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Learn Quantum Computing with Python and IBM Quantum Write your own practical quantum programs with Python【電子書籍】[ Robert Loredo ] 楽天Kobo電子書籍ストア	＜p＞＜b＞A step-by-step guide to working with programs that exploit quantum computing principles, with the help of IBM Quantum, Qiskit, and Python＜/b＞＜/p＞＜h2＞Key Features＜/h2＞＜ul＞＜li＞Understand the difference between classical computers and quantum computers＜/li＞＜li＞Work with key quantum computational principles such as superposition, interference, and entanglement, and observe how they are leveraged on the IBM Quantum systems＜/li＞＜li＞Run your own quantum experiments and applications by integrating with Qiskit and Python＜/li＞＜/ul＞＜h2＞Book Description＜/h2＞The IBM Quantum Platform was built to enable developers to learn the basics of quantum computing by providing access to high performant quantum computers and provide documentation and courses to help get up to speed with the latest features in quantum computing. Updated with new examples and changes to the platform, this edition begins with an introduction to the IBM Quantum Platform and the Quantum Information Science Kit (Qiskit) SDK. You will become well versed in the IBM Quantum Composer interface as well as running your quantum circuits on a real quantum computer. Along the way, you’ll learn some of the fundamental principles regarding quantum mechanics, qubits, quantum gates, quantum circuits, and the error mitigation techniques that are used to perform operations on qubits. As you build on your knowledge, you’ll understand the functionality of IBM Qiskit and the developer-focused resources so you can create your own quantum algorithms. You’ll learn how to monitor and optimize your quantum circuits. Lastly, you’ll look at the fundamental quantum algorithms and understand how they can be applied effectively. By the end of this quantum computing book, you'll know how to build quantum algorithms and will have gained a practical understanding of quantum computation that you can apply to your research or business. ＜h2＞What you will learn＜/h2＞＜ul＞＜li＞Get familiar with the features within the IBM Quantum Platform＜/li＞＜li＞Create and visualize quantum gates and circuits＜/li＞＜li＞Operate quantum gates on qubits using the IBM Quantum Composer＜/li＞＜li＞Install and run your quantum circuits on an IBM Quantum computer＜/li＞＜li＞Discover Qiskit and its many features such as the Qiskit Runtime＜/li＞＜li＞Get to grips with fundamental quantum algorithms and error mitigation techniques to help you get started＜/li＞＜li＞Understand the new era of quantum utility and how this moves us closer towards quantum advantage＜/li＞＜/ul＞＜h2＞Who this book is for＜/h2＞＜p＞This book is for Python developers who are looking to learn quantum computing from the ground up and put their knowledge to use in practical situations with the help of the IBM Quantum Platform and Qiskit. Some background in computer science and high school-level physics and math is required. ＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	3,874円
Mastering Time Series Analysis and Forecasting with Python: Bridging Theory and Practice Through Insights, Techniques, and Tools for Effective Time Series Analysis in Python【電子書籍】[ Sulekha Aloorravi ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Decode the language of time with Python. Discover powerful techniques to analyze, forecast, and innovate.＜/strong＞＜/p＞＜p＞＜strong＞Key Features＜/strong＞＜/p＞＜p＞● Dive into time series analysis fundamentals, progressing to advanced Python techniques.＜/p＞＜p＞● Gain practical expertise with real-world datasets and hands-on examples.＜/p＞＜p＞● Strengthen skills with code snippets, exercises, and projects for deeper understanding.＜/p＞＜p＞＜strong＞Book Description＜/strong＞＜/p＞＜p＞"＜strong＞Mastering Time Series Analysis and Forecasting with Python＜/strong＞" is an essential handbook tailored for those seeking to harness the power of time series data in their work.＜/p＞＜p＞The book begins with foundational concepts and seamlessly guides readers through Python libraries such as Pandas, NumPy, and Plotly for effective data manipulation, visualization, and exploration. Offering pragmatic insights, it enables adept visualization, pattern recognition, and anomaly detection.＜/p＞＜p＞Advanced discussions cover feature engineering and a spectrum of forecasting methodologies, including machine learning and deep learning techniques such as ARIMA, LSTM, and CNN. Additionally, the book covers multivariate and multiple time series forecasting, providing readers with a comprehensive understanding of advanced modeling techniques and their applications across diverse domains.＜/p＞＜p＞Readers develop expertise in crafting precise predictive models and addressing real-world complexities. Complete with illustrative examples, code snippets, and hands-on exercises, this manual empowers readers to excel, make informed decisions, and derive optimal value from time series data.＜/p＞＜p＞＜strong＞What you will learn＜/strong＞＜/p＞＜p＞● Understand the fundamentals of time series data, including temporal patterns, trends, and seasonality.＜/p＞＜p＞● Proficiently utilize Python libraries such as pandas, NumPy, and matplotlib for efficient data manipulation and visualization.＜/p＞＜p＞● Conduct exploratory analysis of time series data, including identifying patterns, detecting anomalies, and extracting meaningful features.＜/p＞＜p＞● Build accurate and reliable predictive models using a variety of machine learning and deep learning techniques, including ARIMA, LSTM, and CNN.＜/p＞＜p＞● Perform multivariate and multiple time series forecasting, allowing for more comprehensive analysis and prediction across diverse datasets.＜/p＞＜p＞● Evaluate model performance using a range of metrics and validation techniques, ensuring the reliability and robustness of predictive models.＜/p＞＜p＞＜strong＞Table of Contents＜/strong＞＜/p＞＜ol＞＜li＞＜p＞Introduction to Time Series＜/p＞＜/li＞＜li＞＜p＞Overview of Time Series Libraries in Python＜/p＞＜/li＞＜li＞＜p＞Visualization of Time Series Data＜/p＞＜/li＞＜li＞＜p＞Exploratory Analysis of Time Series Data＜/p＞＜/li＞＜li＞＜p＞Feature Engineering on Time Series＜/p＞＜/li＞＜li＞＜p＞Time Series Forecasting ? ML Approach Part 1＜/p＞＜/li＞＜li＞＜p＞Time Series Forecasting ? ML Approach Part 2＜/p＞＜/li＞＜li＞＜p＞Time Series Forecasting - DL Approach＜/p＞＜/li＞＜li＞＜p＞Multivariate Time Series, Metrics, and Validation＜/p＞＜/li＞＜/ol＞＜p＞＜strong＞Index＜/strong＞＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	2,952円
Modern Data Architectures with Python A practical guide to building and deploying data pipelines, data warehouses, and data lakes with Python【電子書籍】[ Brian Lipp ] 楽天Kobo電子書籍ストア	＜p＞＜b＞Build scalable and reliable data ecosystems using Data Mesh, Databricks Spark, and Kafka＜/b＞＜/p＞＜h2＞Key Features＜/h2＞＜ul＞＜li＞Develop modern data skills used in emerging technologies＜/li＞＜li＞Learn pragmatic design methodologies such as Data Mesh and data lakehouses＜/li＞＜li＞Gain a deeper understanding of data governance＜/li＞＜li＞Purchase of the print or Kindle book includes a free PDF eBook＜/li＞＜/ul＞＜h2＞Book Description＜/h2＞Modern Data Architectures with Python will teach you how to seamlessly incorporate your machine learning and data science work streams into your open data platforms. You’ll learn how to take your data and create open lakehouses that work with any technology using tried-and-true techniques, including the medallion architecture and Delta Lake. Starting with the fundamentals, this book will help you build pipelines on Databricks, an open data platform, using SQL and Python. You’ll gain an understanding of notebooks and applications written in Python using standard software engineering tools such as git, pre-commit, Jenkins, and Github. Next, you’ll delve into streaming and batch-based data processing using Apache Spark and Confluent Kafka. As you advance, you’ll learn how to deploy your resources using infrastructure as code and how to automate your workflows and code development. Since any data platform's ability to handle and work with AI and ML is a vital component, you’ll also explore the basics of ML and how to work with modern MLOps tooling. Finally, you’ll get hands-on experience with Apache Spark, one of the key data technologies in today’s market. By the end of this book, you’ll have amassed a wealth of practical and theoretical knowledge to build, manage, orchestrate, and architect your data ecosystems.＜h2＞What you will learn＜/h2＞＜ul＞＜li＞Understand data patterns including delta architecture＜/li＞＜li＞Discover how to increase performance with Spark internals＜/li＞＜li＞Find out how to design critical data diagrams＜/li＞＜li＞Explore MLOps with tools such as AutoML and MLflow＜/li＞＜li＞Get to grips with building data products in a data mesh＜/li＞＜li＞Discover data governance and build confidence in your data＜/li＞＜li＞Introduce data visualizations and dashboards into your data practice＜/li＞＜/ul＞＜h2＞Who this book is for＜/h2＞＜p＞This book is for developers, analytics engineers, and managers looking to further develop a data ecosystem within their organization. While they’re not prerequisites, basic knowledge of Python and prior experience with data will help you to read and follow along with the examples.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	4,304円
