Автор: Mele, Antonio Belderbos, Bob Название: Django 4 by example ISBN: 1801813051 ISBN-13(EAN): 9781801813051 Издательство: Неизвестно Рейтинг: Цена: 10918.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Django 4 by Example is the fourth edition of the best-selling franchise that helps you build web applications. This book will guide you through the entire process of developing professional web applications with Django. The book focuses on explaining how the Django Web Framework works by building multiple projects from the ground up.
Название: Python Programming on Win32 ISBN: 1565926218 ISBN-13(EAN): 9781565926219 Издательство: Wiley Рейтинг: Цена: 7602.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Demonstrates how to use Python as a serious Windows development tool. The book addresses all the basic technolgies for common integration tasks on Windows, explaining both the Windows issues and the Python code needed to glue things together.
Автор: Langtangen Hans Petter Название: A Primer on Scientific Programming with Python ISBN: 3662498863 ISBN-13(EAN): 9783662498866 Издательство: Springer Рейтинг: Цена: 4890.00 р. 6986.00-30% Наличие на складе: Есть (1 шт.) Описание: The book serves as a first introduction to computer programming of scientific applications, using the high-level Python language. The exposition is example and problem-oriented, where the applications are taken from mathematics, numerical calculus, statistics, physics, biology and finance. The book teaches 'Matlab-style' and procedural programming as well as object-oriented programming. High school mathematics is a required background and it is advantageous to study classical and numerical one-variable calculus in parallel with reading this book. Besides learning how to program computers, the reader will also learn how to solve mathematical problems, arising in various branches of science and engineering, with the aid of numerical methods and programming. By blending programming, mathematics and scientific applications, the book lays a solid foundation for practicing computational science.From the reviews: Langtangen … does an excellent job of introducing programming as a set of skills in problem solving. He guides the reader into thinking properly about producing program logic and data structures for modeling real-world problems using objects and functions and embracing the object-oriented paradigm. … Summing Up: Highly recommended. F. H. Wild III, Choice, Vol. 47 (8), April 2010Those of us who have learned scientific programming in Python ‘on the streets’ could be a little jealous of students who have the opportunity to take a course out of Langtangen’s Primer.” John D. Cook, The Mathematical Association of America, September 2011This book goes through Python in particular, and programming in general, via tasks that scientists will likely perform. It contains valuable information for students new to scientific computing and would be the perfect bridge between an introduction to programming and an advanced course on numerical methods or computational science.Alex Small, IEEE, CiSE Vol. 14 (2), March?/April 2012 “This fourth edition is awonderful, inclusive textbook that covers pretty much everything one needs toknow to go from zero to fairly sophisticated scientific programming in Python…”Joan Horvath, Computing Reviews, March2015
Описание: Book presents the theoretical aspects of surface and ground water hydraulics, contaminant transport, hydrological and water resource systems. It explains the solution approaches to solve the required analytical and partial differential equations through computer codes and their applications. The codes are based on the Python computational platform.
Название: Python for MBAs ISBN: 0231193920 ISBN-13(EAN): 9780231193924 Издательство: Wiley Рейтинг: Цена: 18533.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is an introduction to programming with Python for MBA students and others in business positions who need a crash course. Beginning with fundamentals such as variables, strings, lists, and functions, it builds up to data analytics and practical ways to derive value from large and complex datasets.
Автор: Yogendra Narayan Pandey et al Название: Machine learning in the oil and gas industry ISBN: 1484260937 ISBN-13(EAN): 9781484260937 Издательство: Springer Рейтинг: Цена: 6288.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Apply machine and deep learning to solve some of the challenges in the oil and gas industry. The book begins with a brief discussion of the oil and gas exploration and production life cycle in the context of data flow through the different stages of industry operations. This leads to a survey of some interesting problems, which are good candidates for applying machine and deep learning approaches.
