Zion Tech Group

Tag: machine learning: an applied mathematics introduction

  • Fundamentals of Complex Analysis with Applications to Engineering and Science (Classic Version) (Pearson Modern Classics for Advanced Mathematics Series)


    Price: $133.32 – $93.73
    (as of Jan 22,2025 19:12:39 UTC – Details)




    Publisher ‏ : ‎ Pearson; 3rd edition (February 13, 2017)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 576 pages
    ISBN-10 ‏ : ‎ 0134689488
    ISBN-13 ‏ : ‎ 978-0134689487
    Item Weight ‏ : ‎ 1.6 pounds
    Dimensions ‏ : ‎ 6.9 x 1.2 x 9.2 inches

    Customers say

    Customers find the book helpful for beginners in learning complex analysis. They appreciate the clear and concise language that articulates challenging material in an easy-to-understand manner. The writing style is well-written and reinforces the concepts presented.

    AI-generated from the text of customer reviews


    In this post, we will explore the classic version of “Fundamentals of Complex Analysis with Applications to Engineering and Science” from the Pearson Modern Classics for Advanced Mathematics Series. This timeless textbook provides a comprehensive introduction to the theory of complex variables and their applications in engineering and science.

    The book covers topics such as complex numbers, functions of a complex variable, contour integration, series, residues, and conformal mapping. It also includes numerous examples and exercises to help readers develop their understanding and problem-solving skills.

    Whether you are a student studying mathematics, engineering, physics, or another scientific discipline, this book is a valuable resource for gaining a deep understanding of complex analysis and its practical applications. It is also suitable for self-study and reference for professionals working in fields that require complex analysis techniques.

    If you are looking to enhance your knowledge of complex analysis and its applications, “Fundamentals of Complex Analysis with Applications to Engineering and Science” is a must-have addition to your library. Get your hands on this classic textbook and unlock the power of complex variables in your studies and research.
    #Fundamentals #Complex #Analysis #Applications #Engineering #Science #Classic #Version #Pearson #Modern #Classics #Advanced #Mathematics #Series,machine learning: an applied mathematics introduction

  • Machine Learning Methods for Ecological Applications



    Machine Learning Methods for Ecological Applications

    Price : 88.69

    Ends on : N/A

    View on eBay
    Machine learning methods have become increasingly popular in ecological research due to their ability to analyze large and complex datasets, identify patterns and trends, and make predictions. In this post, we will explore some of the most commonly used machine learning methods in ecological applications.

    1. Random Forest: Random forest is a versatile and powerful machine learning method that is widely used in ecological research. It works by constructing multiple decision trees and combining their predictions to make more accurate and robust predictions. Random forest can be used for a wide range of ecological tasks, such as species distribution modeling, habitat suitability analysis, and biodiversity estimation.

    2. Support Vector Machines (SVM): SVM is another popular machine learning method that is commonly used in ecological applications. SVM works by finding the optimal hyperplane that separates data points into different classes. It is particularly useful for tasks such as species classification, land cover mapping, and remote sensing analysis.

    3. Neural Networks: Neural networks are a type of deep learning method that can be used for a variety of ecological applications. They are particularly well-suited for tasks that involve complex and non-linear relationships, such as image recognition, time series analysis, and ecological forecasting.

    4. K-means Clustering: K-means clustering is a simple but powerful unsupervised machine learning method that is commonly used in ecological research. It works by partitioning a dataset into a specified number of clusters based on similarities in the data. K-means clustering can be used for tasks such as community ecology analysis, species grouping, and habitat classification.

    5. Convolutional Neural Networks (CNN): CNN is a type of neural network that is specifically designed for analyzing visual data, such as satellite imagery, drone footage, and camera trap images. CNNs are commonly used in ecological applications for tasks such as land cover mapping, species detection, and wildlife monitoring.