Advanced Forecasting with Python With State-of-the-Art-Models Including LSTMs, Facebook’s Prophet, and Amazon’s DeepAR【電子書籍】[ Joos Korstanje ] 楽天Kobo電子書籍ストア	＜p＞Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook’s open-source Prophet model, and Amazon’s DeepAR model.＜/p＞＜p＞Rather than focus on a specific set of models, this book presents an exhaustive overview of all the techniques relevant to practitioners of forecasting. It begins by explaining the different categories of models that are relevant for forecasting in a high-level language. Next, it covers univariate and multivariate time series models followed by advanced machine learning and deep learning models. It concludes with reflections on model selection such as benchmark scores vs. understandability of models vs. compute time, and automated retraining and updating of models.＜/p＞＜p＞Each of the models presented in this book is covered in depth, with an intuitive simple explanation ofthe model, a mathematical transcription of the idea, and Python code that applies the model to an example data set.＜/p＞＜p＞Reading this book will add a competitive edge to your current forecasting skillset. The book is also adapted to those who have recently started working on forecasting tasks and are looking for an exhaustive book that allows them to start with traditional models and gradually move into more and more advanced models.＜/p＞＜p＞＜strong＞What You Will Learn＜/strong＞＜/p＞＜ul＞＜li＞Carry out forecasting with Python＜/li＞＜li＞Mathematically and intuitively understand traditional forecasting models and state-of-the-art machine learning techniques＜/li＞＜li＞Gain the basics of forecasting and machine learning, including evaluation of models, cross-validation, and back testing＜/li＞＜li＞Select the right model for the right use case＜/li＞＜/ul＞＜p＞Who This Book Is For＜/p＞＜p＞The advanced nature of the later chapters makes the book relevant for appliedexperts working in the domain of forecasting, as the models covered have been published only recently. Experts working in the domain will want to update their skills as traditional models are regularly being outperformed by newer models.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	6,685円
Mastering Time Series Analysis and Forecasting with Python Bridging Theory and Practice Through Insights, Techniques, and Tools for Effective Time Series Analysis in Python【電子書籍】[ Sulekha Aloorravi ] 楽天Kobo電子書籍ストア	＜p＞Decode the language of time with Python. Discover powerful techniques to analyze, forecast, and innovate.＜/p＞＜p＞Key Features＜br /＞ ● Dive into time series analysis fundamentals, progressing to advanced Python techniques.＜br /＞ ● Gain practical expertise with real-world datasets and hands-on examples.＜br /＞ ● Strengthen skills with code snippets, exercises, and projects for deeper understanding.＜/p＞＜p＞＜strong＞Book Description＜/strong＞＜br /＞ "＜strong＞Mastering Time Series Analysis and Forecasting with Python＜/strong＞" is an essential handbook tailored for those seeking to harness the power of time series data in their work.＜/p＞＜p＞The book begins with foundational concepts and seamlessly guides readers through Python libraries such as Pandas, NumPy, and Plotly for effective data manipulation, visualization, and exploration. Offering pragmatic insights, it enables adept visualization, pattern recognition, and anomaly detection.＜/p＞＜p＞Advanced discussions cover feature engineering and a spectrum of forecasting methodologies, including machine learning and deep learning techniques such as ARIMA, LSTM, and CNN. Additionally, the book covers multivariate and multiple time series forecasting, providing readers with a comprehensive understanding of advanced modeling techniques and their applications across diverse domains.＜/p＞＜p＞＜strong＞What you will learn＜/strong＞＜br /＞ ● Understand the fundamentals of time series data, including temporal patterns, trends, and seasonality.＜br /＞ ● Proficiently utilize Python libraries such as pandas, NumPy, and matplotlib for efficient data manipulation and visualization.＜br /＞ ● Conduct exploratory analysis of time series data, including identifying patterns, detecting anomalies, and extracting meaningful features.＜br /＞ ● Build accurate and reliable predictive models using a variety of machine learning and deep learning techniques, including ARIMA, LSTM, and CNN.＜br /＞ ● Perform multivariate and multiple time series forecasting, allowing for more comprehensive analysis and prediction across diverse datasets.＜br /＞ ● Evaluate model performance using a range of metrics and validation techniques, ensuring the reliability and robustness of predictive models.＜/p＞＜p＞＜strong＞Who is this book for?＜/strong＞＜br /＞ This book is tailored for data scientists, analysts, professionals, and students seeking to leverage time series data effectively in their work. A foundational understanding of data manipulation techniques using libraries such as pandas and NumPy will be helpful for working with time series datasets. Some understanding of statistical concepts like mean, median, and standard deviation is helpful.＜/p＞＜p＞＜strong＞Table of Contents＜/strong＞＜/p＞＜ol＞＜li＞Introduction to Time Series＜/li＞＜li＞Overview of Time Series Libraries in Python＜/li＞＜li＞Visualization of Time Series Data＜/li＞＜li＞Exploratory Analysis of Time Series Data＜/li＞＜li＞Feature Engineering on Time Series＜/li＞＜li＞Time Series Forecasting ? ML Approach Part 1＜/li＞＜li＞Time Series Forecasting ? ML Approach Part 2＜/li＞＜li＞Time Series Forecasting - DL Approach＜/li＞＜li＞Multivariate Time Series, Metrics, and Validation＜br /＞＜strong＞Index＜/strong＞＜/li＞＜/ol＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	2,952円
Data Structures and Algorithms with Python With an Introduction to Multiprocessing【電子書籍】[ Kent D. Lee ] 楽天Kobo電子書籍ストア	＜p＞This textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently. Project-oriented and classroom-tested, the book presents a number of important algorithmsーsupported by motivating examplesーthat bring meaning to the problems faced by computer programmers. The idea of computational complexity is introduced, demonstrating what can and cannot be computed efficiently at scale, helping programmers make informed judgements about the algorithms they use. The easy-to-read text assumes some basic experience in computer programming and familiarity in an object-oriented language, but not necessarily with Python.＜/p＞＜p＞＜strong＞Topics and features:＜/strong＞＜/p＞＜ul＞＜li＞Includes introductory and advanced data structures and algorithms topics, with suggested chapter sequences for those respective courses＜/li＞＜li＞Provides learning goals, review questions, and programming exercises in each chapter, as well as numerous examples＜/li＞＜li＞Presents a primer on Python for those coming from a different language background＜/li＞＜li＞Adds a new chapter on multiprocessing with Python using the DragonHPC multinode implementation of multiprocessing (includes a tutorial)＜/li＞＜li＞Reviews the use of hashing in sets and maps, and examines binary search trees, tree traversals, and select graph algorithms＜/li＞＜li＞Offers downloadable programs and supplementary files at an associated website to help students＜/li＞＜/ul＞＜p＞Students of computer science will find this clear and concise textbook invaluable for undergraduate courses on data structures and algorithms, at both introductory and advanced levels. The book is also suitable as a refresher guide for computer programmers starting new jobs working with Python.＜/p＞＜p＞＜strong＞Dr. Kent D. Lee＜/strong＞ is a Professor Emeritus of Computer Science at Luther College, Decorah, Iowa, USA. He is the author of the successful Springer books, ＜em＞Python Programming Fundamentals,＜/em＞ and ＜em＞Foundations of Programming Languages.＜/em＞＜/p＞＜p＞＜strong＞Dr. Steve Hubbard＜/strong＞ is a Professor Emeritus of Mathematics and Computer Science at Luther College.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	6,076円
Robo-Advisor with Python A hands-on guide to building and operating your own Robo-advisor【電子書籍】[ Aki Ranin ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Build your own robo-advisor in Python to manage your investments and get up and running in no time＜/strong＞＜/p＞＜p＞＜strong＞Purchase of the print or Kindle book includes a free PDF eBook＜/strong＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞Explore the use cases, workflow, and features that make up robo-advisors＜/li＞＜li＞Learn how to build core robo-advisor capabilities for goals, risk questions, portfolios, and projections＜/li＞＜li＞Discover how to operate the automated processes of a built and deployed robo-advisor＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞＜p＞Robo-advisors are becoming table stakes for the wealth management industry across all segments, from retail to high-net-worth investors. Robo-advisors enable you to manage your own portfolios and financial institutions to create automated platforms for effective digital wealth management. This book is your hands-on guide to understanding how Robo-advisors work, and how to build one efficiently. The chapters are designed in a way to help you get a comprehensive grasp of what Robo-advisors do and how they are structured with an end-to-end workflow.＜/p＞＜p＞You'll begin by learning about the key decisions that influence the building of a Robo-advisor, along with considerations on building and licensing a platform. As you advance, you'll find out how to build all the core capabilities of a Robo-advisor using Python, including goals, risk questionnaires, portfolios, and projections. The book also shows you how to create orders, as well as open accounts and perform KYC verification for transacting. Finally, you'll be able to implement capabilities such as performance reporting and rebalancing for operating a Robo-advisor with ease.＜/p＞＜p＞By the end of this book, you'll have gained a solid understanding of how Robo-advisors work and be well on your way to building one for yourself or your business.＜/p＞＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Explore what Robo-advisors do and why they exist＜/li＞＜li＞Create a workflow to design and build a Robo-advisor from the bottom up＜/li＞＜li＞Build and license Robo-advisors using different approaches＜/li＞＜li＞Open and fund accounts, complete KYC verification, and manage orders＜/li＞＜li＞Build Robo-advisor features for goals, projections, portfolios, and more＜/li＞＜li＞Operate a Robo-advisor with P&L, rebalancing, and fee management＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞If you are a finance professional or a data professional working in wealth management and are curious about how robo-advisors work, this book is for you. It will be helpful to have a basic understanding of Python and investing concepts. This is a great handbook for developers interested in building their own robo-advisor to manage personal investments or build a platform for their business to operate, as well as for product managers and business leaders in financial services looking to lease, buy, or build a robo-advisor.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	4,428円