The initial chapters provide a primer on the Python programming language used for implementing the algorithms; this is followed by an overview of supervised and unsupervised machine learning concepts. The authors provide industry examples using open source data sets along with practical explanations of the algorithms, without diving too deep into the theoretical aspects of the algorithms employed. Machine Learning in the Oil and Gas Industry covers problems encompassing diverse industry topics, including geophysics (seismic interpretation), geological modeling, reservoir engineering, and production engineering.
Throughout the book, the emphasis is on providing a practical approach with step-by-step explanations and code examples for implementing machine and deep learning algorithms for solving real-life problems in the oil and gas industry. What You Will LearnUnderstanding the end-to-end industry life cycle and flow of data in the industrial operations of the oil and gas industryGet the basic concepts of computer programming and machine and deep learning required for implementing the algorithms usedStudy interesting industry problems that are good candidates for being solved by machine and deep learningDiscover the practical considerations and challenges for executing machine and deep learning projects in the oil and gas industry Who This Book Is For Professionals in the oil and gas industry who can benefit from a practical understanding of the machine and deep learning approach to solving real-life problems.
Автор: Idris, Ivan (Author) Название: Python Data Analysis Cookbook ISBN: 178528228X ISBN-13(EAN): 9781785282287 Издательство: Неизвестно Рейтинг: Цена: 10114.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Over 140 practical recipes to help you make sense of your data with ease and build production-ready data appsAbout This Book* Analyze Big Data sets, create attractive visualizations, and manipulate and process various data types* Packed with rich recipes to help you learn and explore amazing algorithms for statistics and machine learning* Authored by Ivan Idris, expert in python programming and proud author of eight highly reviewed booksWho This Book Is ForThis book teaches Python data analysis at an intermediate level with the goal of transforming you from journeyman to master. Basic Python and data analysis skills and affinity are assumed. What You Will Learn* Set up reproducible data analysis* Clean and transform data* Apply advanced statistical analysis* Create attractive data visualizations* Web scrape and work with databases, Hadoop, and Spark* Analyze images and time series data* Mine text and analyze social networks* Use machine learning and evaluate the results* Take advantage of parallelism and concurrencyIn DetailData analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns.
As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning. Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas.
You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You'll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining. In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods.
Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code. By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios.
Style and ApproachThe book is written in "cookbook" style striving for high realism in data analysis. Through the recipe-based format, you can read each recipe separately as required and immediately apply the knowledge gained.
Автор: John M. Stewart Название: Python for Scientists ISBN: 1316641236 ISBN-13(EAN): 9781316641231 Издательство: Cambridge Academ Рейтинг: Цена: 5067.00 р. Наличие на складе: Поставка под заказ.
Описание: Scientific Python is taught from scratch in this book via copious, downloadable, useful and adaptable code snippets. Everything the working scientist needs to know is covered, quickly providing researchers and research students with the skills to start using Python effectively.
Описание: Learn the concepts of time series from traditional to bleeding-edge techniques. This book uses comprehensive examples to clearly illustrate statistical approaches and methods of analyzing time series data and its utilization in the real world. All the code is available in Jupyter notebooks.
You'll begin by reviewing time series fundamentals, the structure of time series data, pre-processing, and how to craft the features through data wrangling. Next, you'll look at traditional time series techniques like ARMA, SARIMAX, VAR, and VARMA using trending framework like StatsModels and pmdarima. The book also explains building classification models using sktime, and covers advanced deep learning-based techniques like ANN, CNN, RNN, LSTM, GRU and Autoencoder to solve time series problem using Tensorflow.