    Overall, machine learning methods offer a wide range of tools and techniques that can greatly enhance ecological research and conservation efforts. By leveraging the power of machine learning, ecologists can better understand complex ecological systems, make more informed decisions, and ultimately contribute to the conservation and sustainability of our planet.
    #Machine #Learning #Methods #Ecological #Applications,machine learning: an applied mathematics introduction

  • SAS Applications Programming: A Gentle Introduction


    Price: $199.95 – $18.99
    (as of Jan 22,2025 18:19:48 UTC – Details)




    Publisher ‏ : ‎ Cengage Learning; 1st edition (February 25, 1991)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 684 pages
    ISBN-10 ‏ : ‎ 0534923909
    ISBN-13 ‏ : ‎ 978-0534923907
    Item Weight ‏ : ‎ 3.18 pounds
    Dimensions ‏ : ‎ 8.5 x 1 x 11 inches


    SAS Applications Programming: A Gentle Introduction

    Are you interested in learning about SAS applications programming but don’t know where to start? Look no further! In this post, we will provide a gentle introduction to SAS applications programming, including what it is, why it’s important, and how you can get started.

    SAS applications programming is the process of using the SAS software suite to analyze and manipulate data. This can include tasks such as data cleaning, statistical analysis, and creating reports. SAS is a powerful tool used by businesses, government agencies, and researchers to make data-driven decisions and solve complex problems.

    One of the key benefits of SAS applications programming is its versatility. With SAS, you can work with a wide range of data types, including structured and unstructured data, and perform a variety of analyses. Whether you’re interested in predictive modeling, data visualization, or machine learning, SAS has the tools you need to get the job done.

    Getting started with SAS applications programming is easier than you might think. There are plenty of resources available online, including tutorials, courses, and forums where you can ask questions and get help from experienced SAS programmers. Additionally, SAS offers certification programs that can help you demonstrate your skills and advance your career in the field.

    So, if you’re ready to dive into the world of SAS applications programming, now is the perfect time to get started. With a little bit of practice and dedication, you’ll be well on your way to becoming a proficient SAS programmer in no time. Happy coding!
    #SAS #Applications #Programming #Gentle #Introduction,machine learning: an applied mathematics introduction

  • Schaum’s Outline of Operating Systems (Schaum’s Outlines)


    Price: $20.13
    (as of Jan 22,2025 17:23:20 UTC – Details)




    ASIN ‏ : ‎ B00B22SBUG
    Publisher ‏ : ‎ McGraw Hill; 1st edition (December 21, 2001)
    Publication date ‏ : ‎ December 21, 2001
    Language ‏ : ‎ English
    File size ‏ : ‎ 13939 KB
    Text-to-Speech ‏ : ‎ Enabled
    Screen Reader ‏ : ‎ Supported
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 383 pages


    Are you struggling to understand the complexities of operating systems? Look no further than “Schaum’s Outline of Operating Systems.” This comprehensive guide covers everything you need to know about operating systems, from the basics to more advanced topics.

    With clear explanations, step-by-step examples, and practice problems, this book will help you master the fundamental concepts of operating systems. Whether you’re a student studying for an exam or a professional looking to expand your knowledge, “Schaum’s Outline of Operating Systems” is the perfect resource to have on hand.

    Don’t let operating systems intimidate you any longer – pick up a copy of “Schaum’s Outline of Operating Systems” today and start learning with confidence.
    #Schaums #Outline #Operating #Systems #Schaums #Outlines,machine learning: an applied mathematics introduction

  • Quantum Computing: A Beginner’s Introduction


    Price: $27.34
    (as of Jan 22,2025 16:30:11 UTC – Details)




    Publisher ‏ : ‎ McGraw Hill; 1st edition (November 1, 2020)
    Language ‏ : ‎ English
    ISBN-10 ‏ : ‎ 9390385261
    ISBN-13 ‏ : ‎ 978-9390385263
    Item Weight ‏ : ‎ 7.4 ounces
    Dimensions ‏ : ‎ 9.06 x 5.91 x 0.28 inches

    Customers say

    Customers find the book provides a concise and comprehensive introduction to quantum computing. It contains detailed explanations for several algorithms, including Grover’s search algorithm and Shor’s. However, some readers report grammatical and content errors, as well as typographical errors on key formulas or terms.

