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【セール＆マラソンポイント22.5倍半額有り】ソフトボード代引き*同梱不可 6'0 OCEAN＆EARTH ハッピーアワー HAPPY HOUR ソフトサーフボードサーフボードサーフィン初心者中級上級セカンドボードショートボードエポキシオーシャンアンドアースプレゼント伊豆下田海の目の前白浜マリーナ	表面は凹凸があるので、滑りにくい加工がしてあります。ワックスなしでそのままま使えます。独自の製法を用いたスペシャルソフトボード。パフォーマンスのために狭いピンテール。ノーズエリアが広く、パドリングが楽々です。素早く波をキャッチするために、簡単にパドリングできる最小限のロッカー。とにかくスピードが出るので、初心者から上級者まで楽しめるソフトサーフボード（ソフトボード）です。 ●EPS CORE- 高度な防水性と軽量性。2Xストリンガーはボードの強度を高め、ロッカーを維持します。 ●HDPE BOTTOM PLATE- 1mm厚のHDPE高密度ポリエチレンプレート。耐久性、強度のあるパフォーマンススリック。 ●交換可能なフィンボックス。ほとんどの主要なフィンブランドと互換性があります。フィンをすばやく簡単に交換できます。（付属の六角フィンキーで固定します。） ●PU TEXTURED DECK SKIN- グリッピー（滑りにくい）なテクスチャデッキ表面。耐水性が高く、耐久性に優れています。優れたグリップ力と耐衝撃性。 ●5フィンのセットアップ- お気に入りのシングルタブフィンでスラスターまたはクワッドセットアップとして遊べます。2XソフトチップPUフィンとフィンキーが付属。【カラー】・スカイブルー・アプリコット・バイオレット【サイズ】・長　さ：6'0"(183cm) ・横　幅：21"(53.34cm) ・厚　さ：2 3/4"(6.99cm) ・容　量：38L ・テイル：ラウウンドピン・フィン：2+1フィン付属 1x 8 "ポリカーボネートセンターフィン2x4（中央= 10 "ボックス）ポリカーボネートサイドフィン（サイドフィン=シングルタブX4）フューチャーフィンと互換性あり【素　材】・デッキ：テクスチャードEVA ・ボトム： HDPE 【ご注意】 ※高温になる車内などに長時間放置しますと、剥離、膨張の症状がみられる可能性がございますので、保管方法には十分ご注意ください。【ご購入時のご注意点】・こちらの商品は代金引換配送には対応いたしません。・メーカー直送の為、他の商品と同梱不可・日時の指定不可商品です。・平日午前中のご注文は当日出荷を心がけております。・一部地域は、お取り寄せでのご対応となる場合がございます。（3-5日で出荷手配となります。）・ご住所を確認後、直送不可の場合はご案内いたします。（東北・茨城・大阪・離島・他一部地域）・離島も発送いたします。・離島については別途送料がかかる場合がございます。（注文確認メールにてお知らせいたします。）・離島送料についてはご利用しやすいよう、運送会社と提携しております。・小笠原/伊豆七島などのお客様にも多数ご利用いただいております。お気軽にお問合せください。メーカー希望小売価格はメーカーカタログに基づいて掲載していますソフトボード代引き同梱不可 6'0 OCEAN＆EARTH ハッピーアワー HAPPY HOUR ソフトサーフボードサーフボードサーフィン初心者中級上級セカンドボードショートボードエポキシオーシャンアンドアースプレゼントおすすめ白浜マリーナソフトボード 6'0 OCEAN＆EARTH ハッピーアワー HAPPY HOUR フィン付属！安心安全ソフトサーフボード！ FIN付き。表面は凹凸があるので、滑りにくい加工がしてあります。ワックスなしでそのままま使えます。独自の製法を用いたスペシャルソフトボード。パフォーマンスのために狭いピンテール。ノーズエリアが広く、パドリングが楽々です。素早く波をキャッチするために、簡単にパドリングできる最小限のロッカー。とにかくスピードが出るので、初心者から上級者まで楽しめるソフトサーフボード（ソフトボード）です。 ●EPSCORE- 高度な防水性と軽量性。2Xストリンガーはボードの強度を高め、ロッカーを維持します。 ●HDPE BOTTOMPLATE- 1mm厚のHDPE高密度ポリエチレンプレート。耐久性、強度のあるパフォーマンススリック。 ●交換可能なフィンボックス。ほとんどの主要なフィンブランドと互換性があります。フィンをすばやく簡単に交換できます。（付属の六角フィンキーで固定します。） ●PU TEXTURED DECKSKIN- グリッピー（滑りにくい）なテクスチャデッキ表面。耐水性が高く、耐久性に優れています。優れたグリップ力と耐衝撃性。 ●5フィンのセットアップ- お気に入りのシングルタブフィンでスラスターまたはクワッドセットアップとして遊べます。2XソフトチップPUフィンとフィンキーが付属。【カラー】・スカイブルー・アプリコット・バイオレット【サイズ】・長　さ：6'0"(183cm) ・横　幅：21"(53.34cm) ・厚　さ：2 3/4"(6.99cm) ・容　量：38L ・テイル：ラウウンドピン・フィン：2+1フィン付属 1x 8 "ポリカーボネートセンターフィン2x4（中央= 10 "ボックス）ポリカーボネートサイドフィン（サイドフィン=シングルタブX4）フューチャーフィンと互換性あり【素　材】・デッキ：テクスチャードEVA ・ボトム： HDPE 【ご注意】 ※高温になる車内などに長時間放置しますと、剥離、膨張の症状がみられる可能性がございますので、保管方法には十分ご注意ください。【ご購入時のご注意点】・こちらの商品は代金引換配送には対応いたしません。・メーカー直送の為、他の商品と同梱不可・日時の指定不可商品です。・平日午前中のご注文は当日出荷を心がけております。・一部地域は、お取り寄せでのご対応となる場合がございます。（3-5日で出荷手配となります。）・ご住所を確認後、直送不可の場合はご案内いたします。（東北・茨城・大阪・離島・他一部地域）・離島も発送いたします。・離島については別途送料がかかる場合がございます。（注文確認メールにてお知らせいたします。）・離島送料についてはご利用しやすいよう、運送会社と提携しております。・小笠原/伊豆七島などのお客様にも多数ご利用いただいております。お気軽にお問合せください。	53,900円
【中古】フューチャーズ・アンド・オプションズ/CD/SOLC-010 / サイコポッド / SOLSTICE MUSIC [CD]【ネコポス発送】もったいない本舗　お急ぎ便店	EANコード：4522197010106■こちらの商品もオススメです ● ディミトリ・D．K．N・プレゼンツ・ザ・ユージュアル・サスペクツ・イン・ザ・ミックス/CD/SOLC-014 / ディミトリ・D.K.N / SOLSTICE MUSIC [CD] ● クローズ・エンカウンターズ/CD/SOLC-015 / ジ・アンティドート / SOLSTICE MUSIC [CD] ● リスターター/CD/SOLMC-060 / トラナン / SOLSTICE MUSIC [CD] ● Astral Scene / Astral Projection / Astral Projection / Gear Fab Records [CD] ● マキシマム・ベロシティ/CD/SOLMC-032 / クランキー・パンチ / SOLSTICE MUSIC [CD] ● Hi-tech Pleasures: Level 2 / Various / Crystal Matrix [CD] ● アンティドットコム/CD/SOLC-005 / ジ・アンティドート / SOLSTICE MUSIC [CD] ● ジョイライダー/CD/SOLMC-058 / フレキションズ / SOLSTICE MUSIC [CD] ● ウエスタン・レベル・アライアンス/CD/SOLMC-068 / ウエスタン・レベル・アライアンス / SOLSTICE MUSIC [CD] ● ワン・ネーション・アンダー・トランス/CD/SOLMC-075 / クアドラ / SOLSTICE MUSIC [CD] ● フェイズ3/CD/SOLMC-051 / オムニバス / SOLSTICE MUSIC [CD] ● レッツ・ロック/CDシングル（12cm）/SOLC-003 / エレクトリック・ティーズ / SOLSTICE MUSIC [CD] ● Liquid Logic / Various / Phantasm [CD] ● The Singles SpiritZone / Spirit Zone / Efa Imports [CD] ■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：サイコポッド枚数：1枚組み限定盤：通常曲数：10曲曲名：DISK1 1.ツー・ドッツ2.サイレント・ランニング3.フューチャーズ4.オプションズ5.ウェンズデイ6.グルーブ・モデレーター7.マグネティック・フォース8.アポロ9.ゴーイング・ダウン・イン・チャイナタウン10.トロン型番：SOLC-010発売年月日：2002年05月10日	477円
Building Data Science Applications with FastAPI Develop, manage, and deploy efficient machine learning applications with Python【電子書籍】[ Fran?ois Voron ] 楽天Kobo電子書籍ストア	＜p＞＜b＞Learn all the features and best practices of FastAPI to build, deploy, and monitor powerful data science and AI apps, like object detection or image generation. Purchase of the print or Kindle book includes a free PDF eBook＜/b＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞Uncover the secrets of FastAPI, including async I/O, type hinting, and dependency injection＜/li＞＜li＞Learn to add authentication, authorization, and interaction with databases in a FastAPI backend＜/li＞＜li＞Develop real-world projects using pre-trained AI models＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞Building Data Science Applications with FastAPI is the go-to resource for creating efficient and dependable data science API backends. This second edition incorporates the latest Python and FastAPI advancements, along with two new AI projects ? a real-time object detection system and a text-to-image generation platform using Stable Diffusion. The book starts with the basics of FastAPI and modern Python programming. You'll grasp FastAPI's robust dependency injection system, which facilitates seamless database communication, authentication implementation, and ML model integration. As you progress, you'll learn testing and deployment best practices, guaranteeing high-quality, resilient applications. Throughout the book, you'll build data science applications using FastAPI with the help of projects covering common AI use cases, such as object detection and text-to-image generation. These hands-on experiences will deepen your understanding of using FastAPI in real-world scenarios. By the end of this book, you'll be well equipped to maintain, design, and monitor applications to meet the highest programming standards using FastAPI, empowering you to create fast and reliable data science API backends with ease while keeping up with the latest advancements.＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Explore the basics of modern Python and async I/O programming＜/li＞＜li＞Get to grips with basic and advanced concepts of the FastAPI framework＜/li＞＜li＞Deploy a performant and reliable web backend for a data science application＜/li＞＜li＞Integrate common Python data science libraries into a web backend＜/li＞＜li＞Integrate an object detection algorithm into a FastAPI backend＜/li＞＜li＞Build a distributed text-to-image AI system with Stable Diffusion＜/li＞＜li＞Add metrics and logging and learn how to monitor them＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞This book is for data scientists and software developers interested in gaining knowledge of FastAPI and its ecosystem to build data science applications. Basic knowledge of data science and machine learning concepts and how to apply them in Python 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。	4,304円
Learning Geospatial Analysis with Python Understand GIS fundamentals and perform remote sensing data analysis using Python 3.7【電子書籍】[ Joel Lawhead ] 楽天Kobo電子書籍ストア	＜p＞＜b＞Learn the core concepts of geospatial data analysis for building actionable and insightful GIS applications＜/b＞＜/p＞＜h2＞Key Features＜/h2＞＜ul＞＜li＞Create GIS solutions using the new features introduced in Python 3.7＜/li＞＜li＞Explore a range of GIS tools and libraries such as PostGIS, QGIS, and PROJ＜/li＞＜li＞Learn to automate geospatial analysis workflows using Python and Jupyter＜/li＞＜/ul＞＜h2＞Book Description＜/h2＞Geospatial analysis is used in almost every domain you can think of, including defense, farming, and even medicine. With this systematic guide, you'll get started with geographic information system (GIS) and remote sensing analysis using the latest features in Python. This book will take you through GIS techniques, geodatabases, geospatial raster data, and much more using the latest built-in tools and libraries in Python 3.7. You'll learn everything you need to know about using software packages or APIs and generic algorithms that can be used for different situations. Furthermore, you'll learn how to apply simple Python GIS geospatial processes to a variety of problems, and work with remote sensing data. By the end of the book, you'll be able to build a generic corporate system, which can be implemented in any organization to manage customer support requests and field support personnel.＜h2＞What you will learn＜/h2＞＜ul＞＜li＞Automate geospatial analysis workflows using Python＜/li＞＜li＞Code the simplest possible GIS in just 60 lines of Python＜/li＞＜li＞Create thematic maps with Python tools such as PyShp, OGR, and the Python Imaging Library＜/li＞＜li＞Understand the different formats that geospatial data comes in＜/li＞＜li＞Produce elevation contours using Python tools＜/li＞＜li＞Create flood inundation models＜/li＞＜li＞Apply geospatial analysis to real-time data tracking and storm chasing＜/li＞＜/ul＞＜h2＞Who this book is for＜/h2＞＜p＞This book is for Python developers, researchers, or analysts who want to perform geospatial modeling and GIS analysis with Python. Basic knowledge of digital mapping and analysis using Python or other scripting languages will be helpful.＜/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
