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【中古】ナウ・アンド・フォーエヴァー/CD/BVCP-40045 / エア・サプライ / BMG JAPAN Inc. [CD]【ネコポス発送】もったいない本舗　お急ぎ便店	EANコード：4988017672518■通常24時間以内に出荷可能です。■ネコポスで送料は1～3点で298円、4点で328円。5点以上で600円からとなります。※2,500円以上の購入で送料無料。※多数ご購入頂いた場合は、宅配便での発送になる場合があります。■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。■まとめ買いの方は「もったいない本舗　おまとめ店」がお買い得です。■「非常に良い」コンディションの商品につきましては、新品ケースに交換済みです。■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。■商品状態の表記につきまして・非常に良い：　　非常に良い状態です。再生には問題がありません。・良い：　　使用されてはいますが、再生に問題はありません。・可：　　再生には問題ありませんが、ケース、ジャケット、　　歌詞カードなどに痛みがあります。アーティスト：エア・サプライ枚数：1枚組み限定盤：限定盤曲数：10曲曲名：DISK1 1.ナウ・アンド・フォーエヴァー2.さよならロンリー・ラヴ3.ヤング・ラヴ4.夜明けのふたり5.テイキング・ザ・チャンス6.ラヴ・ビリーヴァー7.ワン・ステップ・クローサー8.ドント・ビー・アフレイド9.残された言葉10.あの娘はどんな娘！？型番：BVCP-40045発売年月日：2009年08月05日	4,178円
【中古】【未使用】POSRUS NibSaver Surface Cover Wacom Intuos Pro Pen and Touch Medium (PTH651) ペンタブレット用 AJIMURA-SHOP	【中古】【未使用】POSRUS NibSaver Surface Cover Wacom Intuos Pro Pen and Touch Medium (PTH651) ペンタブレット用【メーカー名】【メーカー型番】【ブランド名】POSRUS ペンタブレット, スマートフォン関連製品【商品説明】POSRUS NibSaver Surface Cover Wacom Intuos Pro Pen and Touch Medium (PTH651) ペンタブレット用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。お問い合わせ・メールにて不具合詳細をご連絡ください。他モールとの併売品の為、完売の際はキャンセルご連絡させて頂きます。中古品の商品タイトルに「限定」「初回」「保証」「DLコード」などの表記がありましても、特典・付属品・帯・保証等は付いておりません。電子辞書、コンパクトオーディオプレーヤー等のイヤホンは写真にありましても衛生上、基本お付けしておりません。※未使用品は除く品名に【import】【輸入】【北米】【海外】等の国内商品でないと把握できる表記商品について国内のDVDプレイヤー、ゲーム機で稼働しない場合がございます。予めご了承の上、購入ください。掲載と付属品が異なる場合は確認のご連絡をさせて頂きます。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、お届けまで3〜10営業日程度とお考えください。4、入金確認⇒前払い決済をご選択の場合、ご入金確認後、配送手配を致します。5、出荷⇒配送準備が整い次第、出荷致します。配送業者、追跡番号等の詳細をメール送信致します。6、到着⇒出荷後、1〜3日後に商品が到着します。　※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、お問い合わせ・メールにてお願い致します。営業時間　月〜金　11:00〜17:00★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ください。0	21,475円
コロシアムメンズパーカー・スウェットシャツアウター Men's Black LSU Tigers Arch and Logo 3.0 Full-Zip Hoodie Black asty	■トップスサイズ単位(cm) USサイズ｜胸囲(cm) XS(32-34in)｜84 S(34-36in)｜88 M(36-38in)｜94 L(38-40in)｜100 XL(42-44in)｜106 ■ブランド Colosseum (コロシアム)■商品名 Men's Black LSU Tigers Arch and Logo 3.0 Full-Zip Hoodie■こちらの商品は米国・ヨーロッパからお取り寄せ商品となりますので、お届けまで10日〜2週間前後お時間頂いております。 ■各ブランド・商品・デザインによって大きな差異がある場合がございます。 ■あくまで平均的なサイズ表ですので、「参考」としてご利用ください。 ■店内全品【送料無料】です！（※沖縄・離島は別途送料3,300円がかかります）	17,480円
Burgon and Ball 羊毛毛刈り鋏 (6.5インチフルサイズ) メゾンクローバー	Burgon and Ball 羊毛毛刈り鋏 (6.5インチフルサイズ)	9,850円
【中古】POSRUS NibSaver Surface Cover Wacom Intuos Pro Pen and Touch Medium (PTH651) ペンタブレット用 AJIMURA-SHOP	【中古】POSRUS NibSaver Surface Cover Wacom Intuos Pro Pen and Touch Medium (PTH651) ペンタブレット用【メーカー名】【メーカー型番】【ブランド名】POSRUS ペンタブレット, スマートフォン関連製品【商品説明】POSRUS NibSaver Surface Cover Wacom Intuos Pro Pen and Touch Medium (PTH651) ペンタブレット用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。お問い合わせ・メールにて不具合詳細をご連絡ください。他モールとの併売品の為、完売の際はキャンセルご連絡させて頂きます。中古品の商品タイトルに「限定」「初回」「保証」「DLコード」などの表記がありましても、特典・付属品・帯・保証等は付いておりません。電子辞書、コンパクトオーディオプレーヤー等のイヤホンは写真にありましても衛生上、基本お付けしておりません。※未使用品は除く品名に【import】【輸入】【北米】【海外】等の国内商品でないと把握できる表記商品について国内のDVDプレイヤー、ゲーム機で稼働しない場合がございます。予めご了承の上、購入ください。掲載と付属品が異なる場合は確認のご連絡をさせて頂きます。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、お届けまで3〜10営業日程度とお考えください。4、入金確認⇒前払い決済をご選択の場合、ご入金確認後、配送手配を致します。5、出荷⇒配送準備が整い次第、出荷致します。配送業者、追跡番号等の詳細をメール送信致します。6、到着⇒出荷後、1〜3日後に商品が到着します。　※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、お問い合わせ・メールにてお願い致します。営業時間　月〜金　11:00〜17:00★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ください。0	21,028円
Python for Algorithmic Trading Cookbook Recipes for designing, building, and deploying algorithmic trading strategies with Python【電子書籍】[ Jason Strimpel ] 楽天Kobo電子書籍ストア	＜p＞＜b＞Harness the power of Python libraries to transform freely available financial market data into algorithmic trading strategies and deploy them into a live trading environment Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free＜/b＞＜/p＞＜h2＞Key Features＜/h2＞＜ul＞＜li＞Follow practical Python recipes to acquire, visualize, and store market data for market research＜/li＞＜li＞Design, backtest, and evaluate the performance of trading strategies using professional techniques＜/li＞＜li＞Deploy trading strategies built in Python to a live trading environment with API connectivity＜/li＞＜/ul＞＜h2＞Book Description＜/h2＞Discover how Python has made algorithmic trading accessible to non-professionals with unparalleled expertise and practical insights from Jason Strimpel, founder of PyQuant News and a seasoned professional with global experience in trading and risk management. This book guides you through from the basics of quantitative finance and data acquisition to advanced stages of backtesting and live trading. Detailed recipes will help you leverage the cutting-edge OpenBB SDK to gather freely available data for stocks, options, and futures, and build your own research environment using lightning-fast storage techniques like SQLite, HDF5, and ArcticDB. This book shows you how to use SciPy and statsmodels to identify alpha factors and hedge risk, and construct momentum and mean-reversion factors. You’ll optimize strategy parameters with walk-forward optimization using VectorBT and construct a production-ready backtest using Zipline Reloaded. Implementing all that you’ve learned, you’ll set up and deploy your algorithmic trading strategies in a live trading environment using the Interactive Brokers API, allowing you to stream tick-level data, submit orders, and retrieve portfolio details. By the end of this algorithmic trading book, you'll not only have grasped the essential concepts but also the practical skills needed to implement and execute sophisticated trading strategies using Python.＜h2＞What you will learn＜/h2＞＜ul＞＜li＞Acquire and process freely available market data with the OpenBB Platform＜/li＞＜li＞Build a research environment and populate it with financial market data＜/li＞＜li＞Use machine learning to identify alpha factors and engineer them into signals＜/li＞＜li＞Use VectorBT to find strategy parameters using walk-forward optimization＜/li＞＜li＞Build production-ready backtests with Zipline Reloaded and evaluate factor performance＜/li＞＜li＞Set up the code framework to connect and send an order to Interactive Brokers＜/li＞＜/ul＞＜h2＞Who this book is for＜/h2＞＜p＞Python for Algorithmic Trading Cookbook equips traders, investors, and Python developers with code to design, backtest, and deploy algorithmic trading strategies. You should have experience investing in the stock market, knowledge of Python data structures, and a basic understanding of using Python libraries like pandas. This book is also ideal for individuals with Python experience who are already active in the market or are aspiring to be.＜/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。	5,166円
GRADIENT-BASED BLOCK MATCHING MOTION ESTIMATION AND OBJECT TRACKING WITH PYTHON AND TKINTER【電子書籍】[ Vivian Siahaan ] 楽天Kobo電子書籍ストア	＜p＞The first project, gui_motion_analysis_gbbm.py, is designed to streamline motion analysis in videos using the Gradient-Based Block Matching Algorithm (GBBM) alongside a user-friendly Graphical User Interface (GUI). It encompasses various objectives, including intuitive GUI design with Tkinter, enabling video playback control, performing optical flow analysis, and allowing parameter configuration for tailored motion analysis. The GUI also facilitates interactive zooming, frame-wise analysis, and offers visual feedback through motion vector overlays. Robust error handling and multi-instance support enhance stability and usability, while dynamic title updates provide context within the interface. Overall, the project empowers users with a versatile tool for comprehensive motion analysis in videos.＜/p＞＜p＞By integrating the GBBM algorithm with an intuitive GUI, gui_motion_analysis_gbbm.py simplifies motion analysis in videos. Its objectives range from GUI design to parameter configuration, enabling users to control video playback, perform optical flow analysis, and visualize motion patterns effectively. With features like interactive zooming, frame-wise analysis, and visual feedback, users can delve into motion dynamics seamlessly. Robust error handling ensures stability, while multi-instance support allows for concurrent analysis. Dynamic title updates enhance user awareness, culminating in a versatile tool for in-depth motion analysis.＜/p＞＜p＞The second project, gui_motion_analysis_gbbm_pyramid.py, is dedicated to offering an accessible interface for video motion analysis, employing the Gradient-Based Block Matching Algorithm (GBBM) with a Pyramid Approach. Its objectives encompass several crucial aspects.