    AI-generated from the text of customer reviews


    Quantum Computing: A Beginner’s Introduction

    Are you fascinated by the world of computers and technology? Have you heard about quantum computing but aren’t quite sure what it is or how it works? If so, you’re in the right place! In this post, we’ll provide a beginner’s introduction to quantum computing, breaking down complex concepts into easy-to-understand terms.

    First, let’s start with the basics. Traditional computers, like the one you’re likely using right now, operate using bits. These are binary units of information that can either be a 0 or a 1. Quantum computers, on the other hand, use quantum bits, or qubits, which can exist in a state of 0, 1, or both simultaneously. This allows quantum computers to process information at speeds that far surpass traditional computers.

    One of the key principles of quantum computing is superposition. This is the ability of a qubit to exist in multiple states at once, thanks to the principles of quantum mechanics. Another important concept is entanglement, where qubits become interconnected and can influence each other’s states instantaneously, regardless of the distance between them.

    Quantum computing has the potential to revolutionize a wide range of industries, from healthcare and finance to cybersecurity and artificial intelligence. For example, quantum computers could help us solve complex problems in drug discovery, optimize financial portfolios, and enhance encryption methods to protect sensitive data.

    While quantum computing is still in its early stages of development, scientists and researchers are making significant progress in advancing this technology. Companies like IBM, Google, and Microsoft are investing heavily in quantum computing research, with the goal of creating more powerful and efficient quantum computers in the near future.

    In conclusion, quantum computing is a cutting-edge technology that has the potential to reshape the way we approach computing and problem-solving. By harnessing the power of quantum mechanics, quantum computers can tackle complex problems that are currently beyond the capabilities of traditional computers. As this field continues to evolve, we can expect to see even more exciting breakthroughs and applications in the years to come. Stay tuned for more updates on the fascinating world of quantum computing!
    #Quantum #Computing #Beginners #Introduction,machine learning: an applied mathematics introduction

  • R for SAS and SPSS Users (Statistics and Computing)


    Price: $199.99 – $75.89
    (as of Jan 22,2025 15:37:04 UTC – Details)




    Publisher ‏ : ‎ Springer; 2nd ed. 2011 edition (July 26, 2011)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 714 pages
    ISBN-10 ‏ : ‎ 1461406846
    ISBN-13 ‏ : ‎ 978-1461406846
    Item Weight ‏ : ‎ 2.31 pounds
    Dimensions ‏ : ‎ 6.25 x 1.5 x 9.25 inches

    Customers say

    Customers find that the book provides a good introduction and reference for R programming. They find it easy to read and understand, especially for SPSS or SAS users learning R. The book is useful for beginners, providing useful information like R-based IDEs. Customers appreciate the clear graphics and statistical analysis provided in the book.

    AI-generated from the text of customer reviews


    Are you a SAS or SPSS user looking to expand your statistical and computing skills? Look no further than the powerful programming language R. R is a free, open-source software that is widely used in the fields of statistics and data analysis.

    In this post, we will explore how R can be a valuable tool for SAS and SPSS users looking to enhance their data analysis capabilities. R offers a wide range of packages and functions that make it easy to perform complex statistical analyses, create data visualizations, and manipulate data sets.

    One of the key advantages of R is its flexibility and extensibility. With over 10,000 packages available on the Comprehensive R Archive Network (CRAN), users can easily find tools to suit their specific needs. Whether you are looking to conduct regression analysis, perform clustering, or create interactive plots, there is likely a package available in R that can help you achieve your goals.

