Introduction to Machine Learning in the Cloud with Python: Concepts and Practices, Gupta Pramod, Sehgal Naresh K.
Автор: Raschka, Sebastian Mirjalili, Vahid Название: Python machine learning - ISBN: 1787125939 ISBN-13(EAN): 9781787125933 Издательство: Неизвестно Цена: 8091.00 р. 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: Boyd Stephen Название: Introduction to Applied Linear Algebra ISBN: 1316518965 ISBN-13(EAN): 9781316518960 Издательство: Cambridge Academ Рейтинг: Цена: 6811.00 р. 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.
Описание: This book provides an introduction to machine learning and cloud computing, both from a conceptual level, along with their usage with underlying infrastructure.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.
The updated edition of this practical book uses concrete examples, minimal theory, and three production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.
Описание: This class-tested textbook will provide in-depth coverage of the fundamentals of machine learning, with an exploration of applications in information security. The book will cover malware detection, cryptography, and intrusion detection. The book will be relevant for students in machine learning and computer security courses.
Автор: Sehgal Naresh Kumar, Bhatt Pramod Chandra P., Acken John M. Название: Cloud Computing with Security: Concepts and Practices ISBN: 3030246140 ISBN-13(EAN): 9783030246143 Издательство: Springer Рейтинг: Цена: 11878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides readers with an overview of Cloud Computing, starting with historical background on mainframe computers and early networking protocols, leading to current concerns such as hardware and systems security, performance, emerging areas of IoT, Edge Computing etc.
Описание: Machine Learning Concepts with Python and the Jupyter Notebook Environment
Chapter 1: An Overview of Artificial Intelligence
Chapter 2: An Overview of Machine Learning
Chapter 3: Introduction to Deep Learning
Chapter 4: Machine Learning Versus Deep Learning
Chapter 5: Machine Learning with Python
Chapter 6: Introduction to Jupyter Notebooks
Chapter 7: Python Programming on the Jupyter Notebook
Chapter 8: The Tensorflow Machine Learning Library
Chapter 9: Programming with Tensorflow 1.0
Chapter 10: Introducing TensorFlow 2.0
Chapter 11: Machine Learning Programming with TensorFlow 2.0
Автор: M. Antonia Amaral Turkman, Carlos Daniel Paulino, Peter Muller Название: Computational Bayesian Statistics: An Introduction ISBN: 1108481035 ISBN-13(EAN): 9781108481038 Издательство: Cambridge Academ Рейтинг: Цена: 17424.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book explains the fundamental ideas of Bayesian analysis, with a focus on computational methods such as MCMC and available software such as R/R-INLA, OpenBUGS, JAGS, Stan, and BayesX. It is suitable as a textbook for a first graduate-level course and as a user`s guide for researchers and graduate students from beyond statistics.
Описание: Please note that this book is not a sequel to the First Edition, but rather a restructured and revamped version of the First Edition. Ready to crank up a virtual server and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile?Well, hold on there...Before you embark on your epic journey into the world of machine learning, there is some theory and statistical principles to march through first. But rather than spend $30-$50 USD on a dense long textbook, you may want to read this book first. As a clear and concise alternative to a textbook, this book provides a practical and high-level introduction to the practical components and statistical concepts found in machine learning. Machine Learning for Absolute Beginners Second Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home.This major new edition features many topics not covered in the First Edition, including Cross Validation, Data Scrubbing and Ensemble Modeling. Please note that this book is not a sequel to the First Edition, but rather a restructured and revamped version of the First Edition. Readers of the First Edition should not feel compelled to purchase this Second Edition. Disclaimer: If you have passed the 'beginner' stage in your study of machine learning and are ready to tackle coding and deep learning, you would be well served with a long-format textbook. If, however, you are yet to reach that Lion King moment
Автор: Kingma, Diederik P. Welling, Max Название: Introduction to variational autoencoders ISBN: 1680836226 ISBN-13(EAN): 9781680836226 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 10118.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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: Alpaydin Ethem Название: Introduction to Machine Learning ISBN: 0262043793 ISBN-13(EAN): 9780262043793 Издательство: MIT Press Рейтинг: Цена: 14390.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A substantially revised fourth edition of a comprehensive textbook, including new coverage of recent advances in deep learning and neural networks.
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Machine learning underlies such exciting new technologies as self-driving cars, speech recognition, and translation applications. This substantially revised fourth edition of a comprehensive, widely used machine learning textbook offers new coverage of recent advances in the field in both theory and practice, including developments in deep learning and neural networks.
The book covers a broad array of topics not usually included in introductory machine learning texts, including supervised learning, Bayesian decision theory, parametric methods, semiparametric methods, nonparametric methods, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing. The fourth edition offers a new chapter on deep learning that discusses training, regularizing, and structuring deep neural networks such as convolutional and generative adversarial networks; new material in the chapter on reinforcement learning that covers the use of deep networks, the policy gradient methods, and deep reinforcement learning; new material in the chapter on multilayer perceptrons on autoencoders and the word2vec network; and discussion of a popular method of dimensionality reduction, t-SNE. New appendixes offer background material on linear algebra and optimization. End-of-chapter exercises help readers to apply concepts learned. Introduction to Machine Learning can be used in courses for advanced undergraduate and graduate students and as a reference for professionals.
Автор: Slivkins, Aleksandrs Название: Introduction to multi-armed bandits ISBN: 168083620X ISBN-13(EAN): 9781680836202 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 13306.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Provides a textbook like treatment of multi-armed bandits. The work on multi-armed bandits can be partitioned into a dozen or so directions. Each chapter tackles one line of work, providing a self-contained introduction and pointers for further reading.
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