Hands-On Data Structures and Algorithms with Python Store, manipulate, and access data effectively and boost the performance of your applications, 3rd Edition【電子書籍】[ Dr. Basant Agarwal ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Understand how implementing different data structures and algorithms intelligently can make your Python code and applications more maintainable and efficient＜/strong＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞Explore functional and reactive implementations of traditional and advanced data structures＜/li＞＜li＞Apply a diverse range of algorithms in your Python code＜/li＞＜li＞Implement the skills you have learned to maximize the performance of your applications＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞＜p＞Choosing the right data structure is pivotal to optimizing the performance and scalability of applications. This new edition of Hands-On Data Structures and Algorithms with Python will expand your understanding of key structures, including stacks, queues, and lists, and also show you how to apply priority queues and heaps in applications. You'll learn how to analyze and compare Python algorithms, and understand which algorithms should be used for a problem based on running time and computational complexity. You will also become confident organizing your code in a manageable, consistent, and scalable way, which will boost your productivity as a Python developer.＜/p＞＜p＞By the end of this Python book, you'll be able to manipulate the most important data structures and algorithms to more efficiently store, organize, and access data in your applications.＜/p＞＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Understand common data structures and algorithms using examples, diagrams, and exercises＜/li＞＜li＞Explore how more complex structures, such as priority queues and heaps, can benefit your code＜/li＞＜li＞Implement searching, sorting, and selection algorithms on number and string sequences＜/li＞＜li＞Become confident with key string-matching algorithms＜/li＞＜li＞Understand algorithmic paradigms and apply dynamic programming techniques＜/li＞＜li＞Use asymptotic notation to analyze algorithm performance with regard to time and space complexities＜/li＞＜li＞Write powerful, robust code using the latest features of Python＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞This book is for developers and programmers who are interested in learning about data structures and algorithms in Python to write complex, flexible programs. Basic Python programming knowledge is expected.＜/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。	5,739円
Interpretable Machine Learning with Python Build explainable, fair, and robust high-performance models with hands-on, real-world examples【電子書籍】[ Serg Mas?s ] 楽天Kobo電子書籍ストア	＜p＞＜b＞A deep dive into the key aspects and challenges of machine learning interpretability using a comprehensive toolkit, including SHAP, feature importance, and causal inference, to build fairer, safer, and more reliable models. Purchase of the print or Kindle book includes a free eBook in PDF format.＜/b＞＜/p＞＜h2＞Key Features＜/h2＞＜ul＞＜li＞Interpret real-world data, including cardiovascular disease data and the COMPAS recidivism scores＜/li＞＜li＞Build your interpretability toolkit with global, local, model-agnostic, and model-specific methods＜/li＞＜li＞Analyze and extract insights from complex models from CNNs to BERT to time series models＜/li＞＜/ul＞＜h2＞Book Description＜/h2＞Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models. Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps. In addition to the step-by-step code, you’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. By the end of the book, you’ll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data.＜h2＞What you will learn＜/h2＞＜ul＞＜li＞Progress from basic to advanced techniques, such as causal inference and quantifying uncertainty＜/li＞＜li＞Build your skillset from analyzing linear and logistic models to complex ones, such as CatBoost, CNNs, and NLP transformers＜/li＞＜li＞Use monotonic and interaction constraints to make fairer and safer models＜/li＞＜li＞Understand how to mitigate the influence of bias in datasets＜/li＞＜li＞Leverage sensitivity analysis factor prioritization and factor fixing for any model＜/li＞＜li＞Discover how to make models more reliable with adversarial robustness＜/li＞＜/ul＞＜h2＞Who this book is for＜/h2＞＜p＞This book is for data scientists, machine learning developers, machine learning engineers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the artificial intelligence systems they develop work, their impact on decision making, and how they identify and manage bias. It’s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a good grasp of the Python programming language is needed to implement 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
Quantitative Finance with Python A Practical Guide to Investment Management, Trading, and Financial Engineering【電子書籍】[ Chris Kelliher ] 楽天Kobo電子書籍ストア	＜p＞＜em＞＜strong＞Quantitative Finance with Python: A Practical Guide to Investment Management, Trading and Financial Engineering＜/strong＞＜/em＞ bridges the gap between the theory of mathematical finance and the practical applications of these concepts for derivative pricing and portfolio management. The book provides students with a very hands-on, rigorous introduction to foundational topics in quant finance, such as options pricing, portfolio optimization and machine learning. Simultaneously, the reader benefits from a strong emphasis on the practical applications of these concepts for institutional investors.＜/p＞＜p＞＜strong＞Features＜/strong＞＜/p＞＜ul＞＜li＞Useful as both a teaching resource and as a practical tool for professional investors.＜/li＞＜li＞Ideal textbook for first year graduate students in quantitative finance programs, such as those in master’s programs in Mathematical Finance, Quant Finance or Financial Engineering.＜/li＞＜li＞Includes a perspective on the future of quant finance techniques, and in particular covers some introductory concepts of Machine Learning.＜/li＞＜li＞Free-to-access repository with Python codes available at www.routledge.com/ 9781032014432 and on ＜a href="github.com/lingyixu/Quant-Finance-With-Python-Code"＞github.com/lingyixu/Quant-Finance-With-Python-Code＜/a＞.＜/li＞＜/ul＞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。	21,951円
Probabilistic Machine Learning for Finance and Investing A Primer to Generative AI with Python【電子書籍】[ Deepak K. Kanungo ] 楽天Kobo電子書籍ストア	＜p＞There are several reasons why probabilistic machine learning represents the next-generation ML framework and technology for finance and investing. This generative ensemble learns continually from small and noisy financial datasets while seamlessly enabling probabilistic inference, retrodiction, prediction, and counterfactual reasoning. Probabilistic ML also lets you systematically encode personal, empirical, and institutional knowledge into ML models.＜/p＞＜p＞Whether they're based on academic theories or ML strategies, all financial models are subject to modeling errors that can be mitigated but not eliminated. Probabilistic ML systems treat uncertainties and errors of financial and investing systems as features, not bugs. And they quantify uncertainty generated from inexact inputs and outputs as probability distributions, not point estimates. This makes for realistic financial inferences and predictions that are useful for decision-making and risk management.＜/p＞＜p＞Unlike conventional AI, these systems are capable of warning us when their inferences and predictions are no longer useful in the current market environment. By moving away from flawed statistical methodologies and a restrictive conventional view of probability as a limiting frequency, you’ll move toward an intuitive view of probability as logic within an axiomatic statistical framework that comprehensively and successfully quantifies uncertainty. This book shows you how.＜/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。	5,430円
Generative AI with Python and TensorFlow 2 Create images, text, and music with VAEs, GANs, LSTMs, Transformer models【電子書籍】[ Joseph Babcock ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Fun and exciting projects to learn what artificial minds can create＜/strong＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞Code examples are in TensorFlow 2, which make it easy for PyTorch users to follow along＜/li＞＜li＞Look inside the most famous deep generative models, from GPT to MuseGAN＜/li＞＜li＞Learn to build and adapt your own models in TensorFlow 2.x＜/li＞＜li＞Explore exciting, cutting-edge use cases for deep generative AI＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞＜p＞Machines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI?＜/p＞＜p＞In this book, you'll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You'll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks.＜/p＞＜p＞There's been an explosion in potential use cases for generative models. You'll look at Open AI's news generator, deepfakes, and training deep learning agents to navigate a simulated environment.＜/p＞＜p＞Recreate the code that's under the hood and uncover surprising links between text, image, and music generation.＜/p＞＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Export the code from GitHub into Google Colab to see how everything works for yourself＜/li＞＜li＞Compose music using LSTM models, simple GANs, and MuseGAN＜/li＞＜li＞Create deepfakes using facial landmarks, autoencoders, and pix2pix GAN＜/li＞＜li＞Learn how attention and transformers have changed NLP＜/li＞＜li＞Build several text generation pipelines based on LSTMs, BERT, and GPT-2＜/li＞＜li＞Implement paired and unpaired style transfer with networks like StyleGAN＜/li＞＜li＞Discover emerging applications of generative AI like folding proteins and creating videos from images＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞This is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for 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。	4,160円