＜/p＞＜p＞Primarily, the project responds to the demand for motion analysis in video processing across diverse domains like computer vision and robotics. By integrating the GBBM algorithm into a GUI, it democratizes motion analysis, catering to users without specialized programming or computer vision skills. Leveraging the GBBM algorithm's effectiveness, particularly with the Pyramid Approach, enhances performance and robustness, enabling accurate motion estimation across various scales. The GUI offers extensive control options and visualization features, empowering users to customize analysis parameters and inspect motion dynamics comprehensively. Overall, this project endeavors to advance video processing and analysis by providing an intuitive interface backed by cutting-edge algorithms, fostering accessibility and efficiency in motion analysis tasks.＜/p＞＜p＞The third project, gui_motion_analysis_gbbm_adaptive.py, introduces a GUI application for video motion estimation, employing the Gradient-Based Block Matching Algorithm (GBBM) with Adaptive Block Size. Users can interact with video files, control playback, navigate frames, and visualize optical flow between consecutive frames, facilitated by features like zooming and panning. Developed with Tkinter in Python, the GUI provides intuitive controls for adjusting motion estimation parameters and playback options upon launch.＜/p＞＜p＞At its core, the application dynamically adjusts block sizes based on local gradient magnitude, enhancing motion estimation accuracy, especially in areas with varying complexity. Utilizing PIL and OpenCV libraries, it handles image processing tasks and video file operations, enabling users to interact with the video display canvas for enhanced analysis. Overall, gui_motion_analysis_gbbm_adaptive.py offers a versatile solution for motion analysis in videos, empowering users with visualization tools and parameter customization for diverse applications like video compression and object tracking.＜/p＞＜p＞The fourth project, gui_motion_analysis_gbbm_lucas_kanade.py, introduces a GUI for motion estimation in videos, incorporating both the Gradient-Based Block Matching Algorithm (GBBM) and Lucas-Kanade Optical Flow. It begins by importing necessary libraries such as tkinter for GUI development, PIL for image processing, imageio for video file handling, cv2 for computer vision operations, and numpy for numerical computation. The VideoGBBM_LK_OpticalFlow class serves as the application container, initializing attributes and defining methods for video loading, playback control, parameter setting, frame display, and optical flow visualization. With features like zooming, panning, and event handling for user interactions, the script offers a comprehensive tool for visualizing and analyzing motion dynamics in videos using two distinct optical flow estimation techniques.＜/p＞＜p＞The fifth project, gui_motion_analysis_gbbm_sift.py, introduces a GUI application for optical flow analysis in videos, employing both the Gradient-Based Block Matching Algorithm (GBBM) and Scale-Invariant Feature Transform (SIFT). It begins by importing essential libraries such as tkinter for GUI development, PIL for image processing, imageio for video handling, and OpenCV for computer vision tasks like optical flow computation. The VideoGBBM_SIFT_OpticalFlow class orchestrates the application, initializing GUI elements and defining methods for video loading, playback control, frame display, and optical flow computation using both GBBM and SIFT algorithms. With features for parameter adjustment, frame navigation, zooming, and event handling for user interactions, the script offers a user-friendly interface for in-depth optical flow analysis, enabling insights into motion patterns and dynamics within videos.＜/p＞＜p＞The sixth project, gui_motion_analysis_gbbm_orb.py script, offers a user-friendly interface for motion estimation in videos, utilizing both the Gradient-Based Block Matching Algorithm (GBBM) and ORB (Oriented FAST and Rotated BRIEF) optical flow techniques. Its primary goal is to enable users to analyze and visualize motion dynamics within video files effortlessly. The GUI application provides functionalities for opening video files, navigating frames, adjusting parameters like zoom scale and step size, and controlling playback with buttons for play, pause, stop, next frame, and previous frame.＜/p＞＜p＞Key to the application's functionality is its ability to compute and visualize optical flow using both GBBM and ORB algorithms. Optical flow, depicting object motion in videos, is represented with vectors overlaid on video frames, aiding users in understanding motion patterns and dynamics. Interactive features such as mouse wheel zooming and dragging enhance user exploration of video frames and optical flow visualizations, allowing dynamic adjustment of viewing perspective to focus on specific regions or analyze motion at different scales. Overall, this project provides a comprehensive tool for video motion analysis, merging user-friendly interface elements with advanced motion estimation techniques to empower users in tasks ranging from surveillance to computer vision research.＜/p＞＜p＞The seventh project showcases object tracking using the Gradient-Based Block Matching Algorithm (GBBM), vital in various computer vision applications like surveillance and robotics. By continuously locating and tracking objects of interest in video streams, it highlights GBBM's practical application for real-time tracking. The GUI interface simplifies interaction with video files, allowing easy opening and visualization of frames. Users control playback, navigate frames, and adjust zoom scale, while the heart of the project lies in GBBM's implementation for tracking objects. GBBM estimates object motion by comparing pixel blocks between consecutive frames, generating motion vectors that describe the object's movement. Users can select regions of interest for tracking, adjust algorithm parameters, and receive visual feedback through dynamically adjusting bounding boxes around tracked objects, making it an educational tool for experimenting with object tracking techniques within an accessible interface.＜/p＞＜p＞The eight project endeavors to create an application for object tracking using the Gradient-Based Block Matching Algorithm (GBBM) with a Pyramid Approach, catering to various computer vision applications like surveillance and autonomous vehicles. Built with Tkinter in Python, the user-friendly interface presents controls for video display, object tracking, and parameter adjustment upon launch. Users can load video files, play, pause, navigate frames, and adjust zoom levels effortlessly.＜/p＞＜p＞Central to the application is the GBBM algorithm with a pyramid approach for robust object tracking. By refining search spaces at multiple resolutions, it efficiently estimates motion vectors, accommodating scale variations and occlusions. The application visualizes tracked objects with bounding boxes on the video canvas and updates object coordinates dynamically, providing users with insights into object movement. Advanced features, including dynamic parameter adjustment, enhance the algorithm's adaptability, enabling users to fine-tune tracking based on video characteristics and requirements. Overall, this project offers a practical implementation of object tracking within an accessible interface, catering to users across expertise levels in computer vision.＜/p＞＜p＞The ninth project, "Object Tracking with Gradient-Based Block Matching Algorithm (GBBM) with Adaptive Block Size", focuses on developing a graphical user interface (GUI) application for object tracking in video files using computer vision techniques. Leveraging the GBBM algorithm, a prominent method for motion estimation, the project aims to enable efficient object tracking across video frames, enhancing user interaction and real-time monitoring capabilities.＜/p＞＜p＞The GUI interface facilitates seamless video file loading, playback control, frame navigation, and real-time object tracking, empowering users to interact with video frames, adjust zoom levels, and monitor tracked object coordinates throughout the video sequence. Central to the project's functionality is the adaptive block size variant of the GBBM algorithm, dynamically adjusting block s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。	976円