Derivatives Analytics with Python Data Analysis, Models, Simulation, Calibration and Hedging【電子書籍】[ Yves Hilpisch ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Supercharge options analytics and hedging using the power of Python＜/strong＞＜/p＞＜p＞＜em＞Derivatives Analytics with Python＜/em＞ shows you how to implement market-consistent valuation and hedging approaches using advanced financial models, efficient numerical techniques, and the powerful capabilities of the Python programming language. This unique guide offers detailed explanations of all theory, methods, and processes, giving you the background and tools necessary to value stock index options from a sound foundation. You'll find and use self-contained Python scripts and modules and learn how to apply Python to advanced data and derivatives analytics as you benefit from the 5,000+ lines of code that are provided to help you reproduce the results and graphics presented. Coverage includes market data analysis, risk-neutral valuation, Monte Carlo simulation, model calibration, valuation, and dynamic hedging, with models that exhibit stochastic volatility, jump components, stochastic short rates, and more. The companion website features all code and IPython Notebooks for immediate execution and automation.＜/p＞＜p＞Python is gaining ground in the derivatives analytics space, allowing institutions to quickly and efficiently deliver portfolio, trading, and risk management results. This book is the finance professional's guide to exploiting Python's capabilities for efficient and performing derivatives analytics.＜/p＞＜ul＞＜li＞Reproduce major stylized facts of equity and options markets yourself＜/li＞＜li＞Apply Fourier transform techniques and advanced Monte Carlo pricing＜/li＞＜li＞Calibrate advanced option pricing models to market data＜/li＞＜li＞Integrate advanced models and numeric methods to dynamically hedge options＜/li＞＜/ul＞＜p＞Recent developments in the Python ecosystem enable analysts to implement analytics tasks as performing as with C or C++, but using only about one-tenth of the code or even less. ＜em＞Derivatives Analytics with Python ー Data Analysis, Models, Simulation, Calibration and Hedging＜/em＞ shows you what you need to know to supercharge your derivatives and risk analytics efforts.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	12,408円
MARKETING ANALYSIS AND PREDICTION USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON【電子書籍】[ Vivian Siahaan ] 楽天Kobo電子書籍ストア	＜p＞This data set was provided to students for their final project in order to test their statistical analysis skills as part of a MSc. in Business Analytics. It can be utilized for EDA, Statistical Analysis, and Visualizations.＜/p＞＜p＞Following are the features in the dataset: ID = Customer's unique identifier; Year_Birth = Customer's birth year; Education = Customer's education level; Marital_Status = Customer's marital status; Income = Customer's yearly household income; Kidhome = Number of children in customer's household; Teenhome = Number of teenagers in customer's household; Dt_Customer = Date of customer's enrollment with the company; Recency = Number of days since customer's last purchase; MntWines = Amount spent on wine in the last 2 years; MntFruits = Amount spent on fruits in the last 2 years; MntMeatProducts = Amount spent on meat in the last 2 years; MntFishProducts = Amount spent on fish in the last 2 years; MntSweetProducts = Amount spent on sweets in the last 2 years; MntGoldProds = Amount spent on gold in the last 2 years; NumDealsPurchases = Number of purchases made with a discount; NumWebPurchases = Number of purchases made through the company's web site; NumCatalogPurchases = Number of purchases made using a catalogue; NumStorePurchases = Number of purchases made directly in stores; NumWebVisitsMonth = Number of visits to company's web site in the last month; AcceptedCmp3 = 1 if customer accepted the offer in the 3rd campaign, 0 otherwise; AcceptedCmp4 = 1 if customer accepted the offer in the 4th campaign, 0 otherwise; AcceptedCmp5 = 1 if customer accepted the offer in the 5th campaign, 0 otherwise; AcceptedCmp1 = 1 if customer accepted the offer in the 1st campaign, 0 otherwise; AcceptedCmp2 = 1 if customer accepted the offer in the 2nd campaign, 0 otherwise; Response = 1 if customer accepted the offer in the last campaign, 0 otherwise; Complain = 1 if customer complained in the last 2 years, 0 otherwise; and Country = Customer's location.＜/p＞＜p＞The machine and deep learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	1,627円
Wildlife Wong and the Pygmy Elephant Wildlife Wong, #3【電子書籍】[ Sarah Pye ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Real-life adventure story, facts and experiments for children aged 8-12.＜/strong＞＜/p＞＜p＞Pygmy means 'small', but pygmy elephants are the largest mammals in Borneo, and they can be very dangerous. When Wildlife Wong is tracking bears in the rainforest, he tries his best to avoid them. He isn't always successful. In this story, curious elephants take their own photos with his camera traps, an angry male chases him down the road, and a family of elephants sit on his car in the middle of the night!＜/p＞＜p＞＜strong＞So, what will Wong do when a lonely baby elephant walks across the road in front of him?＜/strong＞＜/p＞＜p＞After the story, discover amazing facts about pygmy elephants and conduct your own experiments.＜/p＞＜p＞＜strong＞This is the third book in the Wildlife Wong Series. Why not download them all?＜/strong＞＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	600円
Hands-On Q-Learning with Python Practical Q-learning with OpenAI Gym, Keras, and TensorFlow【電子書籍】[ Nazia Habib ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Leverage the power of reward-based training for your deep learning models with Python＜/strong＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞Understand Q-learning algorithms to train neural networks using Markov Decision Process (MDP)＜/li＞＜li＞Study practical deep reinforcement learning using Q-Networks＜/li＞＜li＞Explore state-based unsupervised learning for machine learning models＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞＜p＞Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers.＜/p＞＜p＞This book starts off by introducing you to reinforcement learning and Q-learning, in addition to helping you get familiar with OpenAI Gym as well as libraries such as Keras and TensorFlow. A few chapters into the book, you will gain insights into modelfree Q-learning and use deep Q-networks and double deep Q-networks to solve complex problems. This book will guide you in exploring use cases such as self-driving vehicles and OpenAI Gym's CartPole problem. You will also learn how to tune and optimize Q-networks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving real-world problems. You will also explore how to use Q-learning and related algorithms in real-world applications such as scientific research. Toward the end, you'll gain a sense of what's in store for reinforcement learning.＜/p＞＜p＞By the end of this book, you will be equipped with the skills you need to solve reinforcement learning problems using Q-learning algorithms with OpenAI Gym, Keras, and TensorFlow.＜/p＞＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Explore the fundamentals of reinforcement learning and the state-action-reward process＜/li＞＜li＞Understand Markov decision processes＜/li＞＜li＞Get well versed with libraries such as Keras, and TensorFlow＜/li＞＜li＞Create and deploy model-free learning and deep Q-learning agents with TensorFlow, Keras, and OpenAI Gym＜/li＞＜li＞Choose and optimize a Q-Network's learning parameters and fine-tune its performance＜/li＞＜li＞Discover real-world applications and use cases of Q-learning＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞If you are a machine learning developer, engineer, or professional who wants to delve into the deep learning approach for a complex environment, then this is the book for you. Proficiency in Python programming and basic understanding of decision-making in reinforcement learning is assumed.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	3,597円