Doing Math with Python Use Programming to Explore Algebra, Statistics, Calculus, and More!【電子書籍】[ Amit Saha ] 楽天Kobo電子書籍ストア	＜p＞＜em＞Doing Math with Python＜/em＞ shows you how to use Python to delve into high school?level math topics like statistics, geometry, probability, and calculus. You’ll start with simple projects, like a factoring program and a quadratic-equation solver, and then create more complex projects once you’ve gotten the hang of things.＜/p＞＜p＞Along the way, you’ll discover new ways to explore math and gain valuable programming skills that you’ll use throughout your study of math and computer science. Learn how to:＜br /＞ ?Describe your data with statistics, and visualize it with line graphs, bar charts, and scatter plots＜br /＞ ?Explore set theory and probability with programs for coin flips, dicing, and other games of chance＜br /＞ ?Solve algebra problems using Python’s symbolic math functions＜br /＞ ?Draw geometric shapes and explore fractals like the Barnsley fern, the Sierpinski triangle, and the Mandelbrot set＜br /＞ ?Write programs to find derivatives and integrate functions＜/p＞＜p＞Creative coding challenges and applied examples help you see how you can put your new math and coding skills into practice. You’ll write an inequality solver, plot gravity’s effect on how far a bullet will travel, shuffle a deck of cards, estimate the area of a circle by throwing 100,000 "darts" at a board, explore the relationship between the Fibonacci sequence and the golden ratio, and more.＜/p＞＜p＞Whether you’re interested in math but have yet to dip into programming or you’re a teacher looking to bring programming into the classroom, you’ll find that Python makes programming easy and practical. Let Python handle the grunt work while you focus on the math.＜/p＞＜p＞＜strong＞Uses Python 3＜/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。	3,781円
Hands-On Web Scraping with Python Perform advanced scraping operations using various Python libraries and tools such as Selenium, Regex, and others【電子書籍】[ Anish Chapagain ] 楽天Kobo電子書籍ストア	＜p＞＜b＞Collect and scrape different complexities of data from the modern Web using the latest tools, best practices, and techniques ＜/b＞＜/p＞＜h2＞Key Features＜/h2＞＜ul＞＜li＞Learn different scraping techniques using a range of Python libraries such as Scrapy and Beautiful Soup＜/li＞＜li＞Build scrapers and crawlers to extract relevant information from the web＜/li＞＜li＞Automate web scraping operations to bridge the accuracy gap and manage complex business needs＜/li＞＜/ul＞＜h2＞Book Description＜/h2＞Web scraping is an essential technique used in many organizations to gather valuable data from web pages. This book will enable you to delve into web scraping techniques and methodologies. The book will introduce you to the fundamental concepts of web scraping techniques and how they can be applied to multiple sets of web pages. You'll use powerful libraries from the Python ecosystem such as Scrapy, lxml, pyquery, and bs4 to carry out web scraping operations. You will then get up to speed with simple to intermediate scraping operations such as identifying information from web pages and using patterns or attributes to retrieve information. This book adopts a practical approach to web scraping concepts and tools, guiding you through a series of use cases and showing you how to use the best tools and techniques to efficiently scrape web pages. You'll even cover the use of other popular web scraping tools, such as Selenium, Regex, and web-based APIs. By the end of this book, you will have learned how to efficiently scrape the web using different techniques with Python and other popular tools.＜h2＞What you will learn＜/h2＞＜ul＞＜li＞Analyze data and information from web pages＜/li＞＜li＞Learn how to use browser-based developer tools from the scraping perspective＜/li＞＜li＞Use XPath and CSS selectors to identify and explore markup elements＜/li＞＜li＞Learn to handle and manage cookies＜/li＞＜li＞Explore advanced concepts in handling HTML forms and processing logins＜/li＞＜li＞Optimize web securities, data storage, and API use to scrape data＜/li＞＜li＞Use Regex with Python to extract data＜/li＞＜li＞Deal with complex web entities by using Selenium to find and extract data＜/li＞＜/ul＞＜h2＞Who this book is for＜/h2＞＜p＞This book is for Python programmers, data analysts, web scraping newbies, and anyone who wants to learn how to perform web scraping from scratch. If you want to begin your journey in applying web scraping techniques to a range of web pages, then this book is what you need! A working knowledge of the Python programming language is expected. ＜/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
Mastering Geospatial Analysis with Python Explore GIS processing and learn to work with GeoDjango, CARTOframes and MapboxGL-Jupyter【電子書籍】[ Silas Toms ] 楽天Kobo電子書籍ストア	＜p＞Explore GIS processing and learn to work with various tools and libraries in Python. About This Book ? Analyze and process geospatial data using Python libraries such as; Anaconda, GeoPandas ? Leverage new ArcGIS API to process geospatial data for the cloud. ? Explore various Python geospatial web and machine learning frameworks. Who This Book Is For The audience for this book includes students, developers, and geospatial professionals who need a reference book that covers GIS data management, analysis, and automation techniques with code libraries built in Python 3. What You Will Learn ? Manage code libraries and abstract geospatial analysis techniques using Python 3. ? Explore popular code libraries that perform specific tasks for geospatial analysis. ? Utilize code libraries for data conversion, data management, web maps, and REST API creation. ? Learn techniques related to processing geospatial data in the cloud. ? Leverage features of Python 3 with geospatial databases such as PostGIS, SQL Server, and SpatiaLite. In Detail Python comes with a host of open source libraries and tools that help you work on professional geoprocessing tasks without investing in expensive tools. This book will introduce Python developers, both new and experienced, to a variety of new code libraries that have been developed to perform geospatial analysis, statistical analysis, and data management. This book will use examples and code snippets that will help explain how Python 3 differs from Python 2, and how these new code libraries can be used to solve age-old problems in geospatial analysis. You will begin by understanding what geoprocessing is and explore the tools and libraries that Python 3 offers. You will then learn to use Python code libraries to read and write geospatial data. You will then learn to perform geospatial queries within databases and learn PyQGIS to automate analysis within the QGIS mapping suite. Moving forward, you will explore the newly released ArcGIS API for Python and ArcGIS Online to perform geospatial analysis and create ArcGIS Online web maps. Further, you will deep dive into Python Geospatial web frameworks and learn to create a geospatial REST API. Style and approach The book takes a practical, example-driven approach to teach you GIS analysis and automation techniques with Python 3.＜/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。	6,313円
Machine Learning with PyTorch and Scikit-Learn Develop machine learning and deep learning models with Python【電子書籍】[ Sebastian Raschka ] 楽天Kobo電子書籍ストア	＜p＞＜b＞This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format.＜/b＞＜/p＞＜h2＞Key Features＜/h2＞＜ul＞＜li＞Learn applied machine learning with a solid foundation in theory＜/li＞＜li＞Clear, intuitive explanations take you deep into the theory and practice of Python machine learning＜/li＞＜li＞Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices＜/li＞＜/ul＞＜h2＞Book Description＜/h2＞Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.＜h2＞What you will learn＜/h2＞＜ul＞＜li＞Explore frameworks, models, and techniques for machines to learn from data＜/li＞＜li＞Use scikit-learn for machine learning and PyTorch for deep learning＜/li＞＜li＞Train machine learning classifiers on images, text, and more＜/li＞＜li＞Build and train neural networks, transformers, and boosting algorithms＜/li＞＜li＞Discover best practices for evaluating and tuning models＜/li＞＜li＞Predict continuous target outcomes using regression analysis＜/li＞＜li＞Dig deeper into textual and social media data using sentiment analysis＜/li＞＜/ul＞＜h2＞Who this book is for＜/h2＞＜p＞If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as 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。	4,734円