The Art of Reinforcement Learning Fundamentals, Mathematics, and Implementations with Python【電子書籍】[ Michael Hu ] 楽天Kobo電子書籍ストア	＜p＞Unlock the full potential of reinforcement learning (RL), a crucial subfield of Artificial Intelligence, with this comprehensive guide. This book provides a deep dive into RL's core concepts, mathematics, and practical algorithms, helping you to develop a thorough understanding of this cutting-edge technology.＜/p＞＜p＞Beginning with an overview of fundamental concepts such as Markov decision processes, dynamic programming, Monte Carlo methods, and temporal difference learning, this book uses clear and concise examples to explain the basics of RL theory. The following section covers value function approximation, a critical technique in RL, and explores various policy approximations such as policy gradient methods and advanced algorithms like Proximal Policy Optimization (PPO).＜/p＞＜p＞This book also delves into advanced topics, including distributed reinforcement learning, curiosity-driven exploration, and the famous AlphaZero algorithm, providing readers with a detailed account of these cutting-edge techniques.＜/p＞＜p＞With a focus on explaining algorithms and the intuition behind them, ＜em＞The Art of Reinforcement Learning＜/em＞ includes practical source code examples that you can use to implement RL algorithms. Upon completing this book, you will have a deep understanding of the concepts, mathematics, and algorithms behind reinforcement learning, making it an essential resource for AI practitioners, researchers, and students.＜/p＞＜p＞＜strong＞What You Will Learn＜/strong＞＜/p＞＜ul＞＜li＞Grasp fundamental concepts and distinguishing features of reinforcement learning, including how it differs from other AI and non-interactive machine learning approaches＜/li＞＜li＞Model problems as Markov decision processes, and how to evaluate and optimize policies using dynamic programming, Monte Carlo methods, and temporal difference learning＜/li＞＜li＞Utilize techniques for approximating value functions and policies, including linear and nonlinear value function approximation and policy gradient methods＜/li＞＜li＞Understand the architecture and advantages of distributed reinforcement learning＜/li＞＜li＞Master the concept of curiosity-driven exploration and how it can be leveraged to improve reinforcement learning agents＜/li＞＜li＞Explore the AlphaZero algorithm and how it was able to beat professional Go players＜/li＞＜/ul＞＜p＞＜strong＞Who This Book Is For＜/strong＞＜/p＞＜p＞Machine learning engineers, data scientists, software engineers, and developers who want to incorporate reinforcement learning algorithms into their projects and applications.＜/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
Advanced Analytics with Power BI and Excel Learn powerful visualization and data analysis techniques using Microsoft BI tools along with Python and R【電子書籍】[ Dejan Sarka ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Empowering You to Master Business Intelligence and Solve Real-world Analytical Problems.＜/strong＞＜/p＞＜p＞＜strong＞DESCRIPTION＜/strong＞＜br /＞ In today's analytics landscape, proficiency in Excel and Power BI is practically a prerequisite for a successful career. This book provides a concise yet comprehensive exploration of these foundational elements of modern Business Intelligence (BI).＜/p＞＜p＞Comprising ten chapters, this book covers the entire development journey of a Power BI analytical solution, spanning from data modeling and preparation to report creation, publication, and dashboard and app deployment. It offers insights into security measures and performance optimization, ensuring a well-rounded understanding of the BI ecosystem. Furthermore, it delves into advanced topics, such as leveraging data science algorithms within reports, offering readers an extensive learning experience.＜/p＞＜p＞The book takes a holistic approach to these technologies, offering a contrast to the narrow perspectives often adopted by contemporary BI professionals who rely solely on a single tool or language. The book allows data enrichment through calculations that can be achieved using various languages, including SQL queries at the source, Power Query Formula Language, M, Python, R, and DAX. By the end of this book, you will master these options but be able to also weigh their pros and cons to make informed decisions.＜/p＞＜p＞＜strong＞TABLE OF CONTENTS＜/strong＞＜/p＞＜ol＞＜li＞Introducing the Theoretical Background for Democratizing Analytics＜/li＞＜li＞Acquiring and transforming data from multiple sources＜/li＞＜li＞Power Query Transformations and Formula Language (M) Overview＜/li＞＜li＞Building a simple analytical solution with Power Pivot in Excel＜/li＞＜li＞Enhancing the model and business analysis with DAX＜/li＞＜li＞Creating reports in Power BI Desktop＜/li＞＜li＞Using the Power BI Service＜/li＞＜li＞Data Science in Power BI＜/li＞＜li＞Optimizing Power BI models and reports＜/li＞＜li＞Deploying, Maintaining and Securing Power BI Assets＜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
Natural Language Understanding with Python Combine natural language technology, deep learning, and large language models to create human-like comprehension【電子書籍】[ Deborah A. Dahl ] 楽天Kobo電子書籍ストア	＜p＞＜b＞Unleash the full potential of natural language understanding (NLU) and create impeccable systems by mastering the art of data acquisition and technology selection Purchase of the print or Kindle book includes a free PDF eBook ＜/b＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞Master NLU concepts from basic text processing to advanced deep learning techniques＜/li＞＜li＞Explore practical NLU applications like chatbots, sentiment analysis, and language translation＜/li＞＜li＞Gain a deeper understanding of large language models like ChatGPT＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞Natural Language Understanding facilitates the organization and structuring of language allowing computer systems to effectively process textual information for various practical applications. Natural Language Understanding with Python will help you explore practical techniques for harnessing NLU to create diverse applications. Complete with step-by-step explanations of essential concepts and practical examples, you’ll begin by learning about NLU and its applications. You’ll then explore a wide range of current NLU techniques and their most appropriate use-case. In the process, you’ll be introduced to the most useful Python NLU libraries. Not only will you learn the basics of NLU, you’ll also discover practical issues such as acquiring data, evaluating systems, and deploying NLU applications along with their solutions. The book is a comprehensive guide that’ll help you explore techniques and resources that can be used for different applications in the future. By the end of this book, you’ll be well-versed with the concepts of natural language understanding, deep learning, and large language models (LLMs) for building various AI-based applications.＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Explore the uses and applications of different NLP techniques＜/li＞＜li＞Understand practical data acquisition and system evaluation workflows＜/li＞＜li＞Build cutting-edge and practical NLP applications to solve problems＜/li＞＜li＞Master NLP development from selecting an application to deployment＜/li＞＜li＞Optimize NLP application maintenance after deployment＜/li＞＜li＞Build a strong foundation in neural networks and deep learning for NLU＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞This book is for python developers, computational linguists, linguists, data scientists, NLP developers, conversational AI developers, and students looking to learn about natural language understanding (NLU) and applying natural language processing (NLP) technology to real problems. Anyone interested in addressing natural language problems will find this book useful. Working knowledge in Python is a must.＜/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
洋書 Paperback, Hands-On Unsupervised Learning with Python: Implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more 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	15,375円
