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商品	説明	価格
洋書 Python: 4 Books in 1: Beginner's Guide + Tips and Tricks + Best Practices + Advanced Guide to Programming Code with Python (Python, Java, JavaScript, ... Programming, Computer Programming) (Volume 4) Glomarket	* We ship internationally, so do not use a package forwarding service. We cannot ship to a package forwarding company address because of the Japanese customs regulation. If it is shipped and customs office does not let the package go, we do not make a refund. 【注意事項】 * 特に注意してください。 * ・個人ではない法人・団体名義での購入はできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。・お名前にカタカナが入っている場合法人である可能性が高いため当店システムから自動保留します。カタカナで記載が必要な場合はカタカナ変わりローマ字で記載してください。・お名前またはご住所が法人・団体名義（XX株式会社等）、商店名などを含めている場合、または電話番号が個人のものではない場合、税関から法人名義でみなされますのでご注意ください。・転送サービス会社への発送もできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 * ・注文後品切れや価格変動でキャンセルされる場合がございますので予めご了承願います。・当店でご購入された商品は、原則として、「個人輸入」としての取り扱いになり、すべてニュージャージからお客様のもとへ直送されます。・ご注文後、30営業日以内(通常2~3週間)に配送手続きをいたします。配送作業完了後、2週間程度でのお届けとなります。・まれに商品入荷状況や国際情勢、運送、通関事情により、お届けが2ヶ月までかかる場合がありますのでお急ぎの場合は注文をお控えください。・個人輸入される商品は、すべてご注文者自身の「個人使用・個人消費」が前提となりますので、ご注文された商品を第三者へ譲渡・転売することは法律で禁止されております。・関税・消費税が課税される場合があります。詳細はこちらをご確認下さい。PC販売説明文	11,007円
Practical Data Science Programming for Medical Datasets Analysis and Prediction with Python GUI【電子書籍】[ Vivian Siahaan ] 楽天Kobo電子書籍ストア	＜p＞In this book, you will implement two data science projects using Scikit-Learn, Scipy, and other libraries with Python GUI.＜/p＞＜p＞In chapter 1, you will learn how to use Scikit-Learn, SVM, NumPy, Pandas, and other libraries to perform how to predict early stage diabetes using Early Stage Diabetes Risk Prediction Dataset (viviansiahaan.blogspot.com/2023/06/step-by-step-project-based-tutorials.html). This dataset contains the sign and symptom data of newly diabetic or would be diabetic patient. This has been collected using direct questionnaires from the patients of Sylhet Diabetes Hospital in Sylhet, Bangladesh and approved by a doctor. The dataset consist of total 15 features and one target variable named class. Age: Age in years ranging from (20years to 65 years); Gender: Male / Female; Polyuria: Yes / No; Polydipsia: Yes/ No; Sudden weight loss: Yes/ No; Weakness: Yes/ No; Polyphagia: Yes/ No; Genital Thrush: Yes/ No; Visual blurring: Yes/ No; Itching: Yes/ No; Irritability: Yes/No; Delayed healing: Yes/ No; Partial Paresis: Yes/ No; Muscle stiffness: yes/ No; Alopecia: Yes/ No; Obesity: Yes/ No; This dataset contains the sign and symptpom data of newly diabetic or would be diabetic patient. This has been collected using direct questionnaires from the patients of Sylhet Diabetes Hospital in Sylhet, Bangladesh and approved by a doctor. You will develop a GUI using PyQt5 to plot distribution of features, feature importance, cross validation score, and prediced values versus true values. The machine learning models used in this project are Adaboost, Random Forest, Gradient Boosting, Logistic Regression, and Support Vector Machine.＜/p＞＜p＞In chapter 2, you will learn how to use Scikit-Learn, NumPy, Pandas, and other libraries to perform how to analyze and predict breast cancer using Breast Cancer Prediction Dataset (viviansiahaan.blogspot.com/2023/06/step-by-step-project-based-tutorials.html). Worldwide, breast cancer is the most common type of cancer in women and the second highest in terms of mortality rates.Diagnosis of breast cancer is performed when an abnormal lump is found (from self-examination or x-ray) or a tiny speck of calcium is seen (on an x-ray). After a suspicious lump is found, the doctor will conduct a diagnosis to determine whether it is cancerous and, if so, whether it has spread to other parts of the body. This breast cancer dataset was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg. You will develop a GUI using PyQt5 to plot distribution of features, pairwise relationship, test scores, prediced values versus true values, confusion matrix, and decision boundary. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, and Support Vector Machine.＜/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。	2,111円