MicroPython Cookbook Over 110 practical recipes for programming embedded systems and microcontrollers with Python【電子書籍】[ Marwan Alsabbagh ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Learn how you can control LEDs, make music, and read sensor data using popular microcontrollers such as Adafruit Circuit Playground, ESP8266, and the BBC micro:bit＜/strong＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞Load and execute your first program with MicroPython＜/li＞＜li＞Program an IoT device to retrieve weather data using a RESTful API＜/li＞＜li＞Get to grips with integrating hardware, programming, and networking concepts with MicroPython＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞＜p＞MicroPython is an open source implementation of Python 3 that runs in embedded environments. With MicroPython, you can write clean and simple Python code to control hardware instead of using complex low-level languages like C and C++. This book guides you through all the major applications of the MicroPython platform to build and program projects that use microcontrollers.＜/p＞＜p＞The MicroPython book covers recipes that'll help you experiment with the programming environment and hardware programmed in MicroPython. You'll find tips and techniques for building a variety of objects and prototypes that can sense and respond to touch, sound, position, heat, and light. This book will take you through the uses of MicroPython with a variety of popular input devices and sensors. You'll learn techniques for handling time delays and sensor readings, and apply advanced coding techniques to create complex projects. As you advance, you'll get to deal with Internet of Things (IoT) devices and integration with other online web services. Furthermore, you'll also use MicroPython to make music with bananas and create portable multiplayer video games that incorporate sound and light animations into the game play.＜/p＞＜p＞By the end of the book, you'll have mastered tips and tricks to troubleshoot your development problems and push your MicroPython project to the next level!＜/p＞＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Execute code without any need for compiling or uploading using REPL (read-evaluate-print-loop)＜/li＞＜li＞Program and control LED matrix and NeoPixel drivers to display patterns and colors＜/li＞＜li＞Build projects that make use of light, temperature, and touch sensors＜/li＞＜li＞Configure devices to create Wi-Fi access points and use network modules to scan and connect to existing networks＜/li＞＜li＞Use Pulse Width Modulation to control DC motors and servos＜/li＞＜li＞Build an IoT device to display live weather data from the Internet at the touch of a button＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞If you want to build and program projects that use microcontrollers, this book will offer you dozens of recipes to guide you through all the major applications of the MicroPython platform. Although no knowledge of MicroPython or microcontrollers is expected, a general understanding of Python is necessary 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。	4,160円
Transformers for Natural Language Processing Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4【電子書籍】[ Denis Rothman ] 楽天Kobo電子書籍ストア	＜p＞＜b＞OpenAI's GPT-3, ChatGPT, GPT-4 and Hugging Face transformers for language tasks in one book. Get a taste of the future of transformers, including computer vision tasks and code writing and assistance. Purchase of the print or Kindle book includes a free eBook in PDF format＜/b＞＜/p＞＜h2＞Key Features＜/h2＞＜ul＞＜li＞Improve your productivity with OpenAI’s ChatGPT and GPT-4 from prompt engineering to creating and analyzing machine learning models＜/li＞＜li＞Pretrain a BERT-based model from scratch using Hugging Face＜/li＞＜li＞Fine-tune powerful transformer models, including OpenAI's GPT-3, to learn the logic of your data＜/li＞＜/ul＞＜h2＞Book Description＜/h2＞Transformers are...well...transforming the world of AI. There are many platforms and models out there, but which ones best suit your needs? Transformers for Natural Language Processing, 2nd Edition, guides you through the world of transformers, highlighting the strengths of different models and platforms, while teaching you the problem-solving skills you need to tackle model weaknesses. You'll use Hugging Face to pretrain a RoBERTa model from scratch, from building the dataset to defining the data collator to training the model. If you're looking to fine-tune a pretrained model, including GPT-3, then Transformers for Natural Language Processing, 2nd Edition, shows you how with step-by-step guides. The book investigates machine translations, speech-to-text, text-to-speech, question-answering, and many more NLP tasks. It provides techniques to solve hard language problems and may even help with fake news anxiety (read chapter 13 for more details). You'll see how cutting-edge platforms, such as OpenAI, have taken transformers beyond language into computer vision tasks and code creation using DALL-E 2, ChatGPT, and GPT-4. By the end of this book, you'll know how transformers work and how to implement them and resolve issues like an AI detective.＜h2＞What you will learn＜/h2＞＜ul＞＜li＞Discover new techniques to investigate complex language problems＜/li＞＜li＞Compare and contrast the results of GPT-3 against T5, GPT-2, and BERT-based transformers＜/li＞＜li＞Carry out sentiment analysis, text summarization, casual speech analysis, machine translations, and more using TensorFlow, PyTorch, and GPT-3＜/li＞＜li＞Find out how ViT and CLIP label images (including blurry ones!) and create images from a sentence using DALL-E＜/li＞＜li＞Learn the mechanics of advanced prompt engineering for ChatGPT and GPT-4＜/li＞＜/ul＞＜h2＞Who this book is for＜/h2＞＜p＞If you want to learn about and apply transformers to your natural language (and image) data, this book is for you. You'll need a good understanding of Python and deep learning and a basic understanding of NLP to benefit most from this book. Many platforms covered in this book provide interactive user interfaces, which allow readers with a general interest in NLP and AI to follow several chapters. And don't worry if you get stuck or have questions; this book gives you direct access to our AI/ML community to help guide you on your transformers journey!＜/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。	7,748円
Parallel and High Performance Programming with Python Unlock parallel and concurrent programming in Python using multithreading, CUDA, Pytorch and Dask. (English Edition)【電子書籍】[ Fabio Nelli ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Unleash the capabilities of Python and its libraries for solving high performance computational problems.＜/strong＞＜/p＞＜p＞＜strong＞KEY FEATURES＜/strong＞＜br /＞ ● Explores parallel programming concepts and techniques for high-performance computing.＜br /＞ ● Covers parallel algorithms, multiprocessing, distributed computing, and GPU programming.＜br /＞ ● Provides practical use of popular Python libraries/tools like NumPy, Pandas, Dask, and TensorFlow.＜/p＞＜p＞＜strong＞DESCRIPTION＜/strong＞＜br /＞ 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.＜/p＞＜p＞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.＜/p＞＜p＞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.＜/p＞＜p＞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＞＜p＞＜strong＞WHAT WILL YOU LEARN＜/strong＞＜br /＞ ● Build faster, smarter, and more efficient applications for data analysis, machine learning, and scientific computing＜br /＞ ● Implement parallel algorithms in Python＜br /＞ ● Best practices for designing, implementing, and scaling parallel programs in Python＜/p＞＜p＞＜strong＞WHO IS THIS BOOK FOR?＜/strong＞＜br /＞ This book is aimed at software developers who wish to take their careers to the next level by improving their skills and learning about concurrent and parallel programming. It is also intended for Python developers who aspire to write fast and efficient programs, and for students who wish to learn the fundamentals of parallel computing and its practical uses.＜/p＞＜p＞＜strong＞TABLE OF CONTENTS＜/strong＞＜/p＞＜ol＞＜li＞Introduction to Parallel Programming＜/li＞＜li＞Building Multithreaded Programs＜/li＞＜li＞Working with Multiprocessing and mpi4py Library＜/li＞＜li＞Asynchronous Programming with AsyncIO＜/li＞＜li＞Realizing Parallelism with Distributed Systems＜/li＞＜li＞Maximizing Performance with GPU Programming using CUDA＜/li＞＜li＞Embracing the Parallel Computing Revolution＜/li＞＜li＞Scaling Your Data Science Applications with Dask＜/li＞＜li＞Exploring the Potential of AI with Parallel Computing＜/li＞＜li＞Hands-on Applications of Parallel Computing＜/li＞＜/ol＞＜p＞＜strong＞AUTHOR BIO＜/strong＞＜br /＞＜strong＞Fabio Nelli＜/strong＞ holds a Master's Degree in Chemistry and a Bachelor's Degree in IT and Automation Engineering. He currently works at various research institutes and private companies, where he delivers educational courses on data analysis and data visualization technologies. He contributes to writing articles on the web and writes in-depth books on the subject.＜/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。	3,830円