Applied Geospatial Data Science with Python Leverage geospatial data analysis and modeling to find unique solutions to environmental problems【電子書籍】[ David S. Jordan ] 楽天Kobo電子書籍ストア	＜p＞＜b＞Intelligently connect data points and gain a deeper understanding of environmental problems through hands-on Geospatial Data Science case studies written in Python The book includes colored images of important concepts＜/b＞＜/p＞＜h2＞Key Features＜/h2＞＜ul＞＜li＞Learn how to integrate spatial data and spatial thinking into traditional data science workflows＜/li＞＜li＞Develop a spatial perspective and learn to avoid common pitfalls along the way＜/li＞＜li＞Gain expertise through practical case studies applicable in a variety of industries with code samples that can be reproduced and expanded＜/li＞＜/ul＞＜h2＞Book Description＜/h2＞Data scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python. Throughout this book, you’ll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You’ll learn how to read, process, and manipulate spatial data effectively. With data in hand, you’ll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you’ll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries. By the end of the book, you’ll be able to tackle random data, find meaningful correlations, and make geospatial data models.＜h2＞What you will learn＜/h2＞＜ul＞＜li＞Understand the fundamentals needed to work with geospatial data＜/li＞＜li＞Transition from tabular to geo-enabled data in your workflows＜/li＞＜li＞Develop an introductory portfolio of spatial data science work using Python＜/li＞＜li＞Gain hands-on skills with case studies relevant to different industries＜/li＞＜li＞Discover best practices focusing on geospatial data to bring a positive change in your environment＜/li＞＜li＞Explore solving use cases, such as traveling salesperson and vehicle routing problems＜/li＞＜/ul＞＜h2＞Who this book is for＜/h2＞＜p＞This book is for you if you are a data scientist seeking to incorporate geospatial thinking into your workflows or a GIS professional seeking to incorporate data science methods into yours. You’ll need to have a foundational knowledge of Python for data analysis and/or data science.＜/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。	4,734円
MACHINE LEARNING FOR CONCRETE COMPRESSIVE STRENGTH ANALYSIS AND PREDICTION WITH PYTHON【電子書籍】[ Vivian Siahaan ] 楽天Kobo電子書籍ストア	＜p＞Welcome to "Machine Learning for Concrete Compressive Strength Analysis and Prediction with Python." In this book, we will explore the fascinating field of applying machine learning techniques to analyze and predict the compressive strength of concrete.＜/p＞＜p＞First, we will dive into the dataset, which includes various features related to concrete mix proportions, age, and other influential factors. We will explore the dataset's structure, dimensions, and feature types, ensuring that we have a solid understanding of the data we are working with. Then, we will focus on data exploration and visualization. We will utilize histograms, box plots, and scatter plots to gain insights into the distribution of features and their relationships with the target variable, enabling us to uncover valuable patterns and trends within the dataset. Before delving into machine learning algorithms, we must preprocess the data. We will handle missing values, encode categorical variables, and scale numerical features to ensure that our data is in the optimal format for training and testing our models.＜/p＞＜p＞Then, we will explore popular algorithms such as Linear Regression, Decision Trees, Random Forests, Support Vector, Na?ve Bayes, K-Nearest Neighbors, Adaboost, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, Catboost, and Multi-Layer Perceptron regression algorithms and use them to predict the concrete compressive strength accurately. We will evaluate and compare the performance of these models using regression metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R2) score.＜/p＞＜p＞Then, we will explore the exciting world of unsupervised learning by applying K-means clustering. This technique allows us to identify patterns within the data and group similar instances together, leading to valuable insights into the characteristics of different concrete samples. To determine the optimal number of clusters within the data, we will introduce evaluation methods such as the elbow method. We will then visualize the clusters using scatter plots or other appropriate techniques, allowing us to gain a deeper understanding of their distribution and distinct groups.＜/p＞＜p＞Next, we will we employed various machine learning models to predict the clusters in the dataset. These models included Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Adaboost, Gradient Boosting, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LGBM), Catboost, and Multi-Layer Perceptron (MLP). The metrics used are Accuracy: it measures the proportion of correctly classified instances out of the total number of instances. It provides an overall assessment of how well the model predicts the correct cluster memberships.; Recall: it, also known as sensitivity or true positive rate, measures the ability of the model to correctly identify instances belonging to a particular cluster. It is the ratio of true positives to the sum of true positives and false negatives.; Precision: it measures the ability of the model to correctly identify instances belonging to a specific cluster, without including any false positives. It is the ratio of true positives to the sum of true positives and false positives.; F1-score: it is the harmonic mean of precision and recall, providing a balanced measure of model performance. It is useful when the dataset is imbalanced, as it considers both false positives and false negatives.; Macro average (macro avg): it calculates the average performance of the model across all clusters by simply averaging the metric values for each cluster. It treats all clusters equally, regardless of their sizes.; and Weighted average (weighted avg): it calculates the average performance of the model across all clusters, taking into account the size of each cluster. It is calculated by weighting each cluster's metric value by its support, which is the number of instances in that cluster. These metrics help evaluate the model's ability to predict cluster memberships accurately. Accuracy measures the overall correctness of the predictions, while recall and precision focus on the model's performance in correctly assigning instances to specific clusters. Macro average and weighted average provide a summary of model performance across all clusters, considering both individual cluster performance and cluster sizes. By analyzing these metrics, we can assess the model's effectiveness in predicting clusters and compare the performance of different machine learning models.＜/p＞＜p＞By the end of this book, you will have gained valuable insights into how machine learning can be leveraged to analyze and predict the compressive strength of concrete. Get ready to embark on an exciting journey into the world of concrete analysis and prediction with machine learning!＜/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
Mastering Data Science with Python: The Ultimate Guide Unlock the Power of Data Analysis and Visualization with Python's Cutting-Edge Tools and Techniques【電子書籍】[ Daniel Huston ] 楽天Kobo電子書籍ストア	＜p＞Mastering Data Science with Python: The Ultimate Guide" is a comprehensive and practical guide to using Python for data analysis and visualization. The book covers the fundamental concepts and tools needed for data wrangling, machine learning, deep learning, and big data processing with Python. Readers will learn how to use popular Python libraries such as pandas, NumPy, Matplotlib, scikit-learn, Keras, TensorFlow, PySpark, and Dask, to build powerful and scalable data applications. The book is designed for data scientists, analysts, and engineers who want to unlock the full potential of Python for data science. It is also ideal for students and educators who want to learn about the latest techniques and tools for data analysis and visualization. The book includes clear explanations, practical examples, and hands-on exercises that will help readers master the concepts and techniques covered. Whether you are a beginner or an experienced data professional, "Mastering Data Science with Python: The Ultimate Guide" is an indispensable resource for anyone who wants to excel in the field of data science. With this book, readers will gain the skills and knowledge they need to succeed in a rapidly evolving and exciting field.＜/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。	1,656円
