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Time Series Algorithms Recipes, Kulkarni


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Цена: 32600.00T
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Наличие: Поставка под заказ.  Есть в наличии на складе поставщика.
Склад Америка: 210 шт.  
При оформлении заказа до: 2025-07-28
Ориентировочная дата поставки: Август-начало Сентября
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Автор: Kulkarni
Название:  Time Series Algorithms Recipes
ISBN: 9781484289778
Издательство: Springer
Классификация:


ISBN-10: 1484289773
Обложка/Формат: Soft cover
Страницы: 174
Вес: 0.30 кг.
Дата издания: 07.01.2023
Язык: English
Издание: 1st ed.
Иллюстрации: 97 illustrations, black and white; xvi, 174 p. 97 illus.
Размер: 235 x 155
Читательская аудитория: Professional & vocational
Основная тема: Computer Science
Подзаголовок: Implement machine learning and deep learning techniques with python
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average). Next, youll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. Youll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations. After finishing this book, you will have a foundational understanding of various concepts relating to time series and its implementation in Python. What You Will Learn * Implement various techniques in time series analysis using Python. * Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average) and ARIMA (autoregressive integrated moving-average) for time series forecasting * Understand univariate and multivariate modeling for time series forecasting * Forecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory) Who This Book Is For Data Scientists, Machine Learning Engineers, and software developers interested in time series analysis.
Дополнительное описание: Chapter 1: Getting Started with Time Series.- Chapter 2: Statistical Univariate Modelling.- Chapter 3: Statistical Multivariate Modelling.- Chapter 4: Machine Learning Regression-Based Forecasting.- Chapter 5: Forecasting Using Deep Learning.


Computer Age Statistical Inference, Student Edition

Автор: Bradley Efron , Trevor Hastie
Название: Computer Age Statistical Inference, Student Edition
ISBN: 1108823416 ISBN-13(EAN): 9781108823418
Издательство: Cambridge Academ
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Цена: 33790.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Computing power has revolutionized the theory and practice of statistical inference. Now in paperback, and fortified with 130 class-tested exercises, this book explains modern statistical thinking from classical theories to state-of-the-art prediction algorithms. Anyone who applies statistical methods to data will value this landmark text.

Machine Learning Guide for Oil and Gas Using Python: A Step-By-Step Breakdown with Data, Algorithms, Codes, and Applications

Автор: Belyadi Hoss, Haghighat Alireza
Название: Machine Learning Guide for Oil and Gas Using Python: A Step-By-Step Breakdown with Data, Algorithms, Codes, and Applications
ISBN: 0128219297 ISBN-13(EAN): 9780128219294
Издательство: Elsevier Science
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Цена: 129130.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:

Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The reference moves from explaining how Python works to step-by-step examples of utilization in various oil and gas scenarios, such as well testing, shale reservoirs and production optimization. Petroleum engineers are quickly applying machine learning techniques to their data challenges, but there is a lack of references beyond the math or heavy theory of machine learning. Machine Learning Guide for Oil and Gas Using Python details the open-source tool Python by explaining how it works at an introductory level then bridging into how to apply the algorithms into different oil and gas scenarios. While similar resources are often too mathematical, this book balances theory with applications, including use cases that help solve different oil and gas data challenges.


Robotics, Vision and Control, 2 ed.

Автор: Corke, Peter
Название: Robotics, Vision and Control, 2 ed.
ISBN: 3319544128 ISBN-13(EAN): 9783319544120
Издательство: Springer
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Цена: 0.00 T
Наличие на складе: Невозможна поставка.
Описание: Robotic vision, the combination of robotics and computer vision, involves the application of computer algorithms to data acquired from sensors. It weaves together theory, algorithms and examples in a narrative that covers robotics and computer vision separately and together.

Artificial Intelligence for Signal Processing and Wireless Communication

Автор: Abhinav Sharma et al.
Название: Artificial Intelligence for Signal Processing and Wireless Communication
ISBN: 3110738821 ISBN-13(EAN): 9783110738827
Издательство: Walter de Gruyter
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Цена: 173490.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:

Without mathematics no science would survive. This especially applies to the engineering sciences which highly depend on the applications of mathematics and mathematical tools such as optimization techniques, finite element methods, differential equations, fluid dynamics, mathematical modelling, and simulation. Neither optimization in engineering, nor the performance of safety-critical system and system security; nor high assurance software architecture and design would be possible without the development of mathematical applications.

De Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences (AMEIS) focusses on the latest applications of engineering and information technology that are possible only with the use of mathematical methods. By identifying the gaps in knowledge of engineering applications the AMEIS series fosters the international interchange between the sciences and keeps the reader informed about the latest developments.