Image Processing Masterclass with Python 50+ Solutions and Techniques Solving Complex Digital Image Processing Challenges Using Numpy, Scipy, Pytorch and Keras (English Edition)【電子書籍】[ Sandipan Dey ] 楽天Kobo電子書籍ストア	＜p＞This book starts with basic Image Processing and manipulation problems and demonstrates how to solve them with popular Python libraries and modules. It then concentrates on problems based on Geometric image transformations and problems to be solved with Image hashing.Next, the book focuses on solving problems based on Sampling, Convolution, Discrete Fourier transform, Frequency domain filtering and image restoration with deconvolution. It also aims at solving Image enhancement problemsusing different algorithms such as spatial filters and create a super resolution image using SRGAN.Finally, it explores popular facial image processing problems and solves them with Machine learning and Deep learning models using popular python ML / DL libraries.＜/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。	2,020円
Learning OpenCV 4 Computer Vision with Python 3 Get to grips with tools, techniques, and algorithms for computer vision and machine learning, 3rd Edition【電子書籍】[ Joseph Howse ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Updated for OpenCV 4 and Python 3, this book covers the latest on depth cameras, 3D tracking, augmented reality, and deep neural networks, helping you solve real-world computer vision problems with practical code＜/strong＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞Build powerful computer vision applications in concise code with OpenCV 4 and Python 3＜/li＞＜li＞Learn the fundamental concepts of image processing, object classification, and 2D and 3D tracking＜/li＞＜li＞Train, use, and understand machine learning models such as Support Vector Machines (SVMs) and neural networks＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞＜p＞Computer vision is a rapidly evolving science, encompassing diverse applications and techniques. This book will not only help those who are getting started with computer vision but also experts in the domain. You'll be able to put theory into practice by building apps with OpenCV 4 and Python 3.＜/p＞＜p＞You'll start by understanding OpenCV 4 and how to set it up with Python 3 on various platforms. Next, you'll learn how to perform basic operations such as reading, writing, manipulating, and displaying still images, videos, and camera feeds. From taking you through image processing, video analysis, and depth estimation and segmentation, to helping you gain practice by building a GUI app, this book ensures you'll have opportunities for hands-on activities. Next, you'll tackle two popular challenges: face detection and face recognition. You'll also learn about object classification and machine learning concepts, which will enable you to create and use object detectors and classifiers, and even track objects in movies or video camera feed. Later, you'll develop your skills in 3D tracking and augmented reality. Finally, you'll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age.＜/p＞＜p＞By the end of this book, you'll have the skills you need to execute real-world computer vision projects.＜/p＞＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Install and familiarize yourself with OpenCV 4's Python 3 bindings＜/li＞＜li＞Understand image processing and video analysis basics＜/li＞＜li＞Use a depth camera to distinguish foreground and background regions＜/li＞＜li＞Detect and identify objects, and track their motion in videos＜/li＞＜li＞Train and use your own models to match images and classify objects＜/li＞＜li＞Detect and recognize faces, and classify their gender and age＜/li＞＜li＞Build an augmented reality application to track an image in 3D＜/li＞＜li＞Work with machine learning models, including SVMs, artificial neural networks (ANNs), and deep neural networks (DNNs)＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞If you are interested in learning computer vision, machine learning, and OpenCV in the context of practical real-world applications, then this book is for you. This OpenCV book will also be useful for anyone getting started with computer vision as well as experts who want to stay up-to-date with OpenCV 4 and Python 3. Although no prior knowledge of image processing, computer vision or machine learning is required, familiarity with basic Python programming 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。	5,739円
Architecture Patterns with Python Enabling Test-Driven Development, Domain-Driven Design, and Event-Driven Microservices【電子書籍】[ Harry Percival ] 楽天Kobo電子書籍ストア	＜p＞As Python continues to grow in popularity, projects are becoming larger and more complex. Many Python developers are taking an interest in high-level software design patterns such as hexagonal/clean architecture, event-driven architecture, and the strategic patterns prescribed by domain-driven design (DDD). But translating those patterns into Python isn’t always straightforward.＜/p＞＜p＞With this hands-on guide, Harry Percival and Bob Gregory from MADE.com introduce proven architectural design patterns to help Python developers manage application complexityーand get the most value out of their test suites.＜/p＞＜p＞Each pattern is illustrated with concrete examples in beautiful, idiomatic Python, avoiding some of the verbosity of Java and C# syntax. Patterns include:＜/p＞＜ul＞＜li＞Dependency inversion and its links to ports and adapters (hexagonal/clean architecture)＜/li＞＜li＞Domain-driven design’s distinction between Entities, Value Objects, and Aggregates＜/li＞＜li＞Repository and Unit of Work patterns for persistent storage＜/li＞＜li＞Events, commands, and the message bus＜/li＞＜li＞Command-query responsibility segregation (CQRS)＜/li＞＜li＞Event-driven architecture and reactive microservices＜/li＞＜/ul＞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。	3,890円
Mastering Geospatial Development with QGIS 3.x An in-depth guide to becoming proficient in spatial data analysis using QGIS 3.4 and 3.6 with Python, 3rd Edition【電子書籍】[ Shammunul Islam ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Go beyond the basics and unleash the full power of QGIS 3.4 and 3.6 with practical, step-by-step examples＜/strong＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞One-stop solution to all of your GIS needs＜/li＞＜li＞Master QGIS by learning about database integration, and geoprocessing tools＜/li＞＜li＞Learn about the new and updated Processing toolbox and perform spatial analysis＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞＜p＞QGIS is an open source solution to GIS and widely used by GIS professionals all over the world. It is the leading alternative to proprietary GIS software. Although QGIS is described as intuitive, it is also, by default, complex. Knowing which tools to use and how to apply them is essential to producing valuable deliverables on time.＜/p＞＜p＞Starting with a refresher on the QGIS basics and getting you acquainted with the latest QGIS 3.6 updates, this book will take you all the way through to teaching you how to create a spatial database and a GeoPackage. Next, you will learn how to style raster and vector data by choosing and managing different colors. The book will then focus on processing raster and vector data. You will be then taught advanced applications, such as creating and editing vector data. Along with that, you will also learn about the newly updated Processing Toolbox, which will help you develop the advanced data visualizations. The book will then explain to you the graphic modeler, how to create QGIS plugins with PyQGIS, and how to integrate Python analysis scripts with QGIS.＜/p＞＜p＞By the end of the book, you will understand how to work with all aspects of QGIS and will be ready to use it for any type of GIS work.＜/p＞＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Create and manage a spatial database＜/li＞＜li＞Get to know advanced techniques to style GIS data＜/li＞＜li＞Prepare both vector and raster data for processing＜/li＞＜li＞Add heat maps, live layer effects, and labels to your maps＜/li＞＜li＞Master LAStools and GRASS integration with the Processing Toolbox＜/li＞＜li＞Edit and repair topological data errors＜/li＞＜li＞Automate workflows with batch processing and the QGIS Graphical Modeler＜/li＞＜li＞Integrate Python scripting into your data processing workflows＜/li＞＜li＞Develop your own QGIS plugins＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞If you are a GIS professional, a consultant, a student, or perhaps a fast learner who wants to go beyond the basics of QGIS, then this book is for you. It will prepare you to realize the full potential of QGIS.＜/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。	4,160円