Deep Learning Projects Using TensorFlow 2 Neural Network Development with Python and Keras【電子書籍】[ Vinita Silaparasetty ] 楽天Kobo電子書籍ストア	＜p＞Work through engaging and practical deep learning projects using TensorFlow 2.0. Using a hands-on approach, the projects in this book will lead new programmers through the basics into developing practical deep learning applications.＜/p＞＜p＞Deep learning is quickly integrating itself into the technology landscape. Its applications range from applicable data science to deep fakes and so much more. It is crucial for aspiring data scientists or those who want to enter the field of AI to understand deep learning concepts.＜/p＞＜p＞The best way to learn is by doing. You'll develop a working knowledge of not only TensorFlow, but also related technologies such as Python and Keras. You'll also work with Neural Networks and other deep learning concepts. By the end of the book, you'll have a collection of unique projects that you can add to your GitHub profiles and expand on for professional application.＜/p＞＜p＞＜strong＞What You'll Learn＜/strong＞＜/p＞＜ul＞＜li＞＜p＞Grasp the basic process of neural networks through projects, such as creating music＜/p＞＜/li＞＜li＞＜p＞Restore and colorize black and white images with deep learning processes＜/p＞＜/li＞＜/ul＞＜p＞＜strong＞Who This Book Is For＜/strong＞＜/p＞＜p＞Beginners new to TensorFlow and Python.＜/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。	9,116円
Generative AI with Python and PyTorch Hands-on projects and cutting-edge techniques using generative adversarial networks and LLMs【電子書籍】[ Joseph Babcock ] 楽天Kobo電子書籍ストア	＜p＞＜b＞Master generative AI techniques to create images and text using Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), LSTMs, and Large Language Models (LLMs)＜/b＞＜/p＞＜h2＞Key Features＜/h2＞＜ul＞＜li＞Implement real-world applications of LLMs and generative AI＜/li＞＜li＞Use PEFT and LoRA to fine-tune models with a subset of the model weights to speed up training＜/li＞＜li＞Enhance your LLM toolbox with Retrieval Augmented Generation (RAG) techniques, LangChain, and LlamaIndex＜/li＞＜li＞Purchase of the print or Kindle book includes a free eBook in PDF format＜/li＞＜/ul＞＜h2＞Book Description＜/h2＞Become an expert in generative AI through practical projects to leverage cutting-edge models for Natural Language Processing (NLP) and computer vision. Generative AI with Python and PyTorch, Second Edition, is your comprehensive guide to creating advanced AI applications. Leveraging Python, this book provides a detailed exploration of the latest generative AI technologies. From NLP to image generation, this edition dives into practical applications and the underlying theories that enable these technologies. By integrating the latest advancements and applications of large language models, this book prepares you to design and implement powerful AI systems that transform data into actionable insights. You’ll build your LLM toolbox by learning about various models, tools, and techniques, including GPT-4, LangChain, RLHF, LoRA, and retrieval augmented generation. This deep learning book shows you how to generate images and apply styler transfer using GANs, before implementing CLIP and diffusion models. Whether you’re creating dynamic content or developing complex AI-driven solutions, Generative AI with Python and PyTorch, Second Edition, equips you with the knowledge to use Python and AI to their full potential.＜h2＞What you will learn＜/h2＞＜ul＞＜li＞Understand the core concepts behind large language models and their capabilities＜/li＞＜li＞Craft effective prompts using chain-of-thought, ReAct, and prompt query language to guide LLMs toward your desired outputs＜/li＞＜li＞Learn how attention and transformers have changed NLP＜/li＞＜li＞Optimize your diffusion models by combining them with VAEs＜/li＞＜li＞Build several text generation pipelines based on LSTMs and LLMs＜/li＞＜li＞Leverage the power of open-source LLMs, such as Llama and Mistral, for various tasks＜/li＞＜/ul＞＜h2＞Who this book is for＜/h2＞＜p＞This book is for data scientists, machine learning engineers, and software developers seeking practical skills in building generative AI systems. A basic understanding of math and statistics and experience with Python coding is required.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	3,874円