Practical Discrete Mathematics Discover math principles that fuel algorithms for computer science and machine learning with Python【電子書籍】[ Ryan T. White ] 楽天Kobo電子書籍ストア	＜p＞A practical guide simplifying discrete math for curious minds and demonstrating its application in solving problems related to software development, computer algorithms, and data science Key Features ? Apply the math of countable objects to practical problems in computer science ? Explore modern Python libraries such as scikit-learn, NumPy, and SciPy for performing mathematics ? Learn complex statistical and mathematical concepts with the help of hands-on examples and expert guidance Book Description Discrete mathematics deals with studying countable, distinct elements, and its principles are widely used in building algorithms for computer science and data science. The knowledge of discrete math concepts will help you understand the algorithms, binary, and general mathematics that sit at the core of data-driven tasks. Practical Discrete Mathematics is a comprehensive introduction for those who are new to the mathematics of countable objects. This book will help you get up to speed with using discrete math principles to take your computer science skills to a more advanced level. As you learn the language of discrete mathematics, you'll also cover methods crucial to studying and describing computer science and machine learning objects and algorithms. The chapters that follow will guide you through how memory and CPUs work. In addition to this, you'll understand how to analyze data for useful patterns, before finally exploring how to apply math concepts in network routing, web searching, and data science. By the end of this book, you'll have a deeper understanding of discrete math and its applications in computer science, and be ready to work on real-world algorithm development and machine learning. What you will learn ? Understand the terminology and methods in discrete math and their usage in algorithms and data problems ? Use Boolean algebra in formal logic and elementary control structures ? Implement combinatorics to measure computational complexity and manage memory allocation ? Use random variables, calculate descriptive statistics, and find average-case computational complexity ? Solve graph problems involved in routing, pathfinding, and graph searches, such as depth-first search ? Perform ML tasks such as data visualization, regression, and dimensionality reduction Who this book is for This book is for computer scientists looking to expand their knowledge of discrete math, the core topic of their field. University students looking to get hands-on with computer science, mathematics, statistics, engineering, or related disciplines will also find this book useful. Basic Python programming skills and knowledge of elementary real-number algebra are required to get started with this book.＜/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。	6,365円
Foundation Dynamic Web Pages with Python Create Dynamic Web Pages with Django and Flask【電子書籍】[ David Ashley ] 楽天Kobo電子書籍ストア	＜p＞Discover the concepts of creating dynamic web pages (HTML) with Python. This book reviews several methods available to serve up dynamic HTML including CGI, SSI, Django, and Flask.＜/p＞＜p＞You will start by covering HTML pages and CSS in general and then move on to creating pages via CGI. It is easy to use and can serve as a foundation for the more advanced services available for launching dynamic web pages. Next you'll explore the SSI (Server Side Interface) method. This is a slightly more advanced interface included in mots web servers that adds functionality to modify static HTML pages to add such things as the current date or time, include additional HTML, and other features to a static web page before it is delivered to the user.＜/p＞＜p＞The book also covers some of the key the Django module features, which must be added to the web server. These features include creating dynamic web pages and calling a database to provide additional information to the web page. Lastly you will explore the Flask module. While it has limited functionality on its own, it provides a very flexible environment to create a self designed system for delivery of dynamic web pages.＜/p＞＜p＞By the time you finish this book, you will be able to choose the appropriate methodology for delivering dynamic information using fast HTML creation services.＜/p＞＜p＞＜strong＞What You'll Learn＜/strong＞＜/p＞＜ul＞＜li＞Use HTML pages and CSS together to control the style of your web site.＜/li＞＜li＞Install and configure SSI, Django, and Flask for Apache.＜/li＞＜li＞Create dynamic web pages using CGI and creating a library of partial HTML pages to use in this task.＜/li＞＜li＞Build dynamic web pages using SSI and auxiliary Python programs to enhance the SSI functionality.＜/li＞＜li＞Develop dynamic web pages using Django.＜/li＞＜/ul＞＜p＞＜strong＞Who This Book Is For＜/strong＞＜/p＞＜p＞Software Developers with basic Python programming skills interested in learning Web Development＜/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。	5,469円
Image Classification: Step-by-step Classifying Images with Python and Techniques of Computer Vision and Machine Learning【電子書籍】[ Mark Magic ] 楽天Kobo電子書籍ストア	＜p＞* Research Fields: Computer Vision and Machine Learning.＜br /＞ * Book Topic: Image classification from an image database.＜br /＞ * Classification Algorithms: (1) Tiny Images Representation + Classifiers; (2) HOG (Histogram of Oriented Gradients) Features Representation + Classifiers; (3) Bag of SIFT (Scale Invariant Feature Transform) Features Representation + Classifiers; (4) Training a CNN (Convolutional Neural Network) from scratch; (5) Fine Tuning a Pre-Trained Deep Network (AlexNet); (6) Pre-Trained Deep Network (AlexNet) Features Representation + Classifiers.＜br /＞ * Classifiers: k-Nearest Neighbors (KNN) and Support Vector Machines (SVM).＜br /＞ * Programming Language: Step-by-step implementation with Python in Jupyter Notebook.＜br /＞ * Processing Units to Execute the Codes: CPU and GPU (on Google Colaboratory).＜br /＞ * Major Steps: For algorithms with classifiers, first processing the images to get the images representations, then training the classifiers with training data, and last testing the classifiers with testing data to get the prediction accuracies; for algorithms with networks, first building a network, then training the network with training data, and last testing the network with testing data to get the prediction accuracies.＜br /＞ * Main Results: For the testing data, the prediction accuracies vary between about 30% and 90%, while the time consumptions varied from several seconds to more than one hour. Considering both of the criteria, the Pre-Trained AlexNet Features Representation plus a Classifier was concluded as the best algorithm.＜br /＞ * Detailed Description:＜br /＞ This book implemented six different algorithms to classify images with the prediction accuracy of the testing data as the primary criterion (the higher the better) and the time consumption as the secondary one (the shorter the better). The accuracies varied between about 30% and 90%, while the time consumptions varied from several seconds to more than one hour. Considering both of the criteria, the Pre-Trained AlexNet Features Representation plus a Classifier, such as the k-Nearest Neighbors (KNN) and the Support Vector Machines (SVM), was concluded as the best algorithm.＜br /＞ The six algorithms are: Tiny Images Representation + Classifiers; HOG (Histogram of Oriented Gradients) Features Representation + Classifiers; Bag of SIFT (Scale Invariant Feature Transform) Features Representation + Classifiers; Training a CNN (Convolutional Neural Network) from scratch; Fine Tuning a Pre-Trained Deep Network (AlexNet); and Pre-Trained Deep Network (AlexNet) Features Representation + Classifiers.＜br /＞ The codes were written with Python in Jupyter Notebook, and they could be executed on both CPUs and GPUs.＜/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。	1,083円