Hands-On Explainable AI (XAI) with Python Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps【電子書籍】[ Denis Rothman ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Resolve the black box models in your AI applications to make them fair, trustworthy, and secure. Familiarize yourself with the basic principles and tools to deploy Explainable AI (XAI) into your apps and reporting interfaces.＜/strong＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞Learn explainable AI tools and techniques to process trustworthy AI results＜/li＞＜li＞Understand how to detect, handle, and avoid common issues with AI ethics and bias＜/li＞＜li＞Integrate fair AI into popular apps and reporting tools to deliver business value using Python and associated tools＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞＜p＞Effectively translating AI insights to business stakeholders requires careful planning, design, and visualization choices. Describing the problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex.＜/p＞＜p＞Hands-On Explainable AI (XAI) with Python will see you work with specific hands-on machine learning Python projects that are strategically arranged to enhance your grasp on AI results analysis. You will be building models, interpreting results with visualizations, and integrating XAI reporting tools and different applications.＜/p＞＜p＞You will build XAI solutions in Python, TensorFlow 2, Google Cloud's XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source XAI tools for Python that can be used throughout the machine learning project life cycle.＜/p＞＜p＞You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting the visualization of machine learning models into user explainable interfaces.＜/p＞＜p＞By the end of this AI book, you will possess an in-depth understanding of the core concepts of XAI.＜/p＞＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Plan for XAI through the different stages of the machine learning life cycle＜/li＞＜li＞Estimate the strengths and weaknesses of popular open-source XAI applications＜/li＞＜li＞Examine how to detect and handle bias issues in machine learning data＜/li＞＜li＞Review ethics considerations and tools to address common problems in machine learning data＜/li＞＜li＞Share XAI design and visualization best practices＜/li＞＜li＞Integrate explainable AI results using Python models＜/li＞＜li＞Use XAI toolkits for Python in machine learning life cycles to solve business problems＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞This book is not an introduction to Python programming or machine learning concepts. You must have some foundational knowledge and/or experience with machine learning libraries such as scikit-learn to make the most out of this book.＜/p＞＜p＞Some of the potential readers of this book include:＜/p＞＜ol＞＜li＞Professionals who already use Python for as data science, machine learning, research, and analysis＜/li＞＜li＞Data analysts and data scientists who want an introduction into explainable AI tools and techniques＜/li＞＜li＞AI Project managers who must face the contractual and legal obligations of AI Explainability for the acceptance phase of their applications＜/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。	5,309円
Beginner's Guide to Streamlit with Python Build Web-Based Data and Machine Learning Applications【電子書籍】[ Sujay Raghavendra ] 楽天Kobo電子書籍ストア	＜p＞This book will teach you the basics of Streamlit, a Python-based application framework used to build interactive dashboards and machine learning web apps. Streamlit reduces development time for web-based application prototypes of data and machine learning models. As you’ll see, Streamlit helps develop data-enhanced analytics, build dynamic user experiences, and showcases data for data science and machine learning models.＜/p＞＜p＞＜em＞Beginner's Guide to Streamlit with Python＜/em＞ begins with the basics of Streamlit by demonstrating how to build a basic application and advances to visualization techniques and their features. Next, it covers the various aspects of a typical Streamlit web application, and explains how to manage flow control and status elements. You’ll also explore performance optimization techniques necessary for data modules in a Streamlit application. Following this, you’ll see how to deploy Streamlit applications on various platforms. The book concludes with a few prototype natural language processing apps with computer vision implemented using Streamlit.＜/p＞＜p＞After reading this book, you will understand the concepts, functionalities, and performance of Streamlit, and be able to develop dynamic Streamlit web-based data and machine learning applications of your own.＜/p＞＜p＞＜strong＞What You Will Learn＜/strong＞＜/p＞＜ul＞＜li＞How to start developing web applications using Streamlit＜/li＞＜li＞What are Streamlit's components＜/li＞＜li＞Media elements in Streamlit＜/li＞＜li＞How to visualize data using various interactive and dynamic Python libraries＜/li＞＜li＞How to implement models in Streamlit web applications＜/li＞＜/ul＞＜p＞＜strong＞Who This Book Is For＜/strong＞＜/p＞＜p＞Professionals working in data science and machine learning domains who want to showcase and deploy their work in a web application with no prior knowledge of 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
Hands-On Gradient Boosting with XGBoost and scikit-learn Perform accessible machine learning and extreme gradient boosting with Python【電子書籍】[ Corey Wade ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Get to grips with building robust XGBoost models using Python and scikit-learn for deployment＜/strong＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞Get up and running with machine learning and understand how to boost models with XGBoost in no time＜/li＞＜li＞Build real-world machine learning pipelines and fine-tune hyperparameters to achieve optimal results＜/li＞＜li＞Discover tips and tricks and gain innovative insights from XGBoost Kaggle winners＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞＜p＞XGBoost is an industry-proven, open-source software library that provides a gradient boosting framework for scaling billions of data points quickly and efficiently.＜/p＞＜p＞The book introduces machine learning and XGBoost in scikit-learn before building up to the theory behind gradient boosting. You'll cover decision trees and analyze bagging in the machine learning context, learning hyperparameters that extend to XGBoost along the way. You'll build gradient boosting models from scratch and extend gradient boosting to big data while recognizing speed limitations using timers. Details in XGBoost are explored with a focus on speed enhancements and deriving parameters mathematically. With the help of detailed case studies, you'll practice building and fine-tuning XGBoost classifiers and regressors using scikit-learn and the original Python API. You'll leverage XGBoost hyperparameters to improve scores, correct missing values, scale imbalanced datasets, and fine-tune alternative base learners. Finally, you'll apply advanced XGBoost techniques like building non-correlated ensembles, stacking models, and preparing models for industry deployment using sparse matrices, customized transformers, and pipelines.＜/p＞＜p＞By the end of the book, you'll be able to build high-performing machine learning models using XGBoost with minimal errors and maximum speed.＜/p＞＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Build gradient boosting models from scratch＜/li＞＜li＞Develop XGBoost regressors and classifiers with accuracy and speed＜/li＞＜li＞Analyze variance and bias in terms of fine-tuning XGBoost hyperparameters＜/li＞＜li＞Automatically correct missing values and scale imbalanced data＜/li＞＜li＞Apply alternative base learners like dart, linear models, and XGBoost random forests＜/li＞＜li＞Customize transformers and pipelines to deploy XGBoost models＜/li＞＜li＞Build non-correlated ensembles and stack XGBoost models to increase accuracy＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞This book is for data science professionals and enthusiasts, data analysts, and developers who want to build fast and accurate machine learning models that scale with big data. Proficiency in Python, along with a basic understanding of linear algebra, will help you to get the most out of 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。	6,170円