Generative AI with LangChain Build large language model (LLM) apps with Python, ChatGPT, and other LLMs【電子書籍】[ Ben Auffarth ] 楽天Kobo電子書籍ストア	＜p＞＜b＞2024 Edition ? Get to grips with the LangChain framework to develop production-ready applications, including agents and personal assistants. The 2024 edition features updated code examples and an improved GitHub repository. Purchase of the print or Kindle book includes a free PDF eBook.＜/b＞＜/p＞＜h2＞Key Features＜/h2＞＜ul＞＜li＞Learn how to leverage LangChain to work around LLMs’ inherent weaknesses＜/li＞＜li＞Delve into LLMs with LangChain and explore their fundamentals, ethical dimensions, and application challenges＜/li＞＜li＞Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality＜/li＞＜/ul＞＜h2＞Book Description＜/h2＞ChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Gemini. It demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis ? illustrating the expansive utility of LLMs in real-world applications. Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.＜h2＞What you will learn＜/h2＞＜ul＞＜li＞Create LLM apps with LangChain, like question-answering systems and chatbots＜/li＞＜li＞Understand transformer models and attention mechanisms＜/li＞＜li＞Automate data analysis and visualization using pandas and Python＜/li＞＜li＞Grasp prompt engineering to improve performance＜/li＞＜li＞Fine-tune LLMs and get to know the tools to unleash their power＜/li＞＜li＞Deploy LLMs as a service with LangChain and apply evaluation strategies＜/li＞＜li＞Privately interact with documents using open-source LLMs to prevent data leaks＜/li＞＜/ul＞＜h2＞Who this book is for＜/h2＞＜p＞The book is for developers, researchers, and anyone interested in learning more about LangChain. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs using LangChain. Basic knowledge of Python is a prerequisite, while prior exposure to machine learning will help you follow along more easily.＜/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 Data Science with Python and Dask Glomarket	* We ship internationally, so do not use a package forwarding service. We cannot ship to a package forwarding company address because of the Japanese customs regulation. If it is shipped and customs office does not let the package go, we do not make a refund. 【注意事項】 * 特に注意してください。 * ・個人ではない法人・団体名義での購入はできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。・お名前にカタカナが入っている場合法人である可能性が高いため当店システムから自動保留します。カタカナで記載が必要な場合はカタカナ変わりローマ字で記載してください。・お名前またはご住所が法人・団体名義（XX株式会社等）、商店名などを含めている場合、または電話番号が個人のものではない場合、税関から法人名義でみなされますのでご注意ください。・転送サービス会社への発送もできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 * ・注文後品切れや価格変動でキャンセルされる場合がございますので予めご了承願います。・当店でご購入された商品は、原則として、「個人輸入」としての取り扱いになり、すべてニュージャージからお客様のもとへ直送されます。・ご注文後、30営業日以内(通常2~3週間)に配送手続きをいたします。配送作業完了後、2週間程度でのお届けとなります。・まれに商品入荷状況や国際情勢、運送、通関事情により、お届けが2ヶ月までかかる場合がありますのでお急ぎの場合は注文をお控えください。・個人輸入される商品は、すべてご注文者自身の「個人使用・個人消費」が前提となりますので、ご注文された商品を第三者へ譲渡・転売することは法律で禁止されております。・関税・消費税が課税される場合があります。詳細はこちらをご確認下さい。PC販売説明文	11,589円
Production of Biofuels and Chemicals with Pyrolysis【電子書籍】楽天Kobo電子書籍ストア	＜p＞This book presents a collection of studies on state-of-art techniques for converting biomass to chemical products by means of pyrolysis, which are widely applicable to the valorization of biomass. In addition to discussing the fundamentals and mechanisms for producing bio-oils, chemicals, gases and biochar using pyrolysis, it outlines key reaction parameters and reactor configurations for various types of biomass. Written by leading experts and providing a broad range of perspectives on cutting-edge applications, the book is a comprehensive reference guide for academic researchers and industrial engineers in the fields of natural renewable materials, biorefinery of lignocellulose, biofuels, and environmental engineering, and a valuable resource for university students in the fields of chemical engineering, material science and environmental engineering.＜/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。	24,309円