    Additionally, R has a strong community of users who contribute to the development of new packages and provide support through online forums and tutorials. This can be especially helpful for SAS and SPSS users who may be new to R and looking for guidance on how to get started.

    Overall, R is a versatile and powerful tool that can complement the skills of SAS and SPSS users. By learning R, you can expand your repertoire of statistical techniques and enhance your ability to analyze and interpret data. So why not give R a try and see how it can take your data analysis to the next level?
    #SAS #SPSS #Users #Statistics #Computing,machine learning: an applied mathematics introduction

  • Introductory Time Series with R (Use R!)


    Price: $59.99 – $37.65
    (as of Jan 22,2025 14:43:14 UTC – Details)




    Publisher ‏ : ‎ Springer; 2009th edition (June 9, 2009)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 272 pages
    ISBN-10 ‏ : ‎ 0387886974
    ISBN-13 ‏ : ‎ 978-0387886978
    Item Weight ‏ : ‎ 2.31 pounds
    Dimensions ‏ : ‎ 6.1 x 0.62 x 9.25 inches

    Customers say

    Customers find the book readable and suitable for R users. However, some feel it’s not worth purchasing, as it lacks depth and spends too much time on results before ARIMA type models. Opinions differ on the introduction, with some finding it informative and useful, while others say it lacks sufficient information for analyses. There are also mixed reviews regarding the depth of the content, with some finding it thorough and appropriate for each topic, while others feel it lacks depth and is shallow.

    AI-generated from the text of customer reviews


    Are you interested in learning about time series analysis using R? Look no further! In this post, we will delve into the basics of time series analysis using the powerful programming language R.

    Time series analysis is a crucial tool in understanding and predicting trends in data that changes over time. Whether you are working with financial data, weather patterns, or any other time-dependent data, mastering time series analysis can give you valuable insights.

    R is a popular programming language among data scientists and statisticians for its robust packages and libraries designed specifically for time series analysis. In this post, we will take you through the fundamentals of time series analysis in R, covering topics such as data visualization, trend analysis, seasonality, and forecasting.

    So, if you are ready to dive into the world of time series analysis with R, stay tuned for our upcoming posts where we will explore this exciting field in more detail. Get your RStudio ready and let’s start analyzing some time series data!
    #Introductory #Time #Series,machine learning: an applied mathematics introduction

  • Introduction To The Theory Of Neural Computation (Santa Fe Institute Series)


    Price: $91.99 – $73.59
    (as of Jan 22,2025 13:46:48 UTC – Details)




    Publisher ‏ : ‎ CRC Press; 1st edition (June 24, 1991)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 350 pages
    ISBN-10 ‏ : ‎ 0201515601
    ISBN-13 ‏ : ‎ 978-0201515602
    Item Weight ‏ : ‎ 1.15 pounds


    Are you interested in learning about the fascinating world of neural computation? Look no further than the Santa Fe Institute Series on the Theory of Neural Computation. In this post, we will provide an introduction to this groundbreaking theory and explain why it is essential for understanding the inner workings of the brain.

    Neural computation is the study of how neurons in the brain process and transmit information. It seeks to understand how these neurons interact to perform complex cognitive tasks, such as learning, memory, and decision-making. By studying the underlying principles of neural computation, researchers can gain insights into how the brain functions and how it can be replicated in artificial intelligence systems.

    The Santa Fe Institute Series on the Theory of Neural Computation is a collection of books and research papers that explore the latest advancements in this field. Written by leading experts in neuroscience and artificial intelligence, these resources provide a comprehensive overview of the principles and techniques used in neural computation.