Learn OpenCV 4.5 with Python 3.7 by Examples Implement Computer Vision Algorithms Provided by OpenCV with Python for Image Processing, Object Detection and Machine Learning【電子書籍】[ James Chen ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞What This Book is About＜/strong＞＜/p＞＜p＞When you searched for this book, you have already known the importance of the OpenCV/Python in the fields of computer vision, image processing and machine learning. This book begins with step-by-step instructions of installation as well as a simple Hello World, then gets into the OpenCV Basics, Image Processing, Object Detection and finally Machine Learning.＜/p＞＜p＞＜strong＞Key Features＜/strong＞＜/p＞＜p＞Example for every topic, all the source codes are available in Github.＜/p＞＜p＞Line by line explanation of the source codes.＜/p＞＜p＞Focus mainly on implementation of algorithms, rather than mathematical theories.＜/p＞＜p＞＜strong＞Whom This Book Is For＜/strong＞＜/p＞＜p＞This book is for people with a variety of computer programming levels, from those with very limited knowledge of computer vision to the experienced ones. The readers do not need to have previous experiences of Python/OpenCV. No matter you are a beginner or experienced programmer, as long as you want to learn OpenCV with Python, you will benefit from this book.＜/p＞＜p＞＜strong＞Table of Contents＜/strong＞＜/p＞＜p＞＜strong＞1. Introduction＜/strong＞＜/p＞＜ol＞＜li＞What Is OpenCV＜/li＞＜li＞Whom This Book Is For＜/li＞＜li＞How to Get the Source Codes for This Book＜/li＞＜li＞Hardware Requirements and Software Versions＜/li＞＜li＞How This Book Is Organized＜/li＞＜/ol＞＜p＞＜strong＞2. Installation＜/strong＞＜/p＞＜ol＞＜li＞Install on Windows＜/li＞＜li＞Install Python on Ubuntu＜/li＞＜li＞Configure PyCharm and Install OpenCV＜/li＞＜/ol＞＜p＞＜strong＞3. OpenCV Basics＜/strong＞＜/p＞＜ol＞＜li＞Load and Display Images＜/li＞＜li＞Load and Display Videos＜/li＞＜li＞Display Webcam＜/li＞＜li＞Play Youtube Video＜/li＞＜li＞Image Fundamentals＜/li＞＜li＞Draw Shapes＜/li＞＜li＞Draw Texts＜/li＞＜li＞Draw an OpenCV-like Icon＜/li＞＜/ol＞＜p＞＜strong＞4. User Interaction＜/strong＞＜/p＞＜ol＞＜li＞Mouse Operations＜/li＞＜li＞Draw Circles with Mouse＜/li＞＜li＞Draw Polygon with Mouse＜/li＞＜li＞Crop an Image with Mouse＜/li＞＜li＞Input Values with Trackbars＜/li＞＜/ol＞＜p＞＜strong＞5. Image Processing＜/strong＞＜/p＞＜ol＞＜li＞Change Color Spaces＜/li＞＜li＞Resize, Crop and Rotate an Image＜/li＞＜li＞Adjust Contrast and Brightness of an Image＜/li＞＜li＞Adjust Hue, Saturation and Value＜/li＞＜li＞Blend Image＜/li＞＜li＞Bitwise Operation＜/li＞＜li＞Warp Image＜/li＞＜li＞Blur Image＜/li＞＜li＞Histogram＜/li＞＜/ol＞＜p＞＜strong＞6. Object Detection＜/strong＞＜/p＞＜ol＞＜li＞Canny Edge Detection＜/li＞＜li＞Dilation and Erosion＜/li＞＜li＞Shape Detection＜/li＞＜li＞Color Detection＜/li＞＜li＞Text Recognition with Tesseract＜/li＞＜li＞Human Detection＜/li＞＜li＞Face and Eye Detection＜/li＞＜li＞Remove Background＜/li＞＜li＞Blur Background＜/li＞＜/ol＞＜p＞＜strong＞7. Machine Learning＜/strong＞＜/p＞＜ol＞＜li＞K-Means Clustering＜/li＞＜li＞K-Nearest Neighbors＜/li＞＜li＞Support Vector Machine＜/li＞＜li＞Artificial Neural Network (ANN)＜/li＞＜/ol＞＜p＞＜strong＞About the Author＜/strong＞＜/p＞＜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。	2,280円
Natural Language Processing with Python and spaCy A Practical Introduction【電子書籍】[ Yuli Vasiliev ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞An introduction to natural language processing with Python using spaCy, a leading Python natural language processing library.＜/strong＞＜/p＞＜p＞＜em＞Natural Language Processing with Python and spaCy＜/em＞ will show you how to create NLP applications like chatbots, text-condensing scripts, and order-processing tools quickly and easily. You'll learn how to leverage the spaCy library to extract meaning from text intelligently; how to determine the relationships between words in a sentence (syntactic dependency parsing); identify nouns, verbs, and other parts of speech (part-of-speech tagging); and sort proper nouns into categories like people, organizations, and locations (named entity recognizing). You'll even learn how to transform statements into questions to keep a conversation going.＜/p＞＜p＞You'll also learn how to:＜br /＞＜strong＞?＜/strong＞ Work with word vectors to mathematically find words with similar meanings (Chapter 5)＜br /＞＜strong＞?＜/strong＞ Identify patterns within data using spaCy's built-in displaCy visualizer (Chapter 7)＜br /＞＜strong＞?＜/strong＞ Automatically extract keywords from user input and store them in a relational database (Chapter 9)＜br /＞＜strong＞?＜/strong＞ Deploy a chatbot app to interact with users over the internet (Chapter 11)＜/p＞＜p＞"Try This" sections in each chapter encourage you to practice what you've learned by expanding the book's example scripts to handle a wider range of inputs, add error handling, and build professional-quality applications.＜/p＞＜p＞By the end of the book, you'll be creating your own NLP applications with Python and spaCy.＜/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。	4,855円
The Book of Dash Build Dashboards with Python and Plotly【電子書籍】[ Christian Mayer ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Create stunning interactive dashboard applications in Python with the Dash visualization and data analysis tool. Build interfaces that make sense of your data, and make it pretty.＜/strong＞＜/p＞＜p＞A swift and practical introduction to building interactive data visualization apps in Python, known as dashboards. You’ve seen dashboards before; think election result visualizations you can update in real time, or population maps you can filter by demographic. With the Python Dash library you’ll create analytic dashboards that present data in effective, usable, elegant ways in just a few lines of code.＜/p＞＜p＞The book is fast-paced and caters to those entirely new to dashboards. It will talk you through the necessary software, then get straight into building the dashboards themselves. You’ll learn the basic format of a Dash app in a Twitter analysis dashboard that tracks numbers of likes over time. You’ll then build up skills through three more sophisticated projects. The first compares world data in three areas: volume of internet usage, percentage of parliament seats held by women, and CO2 emissions; the second is a financial portfolio dashboard that models your investments; and the third is visualizesmachine learning algorithms. The final chapter sets you up with some useful final skills, like debugging your code and applying color themes.＜/p＞＜p＞In this book you will:＜/p＞＜p＞Create and run your first Dash apps＜/p＞＜p＞Use the pandas library to manipulate and analyze social media and API data＜/p＞＜p＞Create a variety of stunning and effective charts using Plotly＜/p＞＜p＞Learn to use bar charts, chloropleth maps, contour plots, and more＜/p＞＜p＞Examine and build on existing apps written by the pros＜/p＞＜p＞Dash combines several technologies to get you building dashboards quickly and efficiently. This book will do the same.＜/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。	3,781円
Modern Computer Vision with PyTorch A practical roadmap from deep learning fundamentals to advanced applications and Generative AI【電子書籍】[ V Kishore Ayyadevara ] 楽天Kobo電子書籍ストア	＜p＞＜b＞The definitive computer vision book is back, featuring the latest neural network architectures and an exploration of foundation and diffusion models Purchase of the print or Kindle book includes a free eBook in PDF format＜/b＞＜/p＞＜h2＞Key Features＜/h2＞＜ul＞＜li＞Understand the inner workings of various neural network architectures and their implementation, including image classification, object detection, segmentation, generative adversarial networks, transformers, and diffusion models＜/li＞＜li＞Build solutions for real-world computer vision problems using PyTorch＜/li＞＜li＞All the code files are available on GitHub and can be run on Google Colab＜/li＞＜/ul＞＜h2＞Book Description＜/h2＞Whether you are a beginner or are looking to progress in your computer vision career, this book guides you through the fundamentals of neural networks (NNs) and PyTorch and how to implement state-of-the-art architectures for real-world tasks. The second edition of Modern Computer Vision with PyTorch is fully updated to explain and provide practical examples of the latest multimodal models, CLIP, and Stable Diffusion. You’ll discover best practices for working with images, tweaking hyperparameters, and moving models into production. As you progress, you'll implement various use cases for facial keypoint recognition, multi-object detection, segmentation, and human pose detection. This book provides a solid foundation in image generation as you explore different GAN architectures. You’ll leverage transformer-based architectures like ViT, TrOCR, BLIP2, and LayoutLM to perform various real-world tasks and build a diffusion model from scratch. Additionally, you’ll utilize foundation models' capabilities to perform zero-shot object detection and image segmentation. Finally, you’ll learn best practices for deploying a model to production. By the end of this deep learning book, you'll confidently leverage modern NN architectures to solve real-world computer vision problems.＜h2＞What you will learn＜/h2＞＜ul＞＜li＞Get to grips with various transformer-based architectures for computer vision, CLIP, Segment-Anything, and Stable Diffusion, and test their applications, such as in-painting and pose transfer＜/li＞＜li＞Combine CV with NLP to perform OCR, key-value extraction from document images, visual question-answering, and generative AI tasks＜/li＞＜li＞Implement multi-object detection and segmentation＜/li＞＜li＞Leverage foundation models to perform object detection and segmentation without any training data points＜/li＞＜li＞Learn best practices for moving a model to production＜/li＞＜/ul＞＜h2＞Who this book is for＜/h2＞＜p＞This book is for beginners to PyTorch and intermediate-level machine learning practitioners who want to learn computer vision techniques using deep learning and PyTorch. It's useful for those just getting started with neural networks, as it will enable readers to learn from real-world use cases accompanied by notebooks on GitHub. Basic knowledge of the Python programming language and ML is all you need to get started with this book. For more experienced computer vision scientists, this book takes you through more advanced models in the latter part of the 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。	5,251円