OpenCV 3 Computer Vision with Python Cookbook Leverage the power of OpenCV 3 and Python to build computer vision applications【電子書籍】[ Aleksandr Rybnikov ] 楽天Kobo電子書籍ストア	＜p＞Recipe-based approach to tackle the most common problems in Computer Vision by leveraging the functionality of OpenCV using Python APIs About This Book ? Build computer vision applications with OpenCV functionality via Python API ? Get to grips with image processing, multiple view geometry, and machine learning ? Learn to use deep learning models for image classification, object detection, and face recognition Who This Book Is For This book is for developers who have a basic knowledge of Python. If you are aware of the basics of OpenCV and are ready to build computer vision systems that are smarter, faster, more complex, and more practical than the competition, then this book is for you. What You Will Learn ? Get familiar with low-level image processing methods ? See the common linear algebra tools needed in computer vision ? Work with different camera models and epipolar geometry ? Find out how to detect interesting points in images and compare them ? Binarize images and mask out regions of interest ? Detect objects and track them in videos In Detail OpenCV 3 is a native cross-platform library for computer vision, machine learning, and image processing. OpenCV's convenient high-level APIs hide very powerful internals designed for computational efficiency that can take advantage of multicore and GPU processing. This book will help you tackle increasingly challenging computer vision problems by providing a number of recipes that you can use to improve your applications. In this book, you will learn how to process an image by manipulating pixels and analyze an image using histograms. Then, we'll show you how to apply image filters to enhance image content and exploit the image geometry in order to relay different views of a pictured scene. We'll explore techniques to achieve camera calibration and perform a multiple-view analysis. Later, you'll work on reconstructing a 3D scene from images, converting low-level pixel information to high-level concepts for applications such as object detection and recognition. You'll also discover how to process video from files or cameras and how to detect and track moving objects. Finally, you'll get acquainted with recent approaches in deep learning and neural networks. By the end of the book, you'll be able to apply your skills in OpenCV to create computer vision applications in various domains. Style and approach This book helps you learn the core concepts of OpenCV faster by taking a recipe-based approach where you can try out different code snippets to understand a concept.＜/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。	5,534円
Introduction to Geostatistics with Python and OpenStreetMap【電子書籍】[ Edgar Butwilowski ] 楽天Kobo電子書籍ストア	＜p＞In an era where data swells into a digital tsunami, understanding the language of space is a key competence for navigating our interconnected world. This book is your compass for mastering that language. Using two of the most powerful and accessible digital toolsーPython and OpenStreetMapーyou will embark on a hands-on journey into the world of geostatistics. Designed for readers with basic programming experience, this compact textbook starts with the absolute fundamentals and quickly builds toward real-world applications. You will learn to forge your own geostatistical tools, from functions that calculate the mean and median of spatial data to powerful methods for determining the true center of a distribution, the most central existing feature, and the dispersion of data points. By the end, you won't just have a technical manual; you will hold a new lens through which to view the world, capable of transforming raw data into valuable insights and strategic recommendations. Are you ready to shape the world of tomorrow? The journey begins here. In addition to this textbook, the separately available "Solution Book for Introduction to Geostatistics with Python and OpenStreetMap" is offered, which contains the complete solutions to all tasks and thus allows you to optimally monitor your learning progress.＜/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。	2,737円
Master Parallel And Concurrent Programming With Python【電子書籍】[ Piper Shaw ] 楽天Kobo電子書籍ストア	＜p＞Master parallel and concurrent programming in Python with this comprehensive 2-in-1 guide that equips beginners to advanced developers to create faster, scalable applications. Dive into key concepts like the Global Interpreter Lock (GIL), multiprocessing for CPU-bound tasks, threading and asyncio for I/O-bound operations, and distributed systems with Celery. Through hands-on projects such as data pipelines, web scrapers, real-time chat servers, and image processors, learn to optimize performance, handle synchronization, and deploy high-performance solutions for data science, web development, automation, and AI.＜/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。	1,100円
Bitcoin Programming: Build Blockchain Applications and Smart Contracts with Python and Bitcoin Script【電子書籍】[ Jason Walker ] 楽天Kobo電子書籍ストア	＜p＞Bitcoin Programming Unlock the full potential of Bitcoin by diving deep into the world of decentralized development. Bitcoin Programming is your complete guide to mastering the technologies, protocols, and techniques that power the world's most secure and innovative digital currency. Whether you're an aspiring blockchain developer or an experienced programmer looking to expand your toolkit, this book equips you with everything needed to build real-world Bitcoin applications from scratch. Gain practical skills in transaction creation, wallet development, scripting, and integrating Bitcoin with web-based platforms. Learn how to work with Bitcoin nodes, leverage the latest advancements like Taproot, and explore scalable solutions using Layer 2 technologies such as the Lightning Network. This book doesn't just show you how Bitcoin worksーit teaches you how to build with it. Inside This Book, You'll Discover: How to set up a robust Bitcoin development environment from the ground up The inner workings of Bitcoin transactions and how to construct them programmatically The foundational logic behind Bitcoin Script and how to write powerful custom scripts Techniques for interacting with nodes and utilizing RPC interfaces securely and efficiently The process of wallet development and best practices for address generation How Taproot enhances privacy, efficiency, and scripting capabilities in Bitcoin Integration strategies for adding Bitcoin payments to your web applications With each chapter building your confidence and skill, you'll emerge ready to design secure, scalable, and innovative applications that fully embrace the principles of decentralization and open financial systems. Scroll Up and Grab Your Copy Today!＜/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。	300円
【中古】 Programming Google App Engine with Python: Build and Run Scalable Python Apps on Google's Infrastruc / Dan Sanderson / O’Reilly Media [ペーパーバック]【メール便送料無料】【最短翌日配達対応】もったいない本舗　楽天市場店	著者：Dan Sanderson出版社：O’Reilly Mediaサイズ：ペーパーバックISBN-10：1491900253ISBN-13：9781491900253■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48時間かかる場合があります。あらかじめご了承ください。 ■メール便は、1冊から送料無料です。※宅配便の場合、2,500円以上送料無料です。※最短翌日配達ご希望の方は、宅配便をご選択下さい。※「代引き」ご希望の方は宅配便をご選択下さい。※配送番号付きのゆうパケットをご希望の場合は、追跡可能メール便（送料210円）をご選択ください。■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。■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■まとめ買いの方は「もったいない本舗　おまとめ店」がお買い得です。■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、代引き等、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。	5,412円
Exploratory Data Analysis with Python Cookbook Over 50 recipes to analyze, visualize, and extract insights from structured and unstructured data【電子書籍】[ Ayodele Oluleye ] 楽天Kobo電子書籍ストア	＜p＞＜b＞Extract valuable insights from data by leveraging various analysis and visualization techniques with this comprehensive guide Purchase of the print or Kindle book includes a free PDF eBook＜/b＞＜/p＞＜h2＞Key Features＜/h2＞＜ul＞＜li＞Gain practical experience in conducting EDA on a single variable of interest in Python＜/li＞＜li＞Learn the different techniques for analyzing and exploring tabular, time series, and textual data in Python＜/li＞＜li＞Get well versed in data visualization using leading Python libraries like Matplotlib and seaborn＜/li＞＜/ul＞＜h2＞Book Description＜/h2＞In today's data-centric world, the ability to extract meaningful insights from vast amounts of data has become a valuable skill across industries. Exploratory Data Analysis (EDA) lies at the heart of this process, enabling us to comprehend, visualize, and derive valuable insights from various forms of data. This book is a comprehensive guide to Exploratory Data Analysis using the Python programming language. It provides practical steps needed to effectively explore, analyze, and visualize structured and unstructured data. It offers hands-on guidance and code for concepts such as generating summary statistics, analyzing single and multiple variables, visualizing data, analyzing text data, handling outliers, handling missing values and automating the EDA process. It is suited for data scientists, data analysts, researchers or curious learners looking to gain essential knowledge and practical steps for analyzing vast amounts of data to uncover insights. Python is an open-source general purpose programming language which is used widely for data science and data analysis given its simplicity and versatility. It offers several libraries which can be used to clean, analyze, and visualize data. In this book, we will explore popular Python libraries such as Pandas, Matplotlib, and Seaborn and provide workable code for analyzing data in Python using these libraries. By the end of this book, you will have gained comprehensive knowledge about EDA and mastered the powerful set of EDA techniques and tools required for analyzing both structured and unstructured data to derive valuable insights.