It concludes by explaining the popular framework fbprophet for modeling time series analysis. After reading Hands -On Time Series Analysis with Python, you'll be able to apply these new techniques in industries, such as oil and gas, robotics, manufacturing, government, banking, retail, healthcare, and more. What You'll Learn:* Explains basics to advanced concepts of time series* How to design, develop, train, and validate time-series methodologies* What are smoothing, ARMA, ARIMA, SARIMA,SRIMAX, VAR, VARMA techniques in time series and how to optimally tune parameters to yield best results* Learn how to leverage bleeding-edge techniques such as ANN, CNN, RNN, LSTM, GRU, Autoencoder to solve both Univariate and multivariate problems by using two types of data preparation methods for time series.
* Univariate and multivariate problem solving using fbprophet. Who This Book Is ForData scientists, data analysts, financial analysts, and stock market researchers
Описание: This new edition is completely revised and updated to work with Python 3. In addition to new chapters on network data analysis with ELK stack (Elasticsearch, Logstash, Kibana, and Beats) and Azure Cloud Networking, it also includes updates on using newer libraries such as pyATS, Genie, and Nornir, as well as Ansible 2.8.
Автор: Pagonis Vasilis Название: Luminescence Signal Analysis Using Python ISBN: 3030967972 ISBN-13(EAN): 9783030967970 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book compiles and presents a complete package of open-access Python software code for luminescence signal analysis in the areas of radiation dosimetry, luminescence dosimetry, and luminescence dating. Featuring more than 90 detailed worked examples of Python code, fully integrated into the text, 16 chapters summarize the theory and equations behind the subject matter, while presenting the practical Python codes used to analyze experimental data and extract the various parameters that mathematically describe the luminescence signals. Several examples are provided of how researchers can use and modify the available codes for different practical situations. Types of luminescence signals analyzed in the book are thermoluminescence (TL), isothermal luminescence (ITL), optically stimulated luminescence (OSL), infrared stimulated luminescence (IRSL), timeresolved luminescence (TR) and dose response of dosimetric materials. The open-access Python codes are available at GitHub. The book is well suited to the broader scientific audience using the tools of luminescence dosimetry: physicists, geologists, archaeologists, solid-state physicists, medical physicists, and all scientists using luminescence dosimetry in their research. The detailed code provided allows both students and researchers to be trained quickly and efficiently on the practical aspects of their work, while also providing an overview of the theory behind the analytical equations.
Develop skills in Python by implementing exciting algorithms, including mathematical functions, classical searching, data analysis, plotting data, machine learning techniques, and quantum circuits
Key Features:
Learn Python basics to write elegant and efficient code
Create quantum circuits and algorithms using Qiskit and run them on quantum computing hardware and simulators
Delve into Python's advanced features, including machine learning, analyzing data, and searching
Book Description:
Coding is the art and engineering of creating software, and Python has been one of the core coding languages for many years. This introductory Python book helps you learn classical and quantum computing in a unified and practical way. It will help you explore work with numbers, strings, collections, iterators, and files.
The book goes beyond functions and classes and teaches you to use Python and Qiskit to create gates and circuits for classical and quantum computing. Learn how quantum extends classical techniques using the Grover Search Algorithm and the code that implements it. Dive into some advanced and widely used applications of Python and revisit strings with more sophisticated tools such as regular expressions and basic natural language processing (NLP). The final chapters introduce you to data analysis, visualizations, and supervised and unsupervised machine learning. By the end of the book, you will be proficient in classical coding and programming the latest and most powerful quantum computers.
What You Will Learn:
Create Python code using numbers, strings, collections, classes, objects, functions, conditionals, loops, and operators
Write succinct code the Pythonic way using magic methods, iterators, and generators
Explore different quantum gates and use them to build quantum circuits
Analyze data, build basic machine learning models and plot the results
Search for information using traditional methods and the quantum Grover Search Algorithm
Optimize and test your code to run efficiently
Who this book is for:
The book is for Python and coding beginners. Basic familiarity with algebra, geometry, trigonometry, and logarithms is required as the book does not cover the detailed mathematics and theory of quantum computing. You can check out the author's Dancing with Qubits book, also published by Packt, for an approachable and comprehensive introduction to quantum computing.
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