3D Image Reconstruction for CT and PET A Practical Guide with Python【電子書籍】[ Daniele Panetta ] 楽天Kobo電子書籍ストア	＜p＞This is a practical guide to tomographic image reconstruction with projection data, with strong focus on Computed Tomography (CT) and Positron Emission Tomography (PET). Classic methods such as FBP, ART, SIRT, MLEM and OSEM are presented with modern and compact notation, with the main goal of guiding the reader from the comprehension of the mathematical background through a fast-route to real practice and computer implementation of the algorithms. Accompanied by example data sets, real ready-to-run Python toolsets and scripts and an overview the latest research in the field, this guide will be invaluable for graduate students and early-career researchers and scientists in medical physics and biomedical engineering who are beginners in the field of image reconstruction.＜/p＞＜ul＞＜li＞A top-down guide from theory to practical implementation of PET and CT reconstruction methods, without sacrificing the rigor of mathematical background＜/li＞＜li＞Accompanied by Python source code snippets, suggested exercises, and supplementary ready-to-run examples for readers to download from the CRC Press website＜/li＞＜li＞Ideal for those willing to move their first steps on the real practice of image reconstruction, with modern scientific programming language and toolsets＜/li＞＜/ul＞＜p＞＜strong＞Daniele Panetta＜/strong＞ is a researcher at the Institute of Clinical Physiology of the Italian National Research Council (CNR-IFC) in Pisa. He earned his MSc degree in Physics in 2004 and specialisation diploma in Health Physics in 2008, both at the University of Pisa. From 2005 to 2007, he worked at the Department of Physics "E. Fermi" of the University of Pisa in the field of tomographic image reconstruction for small animal imaging micro-CT instrumentation. His current research at CNR-IFC has as its goal the identification of novel PET/CT imaging biomarkers for cardiovascular and metabolic diseases. In the field micro-CT imaging, his interests cover applications of three-dimensional morphometry of biosamples and scaffolds for regenerative medicine. He acts as reviewer for scientific journals in the field of Medical Imaging: Physics in Medicine and Biology, Medical Physics, Physica Medica, and others. Since 2012, he is adjunct professor in Medical Physics at the University of Pisa.＜/p＞＜p＞＜strong＞Niccol? Camarlingh****i＜/strong＞ is a researcher at the University of Pisa. He obtained his MSc in Physics in 2007 and his PhD in Applied Physics in 2012. He has been working in the field of Medical Physics since 2008 and his main research fields are medical image analysis and image reconstruction. He is involved in the development of clinical, pre-clinical PET and hadron therapy monitoring scanners. At the time of writing this book he was a lecturer at University of Pisa, teaching courses of life-sciences and medical physics laboratory. He regularly acts as a referee for the following journals: Medical Physics, Physics in Medicine and Biology, Transactions on Medical Imaging, Computers in Biology and Medicine, Physica Medica, EURASIP Journal on Image and Video Processing, Journal of Biomedical and Health Informatics.＜/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。	4,557円
OpenCV 4 with Python Blueprints Build creative computer vision projects with the latest version of OpenCV 4 and Python 3, 2nd Edition【電子書籍】[ Dr. Menua Gevorgyan ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Get to grips with traditional computer vision algorithms and deep learning approaches, and build real-world applications with OpenCV and other machine learning frameworks＜/strong＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞Understand how to capture high-quality image data, detect and track objects, and process the actions of animals or humans＜/li＞＜li＞Implement your learning in different areas of computer vision＜/li＞＜li＞Explore advanced concepts in OpenCV such as machine learning, artificial neural network, and augmented reality＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞＜p＞OpenCV is a native cross-platform C++ library for computer vision, machine learning, and image processing. It is increasingly being adopted in Python for development. This book will get you hands-on with a wide range of intermediate to advanced projects using the latest version of the framework and language, OpenCV 4 and Python 3.8, instead of only covering the core concepts of OpenCV in theoretical lessons. This updated second edition will guide you through working on independent hands-on projects that focus on essential OpenCV concepts such as image processing, object detection, image manipulation, object tracking, and 3D scene reconstruction, in addition to statistical learning and neural networks.＜/p＞＜p＞You'll begin with concepts such as image filters, Kinect depth sensor, and feature matching. As you advance, you'll not only get hands-on with reconstructing and visualizing a scene in 3D but also learn to track visually salient objects. The book will help you further build on your skills by demonstrating how to recognize traffic signs and emotions on faces. Later, you'll understand how to align images, and detect and track objects using neural networks.＜/p＞＜p＞By the end of this OpenCV Python book, you'll have gained hands-on experience and become proficient at developing advanced computer vision apps according to specific business needs.＜/p＞＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Generate real-time visual effects using filters and image manipulation techniques such as dodging and burning＜/li＞＜li＞Recognize hand gestures in real-time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor＜/li＞＜li＞Learn feature extraction and feature matching to track arbitrary objects of interest＜/li＞＜li＞Reconstruct a 3D real-world scene using 2D camera motion and camera reprojection techniques＜/li＞＜li＞Detect faces using a cascade classifier and identify emotions in human faces using multilayer perceptrons＜/li＞＜li＞Classify, localize, and detect objects with deep neural networks＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞This book is for intermediate-level OpenCV users who are looking to enhance their skills by developing advanced applications. Familiarity with OpenCV concepts and Python libraries, and basic knowledge of the Python programming language are 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。	4,877円
Building Data Science Applications with FastAPI Develop, manage, and deploy efficient machine learning applications with Python【電子書籍】[ Francois Voron ] 楽天Kobo電子書籍ストア	＜p＞Get well-versed with FastAPI features and best practices for testing, monitoring, and deployment to run high-quality and robust data science applications Key Features ? Cover the concepts of the FastAPI framework, including aspects relating to asynchronous programming, type hinting, and dependency injection ? Develop efficient RESTful APIs for data science with modern Python ? Build, test, and deploy high performing data science and machine learning systems with FastAPI Book Description FastAPI is a web framework for building APIs with Python 3.6 and its later versions based on standard Python-type hints. With this book, you'll be able to create fast and reliable data science API backends using practical examples. This book starts with the basics of the FastAPI framework and associated modern Python programming language concepts. You'll be taken through all the aspects of the framework, including its powerful dependency injection system and how you can use it to communicate with databases, implement authentication and integrate machine learning models. Later, you'll cover best practices relating to testing and deployment to run a high-quality and robust application. You'll also be introduced to the extensive ecosystem of Python data science packages. As you progress, you'll learn how to build data science applications in Python using FastAPI. The book also demonstrates how to develop fast and efficient machine learning prediction backends and test them to achieve the best performance. Finally, you'll see how to implement a real-time face detection system using WebSockets and a web browser as a client. By the end of this FastAPI book, you'll have not only learned how to implement Python in data science projects but also how to maintain and design them to meet high programming standards with the help of FastAPI. What you will learn ? Explore the basics of modern Python and async I/O programming ? Get to grips with basic and advanced concepts of the FastAPI framework ? Implement a FastAPI dependency to efficiently run a machine learning model ? Integrate a simple face detection algorithm in a FastAPI backend ? Integrate common Python data science libraries in a web backend ? Deploy a performant and reliable web backend for a data science application Who this book is for This Python data science book is for data scientists and software developers interested in gaining knowledge of FastAPI and its ecosystem to build data science applications. Basic knowledge of data science and machine learning concepts and how to apply them in Python 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。	6,313円