Noise Filtering for Big Data Analytics

Автор: Koushik Ghosh, Souvik Bhattacharyya
Название: Noise Filtering for Big Data Analytics
ISBN: 3110697092 ISBN-13(EAN): 9783110697094
Издательство: Walter de Gruyter
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Цена: 173490.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book explains how to perform data de-noising, in large scale, with a satisfactory level of accuracy. Three main issues are considered. Firstly, how to eliminate the error propagation from one stage to next stages while developing a filtered model.

Secondly, how to maintain the positional importance of data whilst purifying it. Finally, preservation of memory in the data is crucial to extract smart data from noisy big data. If, after the application of any form of smoothing or filtering, the memory of the corresponding data changes heavily, then the final data may lose some important information.

This may lead to wrong or erroneous conclusions. But, when anticipating any loss of information due to smoothing or filtering, one cannot avoid the process of denoising as on the other hand any kind of analysis of big data in the presence of noise can be misleading. So, the entire process demands very careful execution with efficient and smart models in order to effectively deal with it.


Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics

Автор: Sujatha R., Aarthy S. L., Vettriselvan R.
Название: Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics
ISBN: 0367466635 ISBN-13(EAN): 9780367466633
Издательство: Taylor&Francis
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Цена: 117390.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data science revolves around two giants, which are big data analytics and deep learning. It is becoming challenging to handle and retrieve useful information due to how fast data is expanding. This book presents the technologies and tools to simplify and streamline the formation of big data along with deep learning systems.

Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications

Автор: Srinivasa K. G., Siddesh G. M., Manisekhar S. R.
Название: Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications
ISBN: 9811524440 ISBN-13(EAN): 9789811524448
Издательство: Springer
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Цена: 167700.00 T
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Описание: This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics.

Convex Optimization: Algorithms and Complexity

Автор: Sebastian Bubeck.
Название: Convex Optimization: Algorithms and Complexity
ISBN: 1601988605 ISBN-13(EAN): 9781601988607
Издательство: Mare Nostrum (Eurospan)
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Цена: 84090.00 T
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Описание: Presents the main complexity theorems in convex optimization and their corresponding algorithms. The book begins with the fundamental theory of black-box optimization and proceeds to guide the reader through recent advances in structural optimization and stochastic optimization.

Machine Learning for Cybersecurity Cookbook

Автор: Tsukerman Emmanuel
Название: Machine Learning for Cybersecurity Cookbook
ISBN: 1789614678 ISBN-13(EAN): 9781789614671
Издательство: Неизвестно
Рейтинг:
Цена: 60070.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book helps data scientists and cybersecurity experts on implementing the latest AI techniques in cybersecurity. Concrete and clear steps for implementing ML security systems are provided, saving you months in research and development. By the end of this book, you will be able to build defensive systems to curb cybersecurity threats.

Artificial Intelligence with Python Cookbook: Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch

Автор: Auffarth Ben
Название: Artificial Intelligence with Python Cookbook: Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch
ISBN: 1789133963 ISBN-13(EAN): 9781789133967
Издательство: Неизвестно
Рейтинг:
Цена: 53940.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: If you are looking to build next-generation AI solutions for work or even for your pet projects, you`ll find this cookbook useful. With the help of easy-to-follow recipes, this book will take you through the advanced AI and machine learning approaches and algorithms that are required to build smart models for problem-solving.

Beyond the Worst-Case Analysis of Algorithms

Автор: Tim Roughgarden
Название: Beyond the Worst-Case Analysis of Algorithms
ISBN: 1108494315 ISBN-13(EAN): 9781108494311
Издательство: Cambridge Academ
Рейтинг:
Цена: 61250.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Understanding when and why algorithms work is a fundamental challenge. For problems ranging from clustering to linear programming to neural networks there are significant gaps between empirical performance and prediction based on traditional worst-case analysis. The book introduces exciting new methods for assessing algorithm performance.

Time-of-Flight and Depth Imaging. Sensors, Algorithms and Applications

Автор: Marcin Grzegorzek; Christian Theobalt; Reinhard Ko
Название: Time-of-Flight and Depth Imaging. Sensors, Algorithms and Applications
ISBN: 3642449638 ISBN-13(EAN): 9783642449635
Издательство: Springer
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Цена: 65210.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Cameras for 3D depth imaging, using either time-of-flight (ToF) or structured light sensors, have received a lot of attention recently and have been improved considerably over the last few years.


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