    Whether you are a student, researcher, or simply curious about the brain, the Theory of Neural Computation is a must-read. Stay tuned for more posts on this exciting topic and start your journey into the world of neural computation today!
    #Introduction #Theory #Neural #Computation #Santa #Institute #Series,machine learning: an applied mathematics introduction

  • Introduction to Scientific Programming and Simulation Using R (Chapman & Hall/CRC The R Series)


    Price: $91.95 – $70.38
    (as of Jan 22,2025 12:50:26 UTC – Details)




    Publisher ‏ : ‎ Chapman and Hall/CRC; 1st edition (March 11, 2009)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 474 pages
    ISBN-10 ‏ : ‎ 1420068725
    ISBN-13 ‏ : ‎ 978-1420068726
    Item Weight ‏ : ‎ 1.75 pounds
    Dimensions ‏ : ‎ 6.5 x 1.25 x 9.5 inches

    Customers say

    Customers find this book helpful for learning scientific programming and simulation using R in computer science. It provides clear examples and satisfying theoretical and practical aspects. They find it easy to read and well-written, with great exercise questions to hone skills. Many consider it an essential text that is worth adding to their collection. The book starts very basic and builds up into complex function writing and more difficult statistical techniques.

    AI-generated from the text of customer reviews


    Are you interested in learning how to use R for scientific programming and simulation? Look no further! In this post, we will introduce you to the book “Introduction to Scientific Programming and Simulation Using R” from Chapman & Hall/CRC The R Series.

    This book serves as a comprehensive guide for beginners and experienced programmers alike who want to utilize R for scientific applications. It covers topics such as data visualization, statistical analysis, and simulation techniques using R programming language.

    With the help of this book, you will learn how to write efficient and reproducible code for scientific research, visualize data using various graphical techniques, and perform statistical analysis and simulations for scientific experiments.

    Whether you are a student, researcher, or a professional in the field of science, this book will equip you with the necessary skills to leverage the power of R for your scientific programming and simulation needs.

    So, if you are ready to dive into the world of scientific programming and simulation using R, be sure to check out “Introduction to Scientific Programming and Simulation Using R” from Chapman & Hall/CRC The R Series. Happy coding!
    #Introduction #Scientific #Programming #Simulation #Chapman #HallCRC #Series,machine learning: an applied mathematics introduction

  • Time Series with Python: How to Implement Time Series Analysis and Forecasting Using Python (Financial Data Analytics Using Python Book 2)


    Price: $31.81
    (as of Jan 22,2025 11:50:57 UTC – Details)




    ASIN ‏ : ‎ B0861T6W53
    Publication date ‏ : ‎ April 12, 2020
    Language ‏ : ‎ English
    File size ‏ : ‎ 14590 KB
    Simultaneous device usage ‏ : ‎ Unlimited
    Text-to-Speech ‏ : ‎ Enabled
    Screen Reader ‏ : ‎ Supported
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 166 pages
    Page numbers source ISBN ‏ : ‎ 1393147380


    In this post, we will explore the world of time series analysis and forecasting using Python. Time series data is a sequence of data points collected at regular intervals over time. This type of data is commonly encountered in finance, economics, weather forecasting, and many other fields.

    In our Financial Data Analytics Using Python Book 2, we will cover the following topics:

    1. Introduction to time series analysis and forecasting
    2. Preparing time series data for analysis
    3. Exploratory data analysis of time series data
    4. Time series decomposition
    5. ARIMA modeling for time series forecasting
    6. Seasonal ARIMA modeling
    7. Prophet modeling for time series forecasting
    8. Evaluating time series forecasting models
    9. Forecasting future values of time series data

    We will provide step-by-step instructions and code examples to help you implement time series analysis and forecasting using Python. By the end of this book, you will have the skills and knowledge to analyze and forecast time series data for financial applications.

    Stay tuned for more updates on Financial Data Analytics Using Python Book 2 and unlock the power of time series analysis and forecasting with Python.
    #Time #Series #Python #Implement #Time #Series #Analysis #Forecasting #Python #Financial #Data #Analytics #Python #Book,machine learning: an applied mathematics introduction

  • Hello, how can I help you today?
Gathering thoughts.. ...
Powered by AI
Chat Icon