Parallel and High Performance Programming with Python【電子書籍】[ Fabio Nelli ] 楽天Kobo電子書籍ストア	＜p＞This book will teach you everything about the powerful techniques and applications of parallel computing, from the basics of parallel programming to the cutting-edge innovations shaping the future of computing. The book starts with an introduction to parallel programming and the different types of parallelism, including parallel programming with threads and processes. The book then delves into asynchronous programming, distributed Python, and GPU programming with Python, providing you with the tools you need to optimize your programs for distributed and high-performance computing. The book also covers a wide range of applications for parallel computing, including data science, artificial intelligence, and other complex scientific simulations. You will learn about the challenges and opportunities presented by parallel computing for these applications and how to overcome them. By the end of the book, you will have insights into the future of parallel computing, the latest research and developments in the field, and explore the exciting possibilities that lie ahead.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	2,291円
Ultimate Neural Network Programming with Python: Create Powerful Modern AI Systems by Harnessing Neural Networks with Python, Keras, and TensorFlow【電子書籍】[ Vishal Rajput ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Master Neural Networks for Building Modern AI Systems.＜/strong＞＜/p＞＜p＞＜strong＞Key Features＜/strong＞＜/p＞＜p＞● Comprehensive Coverage of Foundational AI Concepts and Theories.＜/p＞＜p＞● In-Depth Exploration of Maths Behind Neural Network Mathematics.＜/p＞＜p＞● Effective Strategies for Structuring Deep Learning Code.＜/p＞＜p＞● Real-world applications of AI Principles and Techniques.＜/p＞＜p＞＜strong＞Book Description＜/strong＞＜/p＞＜p＞This book is a practical guide to the world of Artificial Intelligence (AI), unraveling the math and principles behind applications like Google Maps and Amazon. The book starts with an introduction to Python and AI, demystifies complex AI math, teaches you to implement AI concepts, and explores high-level AI libraries.＜/p＞＜p＞Throughout the chapters, readers are engaged with the book through practice exercises and supplementary learning. The book then gradually moves to Neural Networks with Python before diving into constructing ANN models and real-world AI applications. It accommodates various learning styles, letting readers focus on hands-on implementation or mathematical understanding.＜/p＞＜p＞This book isn't just about using AI tools; it's a compass in the world of AI resources, empowering readers to modify and create tools for complex AI systems. It ensures a journey of exploration, experimentation, and proficiency in AI, equipping readers with the skills needed to excel in the AI industry.＜/p＞＜p＞＜strong＞What you will learn＜/strong＞＜/p＞＜p＞● Leverage TensorFlow and Keras while building the foundation for creating AI pipelines.＜/p＞＜p＞● Explore advanced AI concepts, including dimensionality reduction, unsupervised learning, and optimization techniques.＜/p＞＜p＞● Master the intricacies of neural network construction from the ground up.＜/p＞＜p＞● Dive deeper into neural network development, covering derivatives, backpropagation, and optimization strategies.＜/p＞＜p＞● Harness the power of high-level AI libraries to develop production-ready code, allowing you to accelerate the development of AI applications.＜/p＞＜p＞● Stay up-to-date with the latest breakthroughs and advancements in the dynamic field of artificial intelligence.＜/p＞＜p＞＜strong＞Who is this book for?＜/strong＞＜/p＞＜p＞This book serves as an ideal guide for software engineers eager to explore AI, offering a detailed exploration and practical application of AI concepts using Python. AI researchers will find this book enlightening, providing clear insights into the mathematical concepts underlying AI algorithms and aiding in writing production-level code. This book is designed to enhance your skills and knowledge to create sophisticated, AI-powered solutions and advance in the multifaceted field of AI.＜/p＞＜p＞＜strong＞Table of Contents＜/strong＞＜/p＞＜ol＞＜li＞＜p＞Understanding AI History＜/p＞＜/li＞＜li＞＜p＞Setting up Python Workflow for AI Development＜/p＞＜/li＞＜li＞＜p＞Python Libraries for Data Scientists＜/p＞＜/li＞＜li＞＜p＞Foundational Concepts for Effective Neural Network Training＜/p＞＜/li＞＜li＞＜p＞Dimensionality Reduction, Unsupervised Learning and Optimizations＜/p＞＜/li＞＜li＞＜p＞Building Deep Neural Networks from Scratch＜/p＞＜/li＞＜li＞＜p＞Derivatives, Backpropagation, and Optimizers＜/p＞＜/li＞＜li＞＜p＞Understanding Convolution and CNN Architectures＜/p＞＜/li＞＜li＞＜p＞Understanding Basics of TensorFlow and Keras＜/p＞＜/li＞＜li＞＜p＞Building End-to-end Image Segmentation Pipeline＜/p＞＜/li＞＜li＞＜p＞Latest Advancements in AI＜/p＞＜/li＞＜/ol＞＜p＞＜strong＞Index＜/strong＞＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	3,693円
Learn coding with Python and JavaScript A practical introduction for beginners【電子書籍】[ Joachim L. Zuckarelli ] 楽天Kobo電子書籍ストア	＜p＞Whether on the computer, tablet, mobile phone, in the car or in the coffee machine - computer programs determine our everyday life. Software is becoming increasingly important, hardly anything works without the mysterious power of algorithms. But how do programs work? And how do you develop them? This book teaches you the basics of programming. Using everyday examples, you will first learn the basic concepts of programming, which are similar in all programming languages. Based on these basic ideas, you will then learn two popular and very useful programming languages, Python and JavaScript, in a systematic way and with many practical exercises, which you can use for a wide range of different tasks. The book is aimed at novice programmers of all ages (from students to professionals) who have no previous programming experience.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	7,292円
Modern Time Series Forecasting with Python Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas【電子書籍】[ Manu Joseph ] 楽天Kobo電子書籍ストア	＜p＞＜b＞Learn traditional and cutting-edge machine learning (ML) and deep learning techniques and best practices for time series forecasting, including global forecasting models, conformal prediction, and transformer architectures Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free ＜/b＞＜/p＞＜h2＞Key Features＜/h2＞＜ul＞＜li＞Apply ML and global models to improve forecasting accuracy through practical examples＜/li＞＜li＞Enhance your time series toolkit by using deep learning models, including RNNs, transformers, and N-BEATS＜/li＞＜li＞Learn probabilistic forecasting with conformal prediction, Monte Carlo dropout, and quantile regressions＜/li＞＜/ul＞＜h2＞Book Description＜/h2＞Predicting the future, whether it's market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. Whether you’re working with traditional statistical methods or cutting-edge deep learning architectures, this book provides structured learning and best practices for both. Starting with the basics, this data science book introduces fundamental time series concepts, such as ARIMA and exponential smoothing, before gradually progressing to advanced topics, such as machine learning for time series, deep neural networks, and transformers. As part of your fundamentals training, you’ll learn preprocessing, feature engineering, and model evaluation. As you progress, you’ll also explore global forecasting models, ensemble methods, and probabilistic forecasting techniques. This new edition goes deeper into transformer architectures and probabilistic forecasting, including new content on the latest time series models, conformal prediction, and hierarchical forecasting. Whether you seek advanced deep learning insights or specialized architecture implementations, this edition provides practical strategies and new content to elevate your forecasting skills.＜h2＞What you will learn＜/h2＞＜ul＞＜li＞Build machine learning models for regression-based time series forecasting＜/li＞＜li＞Apply powerful feature engineering techniques to enhance prediction accuracy＜/li＞＜li＞Tackle common challenges like non-stationarity and seasonality＜/li＞＜li＞Combine multiple forecasts using ensembling and stacking for superior results＜/li＞＜li＞Explore cutting-edge advancements in probabilistic forecasting and handle intermittent or sparse time series＜/li＞＜li＞Evaluate and validate your forecasts using best practices and statistical metrics＜/li＞＜/ul＞＜h2＞Who this book is for＜/h2＞＜p＞This book is ideal for data scientists, financial analysts, quantitative analysts, machine learning engineers, and researchers who need to model time-dependent data across industries, such as finance, energy, meteorology, risk analysis, and retail. Whether you are a professional looking to apply cutting-edge models to real-world problems or a student aiming to build a strong foundation in time series analysis and forecasting, this book will provide the tools and techniques you need. Familiarity with Python and basic machine learning concepts is recommended.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	4,994円
【中古】OpenCV 3 Computer Vision with Python Cookbook: Leverage the power of OpenCV 3 and Python to build co ブックサプライ	◇◆主にゆうメールによるポスト投函、サイズにより宅配便になります。◆梱包：完全密封のビニール包装または宅配専用パックにてお届けいたします。◆帯、封入物、及び各種コード等の特典は無い場合もございます◆◇【74443】全商品、送料無料！	7,059円
