to work with
The different types of machine learning and how they differ
The various problems that you can solve with machine learning algorithms
And much more...
Starting from nothing, we slowly work our way through all the concepts that are central to machine learning. By the end of this book, you're going to feel as though you have an extremely firm understanding of what machine learning is, how it can be used, and most importantly, how it can change the world. You're also going to have an understanding of the logic behind the algorithms and what they aim to accomplish.
Don't waste your time working with a book that's only going to make an already complicated topic even more complicated. Pick up this book and learn everything you need to know in no time
Автор: Mitra, Sushmita , Datta, Sujay , Perkins, Theodo Название: Introduction to Machine Learning and Bioinformatics ISBN: 0367387239 ISBN-13(EAN): 9780367387235 Издательство: Taylor&Francis Рейтинг: Цена: 65320.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Lucidly Integrates Current Activities
Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other.
Examines Connections between Machine Learning & Bioinformatics
The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation, the authors explore how machine learning techniques apply to bioinformatics problems, such as electron density map interpretation, biclustering, DNA sequence analysis, and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website.
Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems
Shedding light on aspects of both machine learning and bioinformatics, this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today's biological experiments.
If you're looking for a way to become an expert in machine learning, then keep reading...
Machine learning is an incredibly dense topic. It's hard to imagine condensing it into an easily readable and digestible format. However, this book aims to do exactly that.
Throughout the course of this book, we're going to be covering numerous different aspects of machine learning, such as:
The different types of learning algorithm that you can expect to encounter
The numerous applications of machine learning
The future of machine learning
What neural networks and deep learning are
The best practices for picking up machine learning
What languages and libraries to work with
The different types of machine learning and how they differ
The various problems that you can solve with machine learning algorithms
And much more...
Starting from nothing, we slowly work our way through all the concepts that are central to machine learning. By the end of this book, you're going to feel as though you have an extremely firm understanding of what machine learning is, how it can be used, and most importantly, how it can change the world. You're also going to have an understanding of the logic behind the algorithms and what they aim to accomplish.
Don't waste your time working with a book that's only going to make an already complicated topic even more complicated. Pick up this book and learn everything you need to know in no time
Автор: Chirag Shah Название: A Hands-On Introduction to Data Science ISBN: 1108472443 ISBN-13(EAN): 9781108472449 Издательство: Cambridge Academ Рейтинг: Цена: 48570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: A practical introduction to data science with a low barrier entry, this textbook is well-suited to students from a range of disciplines. Assuming no prior knowledge of the subject, the hands-on exercises and real-life application of popular data science tools are accessible even to students without a strong technical background.
Автор: Kulkarni, Shrirang Ambaji , Gurupur, Varadrah P. Название: Introduction to IoT with Machine Learning and Image Processing using Raspberry Pi ISBN: 1138543527 ISBN-13(EAN): 9781138543522 Издательство: Taylor&Francis Рейтинг: Цена: 163330.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book introduces Raspberry Pi, using real world applications in computer vision, machine learning, and deep learning. It provides a detailed, step-by-step, approach to application development for users without any prior programming knowledge.
Автор: Kingma, Diederik P. Welling, Max Название: Introduction to variational autoencoders ISBN: 1680836226 ISBN-13(EAN): 9781680836226 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 67450.00 T Наличие на складе: Нет в наличии. Описание: Presents an introduction to the framework of variational autoencoders (VAEs) that provides a principled method for jointly learning deep latent-variable models and corresponding inference models using stochastic gradient descent.
Автор: Masashi Sugiyama Название: Introduction to Statistical Machine Learning ISBN: 0128021217 ISBN-13(EAN): 9780128021217 Издательство: Elsevier Science Рейтинг: Цена: 114530.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials.
Introduction to Statistical Machine Learning provides ageneral introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks.
Provides the necessary background material to understand machine learning such as statistics, probability, linear algebra, and calculus
Complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning
Includes MATLAB/Octave programs so that readers can test the algorithms numerically and acquire both mathematical and practical skills in a wide range of data analysis tasks
Discusses a wide range of applications in machine learning and statistics and provides examples drawn from image processing, speech processing, natural language processing, robot control, as well as biology, medicine, astronomy, physics, and materials
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