Introduction to Deep Learning for Engineers: Using Python and Google Cloud Platform, Tariq M. Arif
Автор: Louie Stowell Название: Coding for Beginners: Using Python ISBN: 1409599345 ISBN-13(EAN): 9781409599340 Издательство: Usborne Рейтинг: Цена: 13250.00 T Наличие на складе: Есть Описание: A beginner`s guide to coding using Python, one of the most popular computer languages. Step-by-step instructions show how to get started and write a simple program. New commands are introduced with examples and colourful pictures so by the end of the book, readers can code games, drawings and more. Includes extra help and downloads online.
Автор: Guido Sarah Название: Introduction to Machine Learning with Python ISBN: 1449369413 ISBN-13(EAN): 9781449369415 Издательство: Wiley Рейтинг: Цена: 63350.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions.
Автор: Fehr Hans Название: Introduction to Computational Economics Using Fortran ISBN: 0198850379 ISBN-13(EAN): 9780198850373 Издательство: Oxford Academ Рейтинг: Цена: 46980.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This exercise and solutions manual accompanies the main edition of Introduction to Computational Economics Using Fortran. It enables students of all levels to practice the skills and knowledge needed to conduct economic research using Fortran.
The revised and updated second edition of this textbook teaches students to create modeling codes used to analyze, design, and optimize structures and systems used in wireless communications, microwave circuits, and other applications of electromagnetic fields and waves. Worked code examples are provided for key algorithms using the MATLAB technical computing language.
The book begins by introducing the field of numerical analysis and providing an overview of the fundamentals of electromagnetic field theory. Further chapters cover basic numerical tasks, finite difference methods, numerical integration, integral equations and the method of moments, solving linear systems, the finite element method, optimization methods, and inverse problems.
Developing and using numerical methods helps students to learn the theory of wave propagation in a concrete, visual, and hands-on way. This book fills the missing space of current textbooks that either lack depth on key topics or treat the topic at a level that is too advanced for undergraduates or first-year graduate students.
Presenting the topic with clear explanations, relevant examples, and problem sets that move from simple algorithms to complex codes with real-world capabilities, this book helps its readers develop the skills required for taking a mathematical prescription for a numerical method and translating it into a working, validated software code, providing a valuable resource for understanding the finite difference method, the method of moments, the finite element method, and other tools used in the RF and wireless industry.
Автор: Martin Osvaldo Название: Bayesian Analysis with Python - Second Edition: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ ISBN: 1789341655 ISBN-13(EAN): 9781789341652 Издательство: Неизвестно Рейтинг: Цена: 60070.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Bayesian inference uses probability distributions and Bayes` theorem to build flexible models. The book uses PyMC3 to abstract all the mathematical and computational details from this process allowing readers to solve a wide range of problems in data science.
Автор: Tariq M. Arif Название: Introduction to Deep Learning for Engineers: Using Python and Google Cloud Platform ISBN: 1681739151 ISBN-13(EAN): 9781681739151 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 57290.00 T Наличие на складе: Нет в наличии. Описание: This book provides a short introduction and easy-to-follow implementation steps of deep learning using Google Cloud Platform.
It also includes a practical case study that highlights the utilization of Python and related libraries for running a pre-trained deep learning model.
In recent years, deep learning-based modeling approaches have been used in a wide variety of engineering domains, such as autonomous cars, intelligent robotics, computer vision, natural language processing, and bioinformatics. Also, numerous real-world engineering applications utilize an existing pre-trained deep learning model that has already been developed and optimized for a related task. However, incorporating a deep learning model in a research project is quite challenging, especially for someone who doesn't have related machine learning and cloud computing knowledge. Keeping that in mind, this book is intended to be a short introduction of deep learning basics through the example of a practical implementation case.
The audience of this short book is undergraduate engineering students who wish to explore deep learning models in their class project or senior design project without having a full journey through the machine learning theories. The case study part at the end also provides a cost-effective and step-by-step approach that can be replicated by others easily.
Makes Numerical Programming More Accessible to a Wider Audience
Bearing in mind the evolution of modern programming, most specifically emergent programming languages that reflect modern practice, Numerical Programming: A Practical Guide for Scientists and Engineers Using Python and C/C++ utilizes the author's many years of practical research and teaching experience to offer a systematic approach to relevant programming concepts. Adopting a practical, broad appeal, this user-friendly book offers guidance to anyone interested in using numerical programming to solve science and engineering problems.Emphasizing methods generally used in physics and engineering--from elementary methods to complex algorithms--it gradually incorporates algorithmic elements with increasing complexity.
Develop a Combination of Theoretical Knowledge, Efficient Analysis Skills, and Code Design Know-How
The book encourages algorithmic thinking, which is essential to numerical analysis. Establishing the fundamental numerical methods, application numerical behavior and graphical output needed to foster algorithmic reasoning, coding dexterity, and a scientific programming style, it enables readers to successfully navigate relevant algorithms, understand coding design, and develop efficient programming skills. The book incorporates real code, and includes examples and problem sets to assist in hands-on learning.
Begins with an overview on approximate numbers and programming in Python and C/C++, followed by discussion of basic sorting and indexing methods, as well as portable graphic functionality
Contains methods for function evaluation, solving algebraic and transcendental equations, systems of linear algebraic equations, ordinary differential equations, and eigenvalue problems
Addresses approximation of tabulated functions, regression, integration of one- and multi-dimensional functions by classical and Gaussian quadratures, Monte Carlo integration techniques, generation of random variables, discretization methods for ordinary and partial differential equations, and stability analysis
This text introduces platform-independent numerical programming using Python and C/C++, and appeals to advanced undergraduate and graduate students in natural sciences and engineering, researchers involved in scientific computing, and engineers carrying out applicative calculations.
Автор: Efron Название: An Introduction to the Bootstrap ISBN: 0412042312 ISBN-13(EAN): 9780412042317 Издательство: Taylor&Francis Рейтинг: Цена: 153120.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: An exploration of the many different bootstrap techniques. It discusses useful statistical techniques through real data examples and covers nonparametric regression, density estimation, classification trees, and least median squares regression. There are numerous exercises.
Автор: Sandeep Nagar Название: Introduction to Python for Engineers and Scientists ISBN: 1484232038 ISBN-13(EAN): 9781484232033 Издательство: Springer Рейтинг: Цена: 26080.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Familiarize yourself with the basics of Python for engineering and scientific computations using this concise, practical tutorial that is focused on writing code to learn concepts. Introduction to Python is useful for industry engineers, researchers, and students who are looking for open-source solutions for numerical computation.
In this book you will learn by doing, avoiding technical jargon, which makes the concepts easy to learn. First you'll see how to run basic calculations, absorbing technical complexities incrementally as you progress toward advanced topics. Throughout, the language is kept simple to ensure that readers at all levels can grasp the concepts.
What You'll Learn
Understand the fundamentals of the Python programming language
Apply Python to numerical computational programming projects in engineering and science
Discover the Pythonic way of life
Apply data types, operators, and arrays
Carry out plotting for visualization
Work with functions and loops
Who This Book Is For
Engineers, scientists, researchers, and students who are new to Python. Some prior programming experience would be helpful but not required.