COMPANY BANKRUPTCY ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI【電子書籍】[ Vivian Siahaan ] 楽天Kobo電子書籍ストア	＜p＞In this comprehensive project titled "Company Bankruptcy Analysis and Prediction Using Machine Learning with Python GUI," we embarked on a journey to explore, analyze, and predict the bankruptcy status of companies. Our project began with an exploration of the dataset, which involved importing it using Pandas and refining it by removing leading spaces and replacing spaces with underscores in column names to ensure consistency.＜/p＞＜p＞To grasp the dataset's characteristics, we delved into categorized features' distributions, allowing us to understand the underlying patterns within the data. This step helped us gain insights into the distribution of attributes across different classes, aiding in feature selection and engineering.＜/p＞＜p＞Moving on to the heart of our project, the prediction of company bankruptcy, we employed various machine learning models. Utilizing grid search, we performed hyperparameter tuning to optimize model performance. Our model arsenal included Logistic Regression, K-Nearest Neighbors, Support Vector, Decision Trees, Random Forests, Gradient Boosting, AdaBoost, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron (MLP), which were evaluated using accuracy, precision, recall, and F1-score.＜/p＞＜p＞Transitioning to deep learning, we implemented an Artificial Neural Network (ANN) model. This involved constructing a feed-forward neural network with hidden layers, dropouts, and activation functions. We evaluated the ANN using accuracy, precision, recall, and F1-score, gaining a comprehensive understanding of its classification performance.＜/p＞＜p＞Our journey into deep learning continued with the implementation of Long Short-Term Memory (LSTM) networks, which are well-suited for sequence data. We structured the LSTM model with multiple layers and dropouts, evaluating its performance using metrics like accuracy, precision, recall, and F1-score. This marked a pivotal step in predicting company bankruptcy.＜/p＞＜p＞Furthermore, we explored Feed-Forward Neural Networks (FNN) for prediction. Constructing a multi-layered architecture with varied dropouts and activation functions, we assessed its classification capabilities using metrics similar to previous models.＜/p＞＜p＞Incorporating Recurrent Neural Networks (RNN) added another dimension to our analysis. Building an RNN model with sequential data, we examined its accuracy, precision, recall, and F1-score, highlighting its ability to capture sequential patterns in bankruptcy data.＜/p＞＜p＞To comprehensively evaluate our models, we employed a range of metrics including precision, recall, F1-score, and accuracy. These metrics enabled us to gauge not only the overall model performance but also its capability to correctly predict bankrupt and non-bankrupt cases.＜/p＞＜p＞Our project also extended into creating a Python GUI using PyQt. This graphical interface facilitated user interaction, allowing them to input data for prediction and view the outcomes through an intuitive interface. This GUI enhanced accessibility and usability, making it easier for users to engage with our models.＜/p＞＜p＞In conclusion, our journey through the "Company Bankruptcy Analysis and Prediction Using Machine Learning with Python GUI" project encompassed data exploration, categorized features distribution analysis, model selection, performance evaluation using diverse metrics, and the creation of an interactive GUI. This endeavor combined analytical rigor, machine learning expertise, and user-centric design to provide a comprehensive solution for predicting company bankruptcy＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	1,627円
Getting Started with BeagleBone Linux-Powered Electronic Projects With Python and JavaScript【電子書籍】[ Matt Richardson ] 楽天Kobo電子書籍ストア	＜p＞Many people think of Linux as a computer operating system, running on users' desktops and powering servers. But Linux can also be found inside many consumer electronics devices. Whether they're the brains of a cell phone, cable box, or exercise bike, ＜em＞embedded Linux systems＜/em＞ blur the distinction between computer and device.＜/p＞＜p＞Many makers love microcontroller platforms such as Arduino, but as the complexity increases in their projects, they need more power for applications, such as computer vision. The BeagleBone is an embedded Linux board for makers. It's got built-in networking, many inputs and outputs, and a fast processor to handle demanding tasks. This book introduces you to both the original BeagleBone and the new BeagleBone Black and gets you started with projects that take advantage of the board's processing power and its ability to interface with the outside world.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	1,301円
【中古】 The Book of Dash: Build Dashboards with Python and Plotly/NO STARCH PR/Adam Schroeder / Adam Schroeder, Christian Mayer, Ann Marie Ward / No Starch Press [ペーパーバック]【宅配便出荷】もったいない本舗　おまとめ店	著者：Adam Schroeder, Christian Mayer, Ann Marie Ward出版社：No Starch Pressサイズ：ペーパーバックISBN-10：1718502222ISBN-13：9781718502222■通常24時間以内に出荷可能です。※繁忙期やセール等、ご注文数が多い日につきましては　発送まで72時間かかる場合があります。あらかじめご了承ください。■宅配便(送料398円)にて出荷致します。合計3980円以上は送料無料。■ただいま、オリジナルカレンダーをプレゼントしております。■送料無料の「もったいない本舗本店」もご利用ください。メール便送料無料です。■お急ぎの方は「もったいない本舗　お急ぎ便店」をご利用ください。最短翌日配送、手数料298円から■中古品ではございますが、良好なコンディションです。決済はクレジットカード等、各種決済方法がご利用可能です。■万が一品質に不備が有った場合は、返金対応。■クリーニング済み。■商品画像に「帯」が付いているものがありますが、中古品のため、実際の商品には付いていない場合がございます。■商品状態の表記につきまして・非常に良い：　　使用されてはいますが、　　非常にきれいな状態です。　　書き込みや線引きはありません。・良い：　　比較的綺麗な状態の商品です。　　ページやカバーに欠品はありません。　　文章を読むのに支障はありません。・可：　　文章が問題なく読める状態の商品です。　　マーカーやペンで書込があることがあります。　　商品の痛みがある場合があります。	8,564円
Mastering Computer Vision with PyTorch 2.0 Discover, Design, and Build Cutting-Edge High Performance Computer Vision Solutions with PyTorch 2.0 and Deep Learning Techniques【電子書籍】[ M. Arshad ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Unleashing the Power of Computer Vision with PyTorch 2.0.＜/strong＞＜/p＞＜p＞＜strong＞Key Features＜/strong＞＜/p＞＜p＞● Covers core to advanced Computer Vision topics with PyTorch 2.0's latest features and best practices.＜/p＞＜p＞● Progressive learning path to ensure suitability for beginners and experts alike.＜/p＞＜p＞● Tackles practical tasks like optimization, transfer learning, and edge deployment.＜/p＞＜p＞＜strong＞Book Description＜/strong＞＜/p＞＜p＞In an era where Computer Vision has rapidly transformed industries like healthcare and autonomous systems, PyTorch 2.0 has become the leading framework for high-performance AI solutions. ＜strong＞[Mastering Computer Vision with PyTorch 2.0]＜/strong＞ bridges the gap between theory and application, guiding readers through PyTorch essentials while equipping them to solve real-world challenges.＜/p＞＜p＞Starting with PyTorch’s evolution and unique features, the book introduces foundational concepts like tensors, computational graphs, and neural networks. It progresses to advanced topics such as Convolutional Neural Networks (CNNs), transfer learning, and data augmentation. Hands-on chapters focus on building models, optimizing performance, and visualizing architectures. Specialized areas include efficient training with PyTorch Lightning, deploying models on edge devices, and making models production-ready.＜/p＞＜p＞Explore cutting-edge applications, from object detection models like YOLO and Faster R-CNN to image classification architectures like ResNet and Inception. By the end, readers will be confident in implementing scalable AI solutions, staying ahead in this rapidly evolving field. Whether you're a student, AI enthusiast, or professional, this book empowers you to harness the power of PyTorch 2.0 for Computer Vision.＜/p＞＜p＞＜strong＞What you will learn＜/strong＞＜/p＞＜p＞● Build and train neural networks using PyTorch 2.0.＜/p＞＜p＞● Implement advanced image classification and object detection models.＜/p＞＜p＞● Optimize models through augmentation, transfer learning, and fine-tuning.＜/p＞＜p＞● Deploy scalable AI solutions in production and on edge devices.＜/p＞＜p＞● Master PyTorch Lightning for efficient training workflows.＜/p＞＜p＞● Apply real-world techniques for preprocessing, quantization, and deployment.＜/p＞＜p＞＜strong＞Table of Contents＜/strong＞＜/p＞＜ol＞＜li＞＜p＞Diving into PyTorch 2.0＜/p＞＜/li＞＜li＞＜p＞PyTorch Basics＜/p＞＜/li＞＜li＞＜p＞Transitioning from PyTorch 1.x to PyTorch 2.0＜/p＞＜/li＞＜li＞＜p＞Venturing into Artificial Neural Networks＜/p＞＜/li＞＜li＞＜p＞Diving Deep into Convolutional Neural Networks (CNNs)＜/p＞＜/li＞＜li＞＜p＞Data Augmentation and Preprocessing for Vision Tasks＜/p＞＜/li＞＜li＞＜p＞Exploring Transfer Learning with PyTorch＜/p＞＜/li＞＜li＞＜p＞Advanced Image Classification Models＜/p＞＜/li＞＜li＞＜p＞Object Detection Models＜/p＞＜/li＞＜li＞＜p＞Tips and Tricks to Improve Model Performance＜/p＞＜/li＞＜li＞＜p＞Efficient Training with PyTorch Lightning＜/p＞＜/li＞＜li＞＜p＞Model Deployment and Production-Ready Considerations.＜/p＞＜/li＞＜/ol＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	3,693円
Beginning Game Development with Python and Pygame: From Novice to Professional (Beginning From Novice to Professional) [ペーパーバック参考書専門店ブックスドリーム	【30日間返品保証】商品説明に誤りがある場合は、無条件で弊社送料負担で商品到着後30日間返品を承ります。【最短翌日到着】正午12時まで（日曜日は午前9時まで）の注文は当日発送（土日祝も発送）。関東・関西・中部・中国・四国・九州地方は翌日お届け、東北地方・新潟県・北海道・沖縄県は翌々日にお届けします。【インボイス制度対応済み】当社ではインボイス制度に対応した適格請求書発行事業者番号（通称：T番号・登録番号）を印字した納品書（明細書）を商品に同梱してお送りしております。こちらをご利用いただくことで、税務申告時や確定申告時に消費税額控除を受けることが可能になります。また、適格請求書発行事業者番号の入った領収書・請求書をご注文履歴からダウンロードしていただくこともできます（宛名はご希望のものを入力していただけます）。ご満足のいく取引となるよう精一杯対応させていただきます。※下記に商品説明およびコンディション詳細、出荷予定・配送方法・お届けまでの期間について記載しています。ご確認の上ご購入ください。■商品名■Beginning Game Development with Python and Pygame: From Novice to Professional (Beginning From Novice to Professional)■出版社■Apress■著者■McGugan Will■発行年■2008/10/10■ISBN10■1590598725■ISBN13■9781590598726■コンディションランク■可コンディションランク説明ほぼ新品：未使用に近い状態の商品非常に良い：傷や汚れが少なくきれいな状態の商品良い：多少の傷や汚れがあるが、概ね良好な状態の商品(中古品として並の状態の商品)可：傷や汚れが目立つものの、使用には問題ない状態の商品■コンディション詳細■書き込みありません。弊社の良水準の商品より使用感や傷み、汚れがあるため可のコンディションとしております。可の商品の中ではコンディションが比較的良く、使用にあたって問題のない商品です。水濡れ防止梱包の上、迅速丁寧に発送させていただきます。【発送予定日について】正午12時まで（日曜日は午前9時まで）のご注文は当日に発送いたします。正午12時（日曜日は午前9時）以降のご注文は翌日に発送いたします（土曜日・日曜日・祝日も発送しています）。※土曜日・祝日も正午12時までのご注文は当日に発送いたします。※年末年始（12/31〜1/3）は除きます。(例)・月曜0時〜12時までのご注文：月曜日に発送・月曜12時〜24時までのご注文：火曜日に発送・土曜0時〜12時までのご注文：土曜日に発送・土曜12時〜24時のご注文：日曜日に発送・日曜0時〜9時までのご注文：日曜日に発送・日曜9時〜24時のご注文：月曜日に発送【送付方法について】ネコポス、宅配便またはレターパックでの発送となります。東北地方・新潟県・北海道・沖縄県・離島以外は、発送翌日に到着します。東北地方・新潟県・北海道・沖縄県・離島は、発送後2日での到着となります。商品説明と著しく異なる点があった場合や異なる商品が届いた場合は、到着後30日間は無条件で着払いでご返品後に返金させていただきます。メールまたはご注文履歴からご連絡ください。	4,455円