＜h2＞What you will learn＜/h2＞＜ul＞＜li＞Perform EDA with leading python data visualization libraries＜/li＞＜li＞Execute univariate, bivariate and multivariate analysis on tabular data＜/li＞＜li＞Uncover patterns and relationships within time series data＜/li＞＜li＞Identify hidden patterns within textual data＜/li＞＜li＞Learn different techniques to prepare data for analysis＜/li＞＜li＞Overcome challenge of outliers and missing values during data analysis＜/li＞＜li＞Leverage automated EDA for fast and efficient analysis＜/li＞＜/ul＞＜h2＞Who this book is for＜/h2＞＜p＞Whether you are a data analyst, data scientist, researcher or a curious learner looking to analyze structured and unstructured data, this book will appeal to you. It aims to empower you with essential knowledge and practical skills for analyzing and visualizing data to uncover insights. It covers several EDA concepts and provides hands-on instructions on how these can be applied using various Python libraries. Familiarity with basic statistical concepts and foundational knowledge of python programming will help you understand the content better and maximize your learning 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。	4,304円
Machine Learning with Python Design and Develop Machine Learning and Deep Learning Technique using real world code examples【電子書籍】[ Abhishek Vijayvargia ] 楽天Kobo電子書籍ストア	＜p＞This book provides concept of machine learning with mathematical explanation and programming examples. Every chapter starts with fundamentals of the technique and working example on real world dataset. Along with the advice on applying algorithms, each technique is provided with advantages and disadvantages on the data.In this book we provide code examples in python. Python is the most suitable and worldwide accepted language for this. First, it is free and open source. It contains very good support from open community. It contains a lot of library, so you don’t need to code everything. Also, it is scalable for large amount of data and suitable for big data technologies.＜/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。	2,598円
Network Science with Python Explore the networks around us using network science, social network analysis, and machine learning【電子書籍】[ David Knickerbocker ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Discover the use of graph networks to develop a new approach to data science using theoretical and practical methods with this expert guide using Python, printed in color＜/strong＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞Create networks using data points and information＜/li＞＜li＞Learn to visualize and analyze networks to better understand communities＜/li＞＜li＞Explore the use of network data in both - supervised and unsupervised machine learning projects＜/li＞＜li＞Purchase of the print or Kindle book includes a free PDF eBook＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞＜p＞Network analysis is often taught with tiny or toy data sets, leaving you with a limited scope of learning and practical usage. Network Science with Python helps you extract relevant data, draw conclusions and build networks using industry-standard ? practical data sets. You'll begin by learning the basics of natural language processing, network science, and social network analysis, then move on to programmatically building and analyzing networks. You'll get a hands-on understanding of the data source, data extraction, interaction with it, and drawing insights from it. This is a hands-on book with theory grounding, specific technical, and mathematical details for future reference. As you progress, you'll learn to construct and clean networks, conduct network analysis, egocentric network analysis, community detection, and use network data with machine learning. You'll also explore network analysis concepts, from basics to an advanced level.＜/p＞＜p＞By the end of the book, you'll be able to identify network data and use it to extract unconventional insights to comprehend the complex world around you.＜/p＞＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Explore NLP, network science, and social network analysis＜/li＞＜li＞Apply the tech stack used for NLP, network science, and analysis＜/li＞＜li＞Extract insights from NLP and network data＜/li＞＜li＞Generate personalized NLP and network projects＜/li＞＜li＞Authenticate and scrape tweets, connections, the web, and data streams＜/li＞＜li＞Discover the use of network data in machine learning projects＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞Network Science with Python demonstrates how programming and social science can be combined to find new insights. Data scientists, NLP engineers, software engineers, social scientists, and data science students will find this book useful. An intermediate level of Python programming is a prerequisite. Readers from both ? social science and programming backgrounds will find a new perspective and add a feather to their hat.＜/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。	5,534円
Ultimate Python for Fintech Solutions: Build Modern Financial Applications and Fintech Solutions Using Finance Packages and Blockchain with Python【電子書籍】[ Bhagvan Kommadi ] 楽天Kobo電子書籍ストア	＜p＞＜em＞＜strong＞Creating Next Gen Apps in Finance＜/strong＞＜/em＞＜/p＞＜p＞＜strong＞Key Features＜/strong＞＜/p＞＜p＞● Master the Python libraries and packages essential for financial applications, enabling robust development.＜/p＞＜p＞● Utilize Python for developing applications that process financial information, visualize data in diverse formats, and create insightful representations.＜/p＞＜p＞● Derive analytical insights from mathematical models integrated into Python applications for data-driven decision-making in finance and fintech.＜/p＞＜p＞＜strong＞Book Description＜/strong＞＜/p＞＜p＞Dive into the dynamic world where finance meets fintech with Python's versatile capabilities in this 'Ultimate Python for Fintech Solutions'.Whether you're aiming to build secure trading platforms, conduct deep statistical analysis, or pioneer next-generation financial technologies, this book quips you with the knowledge, tools, and practical insights to succeed.＜/p＞＜p＞This book starts with Python's foundational programming techniques, essential for understanding financial principles and laying the groundwork for robust applications. You will learn to build scalable solutions that handle complex financial data with ease by using Python for analysis, forecasting, and data visualization.＜/p＞＜p＞Next, it moves to explore advanced topics like AI/ML applications tailored for finance, enabling you to unlock predictive insights and streamline decision-making processes. You will discover how Python integrates cutting-edge technologies such as Big Data and Blockchain, to offer innovative solutions for modern fintech challenges.＜/p＞＜p＞By the end of this expansive book, you will gain the expertise needed to develop sophisticated financial applications, visualize data effectively across desktop and web platforms, and drive innovation in fintech.＜/p＞＜p＞＜strong＞What you will learn＜/strong＞＜/p＞＜p＞● Learn to build robust applications tailored for financial analysis, modeling, and fintech solutions using Python.＜/p＞＜p＞● Learn to analyze large volumes of financial data, and visualize insights effectively.＜/p＞＜p＞● Apply advanced AI/ML techniques to predict trends, optimize financial strategies, and automate decision-making processes.＜/p＞＜p＞● Integrate Python with Big Data platforms and Blockchain technologies to work with massive datasets and decentralized financial systems.＜/p＞＜p＞● Acquire the knowledge and skills to innovate in the fintech space to address modern financial challenges and opportunities.＜/p＞＜p＞＜strong＞Table of Contents＜/strong＞＜/p＞＜ol＞＜li＞＜p＞Getting Started on Python Infrastructure and Building Financial Apps＜/p＞＜/li＞＜li＞＜p＞Learning Financial Concepts Using Python＜/p＞＜/li＞＜li＞＜p＞Data Structures and Algorithms Using Python＜/p＞＜/li＞＜li＞＜p＞Object Oriented Programming Using Python＜/p＞＜/li＞＜li＞＜p＞Building Simulation and Mathematical Analysis Tools Using Python＜/p＞＜/li＞＜li＞＜p＞Stochastic Mathematics and Building Models Using Python＜/p＞＜/li＞＜li＞＜p＞Prediction Algorithms Using Python＜/p＞＜/li＞＜li＞＜p＞Data Science and Statistical Algorithms Using Python＜/p＞＜/li＞＜li＞＜p＞Desktop and Web Charting Using Python＜/p＞＜/li＞＜li＞＜p＞AI/ML Apps Using Python＜/p＞＜/li＞＜li＞＜p＞Big Data/Blockchain-Based Solutions Using Python＜/p＞＜/li＞＜li＞＜p＞Next Generation FinTech Apps Using Python with Financial Singularity＜/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