SYMMETRIC KEY CRYPTOGRAPHY WITH PYTHON AND TKINTER【電子書籍】[ Vivian Siahaan ] 楽天Kobo電子書籍ストア	＜p＞In the evolving landscape of data security, encryption algorithms play a crucial role in safeguarding sensitive information. This book delves into several prominent encryption algorithms, including AES, DES, 3DES, Blowfish, and CAST. Each algorithm offers unique strengths and is suitable for different use cases. Advanced Encryption Standard (AES) stands out for its robust security and efficiency, making it a preferred choice for many modern applications. Data Encryption Standard (DES), while historically significant, has largely been replaced by Triple DES (3DES), which enhances DES's security by applying it multiple times. Blowfish, a versatile and fast cipher, and CAST, known for its flexibility in key lengths, are also explored for their practical applications in various security scenarios.＜/p＞＜p＞To provide a hands-on approach, this book includes detailed Python examples and Tkinter-based graphical user interfaces for each encryption algorithm. These practical examples illustrate how to implement these ciphers in real-world applications, from basic encryption and decryption processes to more complex use cases involving secure data handling and user interaction. Through these examples, readers will gain a comprehensive understanding of both the theoretical and practical aspects of encryption, empowering them to implement secure solutions tailored to their specific needs.＜/p＞＜p＞In chapter two, we discussed the development of a Tkinter-based GUI application for AES encryption and decryption of synthetic data. The application consists of multiple tabs: one for displaying original data, one for showing encrypted data, another for decrypted data, and a fourth for entering and managing passwords. Key functionalities include generating a synthetic dataset with various attributes like UserID, Name, and Email, encrypting and decrypting this data using AES encryption in GCM mode, and displaying the results in a ttk.Treeview. The application also features password management, allowing users to set a password, generate an encryption key from it, and update the displays accordingly.＜/p＞＜p＞The code includes several methods for data encryption and decryption, key derivation, and DataFrame management. The generate_data_intelligence_dataset method creates synthetic data, while encrypt_data and decrypt_data methods handle AES encryption and decryption. The update_displays method updates the DataFrame with encrypted and decrypted data and saves these to Excel files. The display_dataframe method dynamically displays DataFrames in the Tkinter GUI, with alternating row colors for better readability. This session detailed how each part of the code contributes to creating a rich and interactive application for handling encrypted data.＜/p＞＜p＞In chapter three, we explored a detailed Tkinter application designed for managing and visualizing Bitcoin transaction data. The application generates synthetic data including wallet addresses, transaction types, dates, Bitcoin amounts, and their USD equivalents. Users enter an 8-byte DES key to encrypt Bitcoin amounts using DES encryption. The encrypted data is displayed in a tab within the Tkinter GUI, and users can view a histogram showing the distribution of Bitcoin amounts, both before and after encryption.＜/p＞＜p＞The application leverages various Python libraries: tkinter for the GUI, pandas for data manipulation, Crypto.Cipher.DES for encryption, and matplotlib for plotting. Key functions include generating synthetic Bitcoin data, encrypting data with DES, and visualizing data distributions. The GUI is structured with tabs for entering the password, viewing original and encrypted data, and displaying distribution graphs. The session detailed how each function works, from generating Bitcoin addresses and transaction data to encrypting and decrypting data and plotting results.＜/p＞＜p＞In chapter four, we discussed a Python code that integrates a Tkinter GUI with Triple DES (3DES) encryption and SQLite database management. The code encompasses the setup of an SQLite database, encryption and decryption of transaction data using 3DES, and functions for saving and retrieving encrypted transaction records. Key functions include generating a 3DES key, encrypting and decrypting data with appropriate padding, and handling database operations with retry logic to manage potential locking issues. We explored the database setup with table creation, and transaction management, and incorporated robust error handling to ensure reliable operation.＜/p＞＜p＞The GUI implementation leverages Tkinter for user interaction and display, including functions to generate 3DES keys, handle encryption/decryption operations, and manage transaction records in an SQLite database. The session covered detailed explanations of how each part of the code operates, from database setup and key generation to data encryption/decryption and transaction storage. This comprehensive approach ensures secure handling of sensitive data and integrates encryption functionalities within a user-friendly interface, demonstrating practical applications of cryptographic techniques in a real-world scenario.＜/p＞＜p＞In chapter five, we detailed a Python script using Tkinter for a secure cloud storage application leveraging Blowfish encryption. The script includes several core functionalities: user authentication, file upload and download, and cryptographic operations. Key aspects include helper functions for generating and verifying HMACs, as well as Blowfish encryption and decryption. The GUI is organized into tabs for user login/register, file upload, file download, and logs. Functions are defined to handle file selection, encryption, upload, HMAC generation, and downloading, ensuring that data integrity is maintained through HMAC verification and secure file handling.＜/p＞＜p＞The Tkinter-based GUI is designed to provide a user-friendly interface for interacting with the secure cloud storage system. The SecureCloudStorageApp class initializes and configures the GUI with tabs for different functionalities. It manages user login, registration, file selection, and storage operations. The application supports encryption and decryption of files, storing encrypted data and HMACs, and provides feedback through a log and message boxes. This setup ensures secure data handling and user management, integrating cryptographic functions seamlessly into a functional application interface.＜/p＞＜p＞In chapter six, we discussed and refined a Python script for managing file encryption and decryption using the CAST cipher, implemented with a Tkinter graphical user interface (GUI). The script includes functions for generating encryption keys, encrypting and decrypting files, and handling file uploads and downloads. It also features user authentication and registration mechanisms, utilizing bcrypt for password hashing and checking. The GUI allows users to perform these actions through a series of buttons and input fields, with status updates and error messages displayed in a scrollable text area.＜/p＞＜p＞We further improved the script by adding error handling for file operations and ensuring proper file path management. Enhancements included better user feedback through message boxes for errors related to file reading, JSON decoding, and user actions. These improvements aimed to make the application more robust and user-friendly, ensuring reliable file management and secure encryption practices. The final version also addressed exceptions and edge cases to enhance the overall reliability and functionality of the Tkinter-based application.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	1,627円
Algorithms and Data Structures with Python A comprehensive guide to data structures & algorithms via an interactive learning experience【電子書籍】[ Cuantum Technologies LLC ] 楽天Kobo電子書籍ストア	＜p＞＜b＞Master Python and elevate your algorithmic skills with this comprehensive course. From introductory concepts to advanced computational problems, learn how to efficiently solve complex challenges and optimize your code.＜/b＞＜/p＞＜h2＞Key Features＜/h2＞＜ul＞＜li＞Comprehensive introduction to Python programming and algorithms＜/li＞＜li＞Detailed exploration of data structures and sorting/searching techniques＜/li＞＜li＞Advanced topics including graph algorithms and computational problem-solving＜/li＞＜/ul＞＜h2＞Book Description＜/h2＞Begin your journey with an introduction to Python and algorithms, laying the groundwork for more complex topics. You will start with the basics of Python programming, ensuring a solid foundation before diving into more advanced and sophisticated concepts. As you progress, you'll explore elementary data containers, gaining an understanding of their role in algorithm development. Midway through the course, you’ll delve into the art of sorting and searching, mastering techniques that are crucial for efficient data handling. You will then venture into hierarchical data structures, such as trees and graphs, which are essential for understanding complex data relationships. By mastering algorithmic techniques, you’ll learn how to implement solutions for a variety of computational challenges. The latter part of the course focuses on advanced topics, including network algorithms, string and pattern deciphering, and advanced computational problems. You'll apply your knowledge through practical case studies and optimizations, bridging the gap between theoretical concepts and real-world applications. This comprehensive approach ensures you are well-prepared to handle any programming challenge with confidence.＜h2＞What you will learn＜/h2＞＜ul＞＜li＞Master sorting and searching algorithms＜/li＞＜li＞Implement hierarchical data structures like trees and graphs＜/li＞＜li＞Apply advanced algorithmic techniques to solve complex problems＜/li＞＜li＞Optimize code for efficiency and performance＜/li＞＜li＞Understand and implement advanced graph algorithms＜/li＞＜li＞Translate theoretical concepts into practical, real-world solutions＜/li＞＜/ul＞＜h2＞Who this book is for＜/h2＞＜p＞This course is designed for a diverse group of learners, including technical professionals, software developers, computer science students, and data enthusiasts. It caters to individuals who have a basic understanding of programming and are eager to deepen their knowledge of Python and algorithms. Whether you're a recent graduate, or an experienced developer looking to expand your skill set, this course is tailored to meet the needs of all types of audiences. Ideal for those aiming to strengthen their algorithmic thinking and improve their coding efficiency.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	3,702円
洋書 Apress Paperback, Practical Docker with Python: Build, Release and Distribute your Python App with Docker Glomarket	* We ship internationally, so do not use a package forwarding service. We cannot ship to a package forwarding company address because of the Japanese customs regulation. If it is shipped and customs office does not let the package go, we do not make a refund. 【注意事項】 * 特に注意してください。 * ・個人ではない法人・団体名義での購入はできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。・お名前にカタカナが入っている場合法人である可能性が高いため当店システムから自動保留します。カタカナで記載が必要な場合はカタカナ変わりローマ字で記載してください。・お名前またはご住所が法人・団体名義（XX株式会社等）、商店名などを含めている場合、または電話番号が個人のものではない場合、税関から法人名義でみなされますのでご注意ください。・転送サービス会社への発送もできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 * ・注文後品切れや価格変動でキャンセルされる場合がございますので予めご了承願います。・当店でご購入された商品は、原則として、「個人輸入」としての取り扱いになり、すべてニュージャージからお客様のもとへ直送されます。・ご注文後、30営業日以内(通常2~3週間)に配送手続きをいたします。配送作業完了後、2週間程度でのお届けとなります。・まれに商品入荷状況や国際情勢、運送、通関事情により、お届けが2ヶ月までかかる場合がありますのでお急ぎの場合は注文をお控えください。・個人輸入される商品は、すべてご注文者自身の「個人使用・個人消費」が前提となりますので、ご注文された商品を第三者へ譲渡・転売することは法律で禁止されております。・関税・消費税が課税される場合があります。詳細はこちらをご確認下さい。PC販売説明文	11,691円
Data Analysis with Python: Introducing NumPy, Pandas, Matplotlib, and Essential Elements of Python Programming (English Edition)【電子書籍】[ Rituraj Dixit ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞An Absolute Beginner's Guide to Learning Data Analysis Using Python, a Demanding Skill for Today＜/strong＞＜/p＞＜p＞＜strong＞KEY FEATURES＜/strong＞＜/p＞＜p＞● Hands-on learning experience of Python's fundamentals.＜/p＞＜p＞● Covers various examples of how to code end-to-end data analysis with easy illustrations.＜/p＞＜p＞● An excellent starting point to begin your data analysis journey with Python programming.＜/p＞＜p＞＜strong＞DESCRIPTION＜/strong＞＜/p＞＜p＞In an effort to provide content for beginners, the book 'Data Analysis with Python' provides a concrete first step in learning data analysis. Written by a data professional with decades of experience, this book provides a solid foundation in data analysis and numerous data science processes. In doing so, readers become familiar with common Python libraries and straightforward scripting techniques.＜/p＞＜p＞Python and many of its well-known data analysis libraries, such as Pandas, NumPy, and Matplotlib, are utilized throughout this book to carry out various operations typical of data analysis projects.＜/p＞＜p＞Following an introduction to Python programming fundamentals, the book combines well-known numerical calculation and statistical libraries to demonstrate the fundamentals of programming, accompanied by many practical examples. This book provides a solid groundwork for data analysis by teaching Python programming as well as Python's built-in data analysis capabilities.＜/p＞＜p＞＜strong＞WHAT YOU WILL LEARN＜/strong＞＜/p＞＜p＞● Learn the fundamentals of core Python programming for data analysis.＜/p＞＜p＞● Master Python's most demanding data analysis and visualization libraries, including Pandas, NumPy, and Matplotlib.＜/p＞＜p＞● Refresh your step-by-step data analysis process with live examples.＜/p＞＜p＞● Extend your expertise to include real-time data analysis and the creation of simple Python scripts.＜/p＞＜p＞● Work with external files such as Excel, CSV, and others to clean them up for further analysis.＜/p＞＜p＞＜strong＞WHO THIS BOOK IS FOR＜/strong＞＜/p＞＜p＞This book is intended to help and teach college students and data professionals about Python's data analysis capabilities while also allowing them to work with Python tools.＜/p＞＜p＞Before diving into this book, working knowledge of Python is a definite plus.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	1,350円
【中古】【輸入品・未使用】Architecture Patterns With Python: Enabling Test-Driven Development%カンマ% Domain-Driven Design%カンマ% and Event-Driven Microservices ムジカ＆フェリーチェ楽天市場店	【中古】【輸入品・未使用】Architecture Patterns With Python: Enabling Test-Driven Development%カンマ% Domain-Driven Design%カンマ% and Event-Driven Microservices【メーカー名】Oreilly & Associates Inc【メーカー型番】【ブランド名】O'Reilly Media【商品説明】Architecture Patterns With Python: Enabling Test-Driven Development%カンマ% Domain-Driven Design%カンマ% and Event-Driven Microservices当店では初期不良に限り、商品到着から7日間は返品を受付けております。こちらは海外販売用に買取り致しました未使用品です。買取り致しました為、中古扱いとしております。他モールとの併売品の為、完売の際はご連絡致しますのでご了承下さい。速やかにご返金させて頂きます。ご注文からお届けまで1、ご注文⇒ご注文は24時間受け付けております。2、注文確認⇒ご注文後、当店から注文確認メールを送信します。3、配送⇒当店海外倉庫から取り寄せの場合は10〜30日程度でのお届けとなります。国内到着後、発送の際に通知にてご連絡致します。国内倉庫からの場合は3〜7日でのお届けとなります。　※離島、北海道、九州、沖縄は遅れる場合がございます。予めご了承下さい。お電話でのお問合せは少人数で運営の為受け付けておりませんので、メールにてお問合せお願い致します。営業時間　月〜金　10:00〜17:00お客様都合によるご注文後のキャンセル・返品はお受けしておりませんのでご了承下さい。	17,988円
Deep Reinforcement Learning with Python Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow, 2nd Edition【電子書籍】[ Sudharsan Ravichandiran ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞An example-rich guide for beginners to start their reinforcement and deep reinforcement learning journey with state-of-the-art distinct algorithms＜/strong＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞Covers a vast spectrum of basic-to-advanced RL algorithms with mathematical explanations of each algorithm＜/li＞＜li＞Learn how to implement algorithms with code by following examples with line-by-line explanations＜/li＞＜li＞Explore the latest RL methodologies such as DDPG, PPO, and the use of expert demonstrations＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞＜p＞With significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit.＜/p＞＜p＞In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and actor-critic RL methods. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples.＜/p＞＜p＞The book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage stable baselines, an improvement of OpenAI's baseline library, to effortlessly implement popular RL algorithms. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research.＜/p＞＜p＞By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects.＜/p＞＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Understand core RL concepts including the methodologies, math, and code＜/li＞＜li＞Train an agent to solve Blackjack, FrozenLake, and many other problems using OpenAI Gym＜/li＞＜li＞Train an agent to play Ms Pac-Man using a Deep Q Network＜/li＞＜li＞Learn policy-based, value-based, and actor-critic methods＜/li＞＜li＞Master the math behind DDPG, TD3, TRPO, PPO, and many others＜/li＞＜li＞Explore new avenues such as the distributional RL, meta RL, and inverse RL＜/li＞＜li＞Use Stable Baselines to train an agent to walk and play Atari games＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞If you're a machine learning developer with little or no experience with neural networks interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you.＜/p＞＜p＞Basic familiarity with linear algebra, calculus, and the Python programming language is required. Some experience with TensorFlow would be a plus.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	5,258円