Data Mining with Python Theory, Application, and Case Studies【電子書籍】[ Di Wu ] 楽天Kobo電子書籍ストア	＜p＞Data is everywhere and it’s growing at an unprecedented rate. But making sense of all that data is a challenge. Data Mining is the process of discovering patterns and knowledge from large data sets, and ＜em＞＜strong＞Data Mining with Python＜/strong＞＜/em＞ focuses on the hands-on approach to learning Data Mining. It showcases how to use Python Packages to fulfill the Data Mining pipeline, which is to collect, integrate, manipulate, clean, process, organize, and analyze data for knowledge.＜/p＞＜p＞The contents are organized based on the Data Mining pipeline, so readers can naturally progress step by step through the process. Topics, methods, and tools are explained in three aspects: “What it is” as a theoretical background, “why we need it” as an application orientation, and “how we do it” as a case study.＜/p＞＜p＞This book is designed to give students, data scientists, and business analysts an understanding of Data Mining concepts in an applicable way. Through interactive tutorials that can be run, modified, and used for a more comprehensive learning experience, this book will help its readers to gain practical skills to implement Data Mining techniques in their work.＜/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。	10,418円
Data Labeling in Machine Learning with Python Explore modern ways to prepare labeled data for training and fine-tuning ML and generative AI models【電子書籍】[ Vijaya Kumar Suda ] 楽天Kobo電子書籍ストア	＜p＞＜b＞Take your data preparation, machine learning, and GenAI skills to the next level by learning a range of Python algorithms and tools for data labeling＜/b＞＜/p＞＜h2＞Key Features＜/h2＞＜ul＞＜li＞Generate labels for regression in scenarios with limited training data＜/li＞＜li＞Apply generative AI and large language models (LLMs) to explore and label text data＜/li＞＜li＞Leverage Python libraries for image, video, and audio data analysis and data labeling＜/li＞＜li＞Purchase of the print or Kindle book includes a free PDF eBook＜/li＞＜/ul＞＜h2＞Book Description＜/h2＞Data labeling is the invisible hand that guides the power of artificial intelligence and machine learning. In today’s data-driven world, mastering data labeling is not just an advantage, it’s a necessity. Data Labeling in Machine Learning with Python empowers you to unearth value from raw data, create intelligent systems, and influence the course of technological evolution. With this book, you'll discover the art of employing summary statistics, weak supervision, programmatic rules, and heuristics to assign labels to unlabeled training data programmatically. As you progress, you'll be able to enhance your datasets by mastering the intricacies of semi-supervised learning and data augmentation. Venturing further into the data landscape, you'll immerse yourself in the annotation of image, video, and audio data, harnessing the power of Python libraries such as seaborn, matplotlib, cv2, librosa, openai, and langchain. With hands-on guidance and practical examples, you'll gain proficiency in annotating diverse data types effectively. By the end of this book, you’ll have the practical expertise to programmatically label diverse data types and enhance datasets, unlocking the full potential of your data.＜h2＞What you will learn＜/h2＞＜ul＞＜li＞Excel in exploratory data analysis (EDA) for tabular, text, audio, video, and image data＜/li＞＜li＞Understand how to use Python libraries to apply rules to label raw data＜/li＞＜li＞Discover data augmentation techniques for adding classification labels＜/li＞＜li＞Leverage K-means clustering to classify unsupervised data＜/li＞＜li＞Explore how hybrid supervised learning is applied to add labels for classification＜/li＞＜li＞Master text data classification with generative AI＜/li＞＜li＞Detect objects and classify images with OpenCV and YOLO＜/li＞＜li＞Uncover a range of techniques and resources for data annotation＜/li＞＜/ul＞＜h2＞Who this book is for＜/h2＞＜p＞This book is for machine learning engineers, data scientists, and data engineers who want to learn data labeling methods and algorithms for model training. Data enthusiasts and Python developers will be able to use this book to learn data exploration and annotation using Python libraries. Basic Python knowledge is beneficial but not necessary to get started.＜/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
Machine Learning Theory and Applications Hands-on Use Cases with Python on Classical and Quantum Machines【電子書籍】[ Xavier Vasques ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Machine Learning Theory and Applications＜/strong＞＜/p＞＜p＞＜strong＞Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries＜/strong＞＜/p＞＜p＞＜em＞Machine Learning Theory and Applications＜/em＞ delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending mathematical concepts and implementing them in real-world scenarios using Python and renowned open-source libraries. This comprehensive guide covers a wide range of topics, including data preparation, feature engineering techniques, commonly utilized machine learning algorithms like support vector machines and neural networks, as well as generative AI and foundation models. To facilitate the creation of machine learning pipelines, a dedicated open-source framework named hephAIstos has been developed exclusively for this book. Moreover, the text explores the fascinating domain of quantum machine learning and offers insights on executing machine learning applications across diverse hardware technologies such as CPUs, GPUs, and QPUs. Finally, the book explains how to deploy trained models through containerized applications using Kubernetes and OpenShift, as well as their integration through machine learning operations (MLOps).＜/p＞＜p＞Additional topics covered in ＜em＞Machine Learning Theory and Applications＜/em＞ include:＜/p＞＜ul＞＜li＞Current use cases of AI, including making predictions, recognizing images and speech, performing medical diagnoses, creating intelligent supply chains, natural language processing, and much more＜/li＞＜li＞Classical and quantum machine learning algorithms such as quantum-enhanced Support Vector Machines (QSVMs), QSVM multiclass classification, quantum neural networks, and quantum generative adversarial networks (qGANs)＜/li＞＜li＞Different ways to manipulate data, such as handling missing data, analyzing categorical data, or processing time-related data＜/li＞＜li＞Feature rescaling, extraction, and selection, and how to put your trained models to life and production through containerized applications＜/li＞＜/ul＞＜p＞＜em＞Machine Learning Theory and Applications＜/em＞ is an essential resource for data scientists, engineers, and IT specialists and architects, as well as students in computer science, mathematics, and bioinformatics. The reader is expected to understand basic Python programming and libraries such as NumPy or Pandas and basic mathematical concepts, especially linear algebra.＜/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。	10,763円
Linear Algebra, Signal Processing, and Wavelets - A Unified Approach Python Version【電子書籍】[ ?yvind Ryan ] 楽天Kobo電子書籍ストア	＜p＞This book offers a user friendly, hands-on, and systematic introduction to applied and computational harmonic analysis: to Fourier analysis, signal processing and wavelets; and to their interplay and applications. The approach is novel, and the book can be used in undergraduate courses, for example, following a first course in linear algebra, but is also suitable for use in graduate level courses. The book will benefit anyone with a basic background in linear algebra. It defines fundamental concepts in signal processing and wavelet theory, assuming only a familiarity with elementary linear algebra. No background in signal processing is needed. Additionally, the book demonstrates in detail why linear algebra is often the best way to go. Those with only a signal processing background are also introduced to the world of linear algebra, although a full course is recommended.＜/p＞＜p＞The book comes in two versions: one based on MATLAB, and one on Python, demonstrating the feasibilityand applications of both approaches. Most of the code is available interactively. The applications mainly involve sound and images. The book also includes a rich set of exercises, many of which are of a computational nature.＜/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。	8,507円