Interpretability and Explainability in AI Using Python Decrypt AI Decision-Making Using Interpretability and Explainability with Python to Build Reliable Machine Learning Systems (English Edition)【電子書籍】[ Aruna Chakkirala ] 楽天Kobo電子書籍ストア	＜p＞＜em＞＜strong＞Demystify AI Decisions and Master Interpretability and Explainability Today＜/strong＞＜/em＞＜/p＞＜p＞＜strong＞Key Features＜/strong＞＜br /＞ ● Master Interpretability and Explainability in ML, Deep Learning, Transformers, and LLMs＜br /＞ ● Implement XAI techniques using Python for model transparency＜br /＞ ● Learn global and local interpretability with real-world examples＜/p＞＜p＞＜strong＞Book Description＜/strong＞＜br /＞ Interpretability in AI/ML refers to the ability to understand and explain how a model arrives at its predictions. It ensures that humans can follow the model's reasoning, making it easier to debug, validate, and trust.＜/p＞＜p＞＜strong＞Interpretability and Explainability in AI Using Python＜/strong＞ takes you on a structured journey through interpretability and explainability techniques for both white-box and black-box models.＜/p＞＜p＞You’ll start with foundational concepts in interpretable machine learning, exploring different model types and their transparency levels. As you progress, you’ll dive into post-hoc methods, feature effect analysis, anchors, and counterfactualsーpowerful tools to decode complex models. The book also covers explainability in deep learning, including Neural Networks, Transformers, and Large Language Models (LLMs), equipping you with strategies to uncover decision-making patterns in AI systems.＜/p＞＜p＞Through hands-on Python examples, you’ll learn how to apply these techniques in real-world scenarios. By the end, you’ll be well-versed in choosing the right interpretability methods, implementing them efficiently, and ensuring AI models align with ethical and regulatory standardsーgiving you a competitive edge in the evolving AI landscape.＜/p＞＜p＞＜strong＞What you will learn＜/strong＞＜br /＞ ● Dissect key factors influencing model interpretability and its different types.＜br /＞ ● Apply post-hoc and inherent techniques to enhance AI transparency.＜br /＞ ● Build explainable AI (XAI) solutions using Python frameworks for different models.＜br /＞ ● Implement explainability methods for deep learning at global and local levels.＜br /＞ ● Explore cutting-edge research on transparency in transformers and LLMs.＜br /＞ ● Learn the role of XAI in Responsible AI, including key tools and methods.＜/p＞＜p＞＜strong＞Table of Contents＜/strong＞＜/p＞＜ol＞＜li＞Interpreting Interpretable Machine Learning＜/li＞＜li＞Model Types and Interpretability Techniques＜/li＞＜li＞Interpretability Taxonomy and Techniques＜/li＞＜li＞Feature Effects Analysis with Plots＜/li＞＜li＞Post-Hoc Methods＜/li＞＜li＞Anchors and Counterfactuals＜/li＞＜li＞Interpretability in Neural Networks＜/li＞＜li＞Explainable Neural Networks＜/li＞＜li＞Explainability in Transformers and Large Language Models＜/li＞＜li＞Explainability and Responsible AI＜br /＞＜strong＞Index＜/strong＞＜/li＞＜/ol＞＜p＞＜strong＞About the Authors＜/strong＞＜br /＞＜strong＞Aruna Chakkirala＜/strong＞ a seasoned technical leader and currently serves as an AI Solutions Architect at Microsoft. She was instrumental in the early adoption of Generative AI and constantly strives to keep pace with the evolving domain. As a Data Scientist, she has built Supervised and Unsupervised models to address cybersecurity problems. She holds a patent for her pioneering work in community detection for DNS querying. Her technical expertise spans multiple domains, including Networks, Security, Cloud, Big Data, and AI. She believes that the success of real-world AI applications increasingly depends on well- defined architectures across all encompassing domains. Her current interests include Generative AI, applications of LLMs and SLMs, Causality, Mechanistic Interpretability, and Explainability tools.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	2,952円
洋書 Paperback, An Introduction to Statistics with Python: With Applications in the Life Sciences (Statistics and Computing) Glomarket	* We ship internationally, so do not use a package forwarding service. We cannot ship to a package forwarding company address because of the Japanese customs regulation. If it is shipped and customs office does not let the package go, we do not make a refund. 【注意事項】 * 特に注意してください。 * ・個人ではない法人・団体名義での購入はできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。・お名前にカタカナが入っている場合法人である可能性が高いため当店システムから自動保留します。カタカナで記載が必要な場合はカタカナ変わりローマ字で記載してください。・お名前またはご住所が法人・団体名義（XX株式会社等）、商店名などを含めている場合、または電話番号が個人のものではない場合、税関から法人名義でみなされますのでご注意ください。・転送サービス会社への発送もできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 * ・注文後品切れや価格変動でキャンセルされる場合がございますので予めご了承願います。・当店でご購入された商品は、原則として、「個人輸入」としての取り扱いになり、すべてニュージャージからお客様のもとへ直送されます。・ご注文後、30営業日以内(通常2~3週間)に配送手続きをいたします。配送作業完了後、2週間程度でのお届けとなります。・まれに商品入荷状況や国際情勢、運送、通関事情により、お届けが2ヶ月までかかる場合がありますのでお急ぎの場合は注文をお控えください。・個人輸入される商品は、すべてご注文者自身の「個人使用・個人消費」が前提となりますので、ご注文された商品を第三者へ譲渡・転売することは法律で禁止されております。・関税・消費税が課税される場合があります。詳細はこちらをご確認下さい。PC販売説明文	15,894円
Credit Card Churning Customer Analysis and Prediction Using Machine Learning and Deep Learning with Python【電子書籍】[ Vivian Siahaan ] 楽天Kobo電子書籍ストア	＜p＞The project "Credit Card Churning Customer Analysis and Prediction Using Machine Learning and Deep Learning with Python" involved a comprehensive analysis and prediction task focused on understanding customer attrition in a credit card churning scenario. The objective was to explore a dataset, visualize the distribution of features, and predict the attrition flag using both machine learning and artificial neural network (ANN) techniques.＜/p＞＜p＞The project began by loading the dataset containing information about credit card customers, including various features such as customer demographics, transaction details, and account attributes. The dataset was then explored to gain a better understanding of its structure and contents. This included checking the number of records, identifying the available features, and inspecting the data types. To gain insights into the data, exploratory data analysis (EDA) techniques were employed. This involved examining the distribution of different features, identifying any missing values, and understanding the relationships between variables. Visualizations were created to represent the distribution of features. These visualizations helped identify any patterns, outliers, or potential correlations in the data.＜/p＞＜p＞The target variable for prediction was the attrition flag, which indicated whether a customer had churned or not. The dataset was split into input features (X) and the target variable (y) accordingly. Machine learning algorithms were then applied to predict the attrition flag. Various classifiers such as Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), K-Nearest Neighbors (NN), Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, were utilized. These models were trained using the training dataset and evaluated using appropriate performance metrics.＜/p＞＜p＞Model evaluation involved measuring the accuracy, precision, recall, and F1-score of each classifier. These metrics provided insights into how well the models performed in predicting customer attrition. Additionally, a confusion matrix was created to analyze the true positive, true negative, false positive, and false negative predictions. This matrix allowed for a deeper understanding of the classifier's performance and potential areas for improvement.＜/p＞＜p＞Next, a deep learning approach using an artificial neural network (ANN) was employed for attrition flag prediction. The dataset was preprocessed, including features normalization, one-hot encoding of categorical variables, and splitting into training and testing sets. The ANN model architecture was defined, consisting of an input layer, one or more hidden layers, and an output layer. The number of nodes and activation functions for each layer were determined based on experimentation and best practices. The ANN model was compiled by specifying the loss function, optimizer, and evaluation metrics. Common choices for binary classification problems include binary cross-entropy loss and the Adam optimizer. The model was then trained using the training dataset. The training process involved feeding the input features and target variable through the network, updating the weights and biases using backpropagation, and repeating this process for multiple epochs. During training, the model's performance on both the training and validation sets was monitored. This allowed for the detection of overfitting or underfitting and the adjustment of hyperparameters, such as the learning rate or the number of hidden layers, if necessary.＜/p＞＜p＞The accuracy and loss values were plotted over the epochs to visualize the training and validation performance of the ANN. These plots provided insights into the model's convergence and potential areas for improvement. After training, the model was used to make predictions on the test dataset. A threshold of 0.5 was applied to the predicted probabilities to classify the predictions as either churned or not churned customers. The accuracy score was calculated by comparing the predicted labels with the true labels from the test dataset. Additionally, a classification report was generated, including metrics such as precision, recall, and F1-score for both churned and not churned customers.＜/p＞＜p＞To further evaluate the model's performance, a confusion matrix was created. This matrix visualized the true positive, true negative, false positive, and false negative predictions, allowing for a more detailed analysis of the model's predictive capabilities. Finally, a custom function was utilized to create a plot comparing the predicted values to the true values for the attrition flag. This plot visualized the accuracy of the model and provided a clear understanding of how well the predictions aligned with the actual values.＜/p＞＜p＞Through this comprehensive analysis and prediction process, valuable insights were gained regarding customer attrition in credit card churning scenarios. The machine learning and ANN models provided predictions and performance metrics that can be used for decision-making and developing strategies to mitigate attrition. Overall, this project demonstrated the power of machine learning and deep learning techniques in understanding and predicting customer behavior. By leveraging the available data, it was possible to uncover patterns, make accurate predictions, and guide business decisions aimed at retaining customers and reducing attrition in credit card churning scenarios.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	1,953円