Advanced Python Programming Build high performance, concurrent, and multi-threaded apps with Python using proven design patterns【電子書籍】[ Dr. Gabriele Lanaro ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Create distributed applications with clever design patterns to solve complex problems＜/strong＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞Set up and run distributed algorithms on a cluster using Dask and PySpark＜/li＞＜li＞Master skills to accurately implement concurrency in your code＜/li＞＜li＞Gain practical experience of Python design patterns with real-world examples＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞＜p＞This Learning Path shows you how to leverage the power of both native and third-party Python libraries for building robust and responsive applications. You will learn about profilers and reactive programming, concurrency and parallelism, as well as tools for making your apps quick and efficient. You will discover how to write code for parallel architectures using TensorFlow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. With the knowledge of how Python design patterns work, you will be able to clone objects, secure interfaces, dynamically choose algorithms, and accomplish much more in high performance computing.＜/p＞＜p＞By the end of this Learning Path, you will have the skills and confidence to build engaging models that quickly offer efficient solutions to your problems.＜/p＞＜p＞This Learning Path includes content from the following Packt products:＜/p＞＜ul＞＜li＞Python High Performance - Second Edition by Gabriele Lanaro＜/li＞＜li＞Mastering Concurrency in Python by Quan Nguyen＜/li＞＜li＞Mastering Python Design Patterns by Sakis Kasampalis＜/li＞＜/ul＞＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Use NumPy and pandas to import and manipulate datasets＜/li＞＜li＞Achieve native performance with Cython and Numba＜/li＞＜li＞Write asynchronous code using asyncio and RxPy＜/li＞＜li＞Design highly scalable programs with application scaffolding＜/li＞＜li＞Explore abstract methods to maintain data consistency＜/li＞＜li＞Clone objects using the prototype pattern＜/li＞＜li＞Use the adapter pattern to make incompatible interfaces compatible＜/li＞＜li＞Employ the strategy pattern to dynamically choose an algorithm＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞This Learning Path is specially designed for Python developers who want to build high-performance applications and learn about single core and multi-core programming, distributed concurrency, and Python design patterns. Some experience with Python programming language will help you get the most out of this Learning Path.＜/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。	5,022円
Getting Started with Python for the Internet of Things Leverage the full potential of Python to prototype and build IoT projects using the Raspberry Pi【電子書籍】[ Tim Cox ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Build clever, collaborative, and powerful automation systems with the Raspberry Pi and Python.＜/strong＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞Create your own Pi-Rover or Pi-Hexipod robots＜/li＞＜li＞Develop practical applications in Python using Raspberry Pi＜/li＞＜li＞Build your own Jarvis, a highly advanced computerized AI＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞＜p＞This Learning Path takes you on a journey in the world of robotics and teaches you all that you can achieve with Raspberry Pi and Python.＜/p＞＜p＞It teaches you to harness the power of Python with the Raspberry Pi 3 and the Raspberry Pi zero to build superlative automation systems that can transform your business. You will learn to create text classifiers, predict sentiment in words, and develop applications with the Tkinter library. Things will get more interesting when you build a human face detection and recognition system and a home automation system in Python, where different appliances are controlled using the Raspberry Pi. With such diverse robotics projects, you'll grasp the basics of robotics and its functions, and understand the integration of robotics with the IoT environment.＜/p＞＜p＞By the end of this Learning Path, you will have covered everything from configuring a robotic controller, to creating a self-driven robotic vehicle using Python.＜/p＞＜ul＞＜li＞Raspberry Pi 3 Cookbook for Python Programmers - Third Edition by Tim Cox, Dr. Steven Lawrence Fernandes＜/li＞＜li＞Python Programming with Raspberry Pi by Sai Yamanoor, Srihari Yamanoor＜/li＞＜li＞Python Robotics Projects by Prof. Diwakar Vaish＜/li＞＜/ul＞＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Build text classifiers and predict sentiment in words with the Tkinter library＜/li＞＜li＞Develop human face detection and recognition systems＜/li＞＜li＞Create a neural network module for optical character recognition＜/li＞＜li＞Build a mobile robot using the Raspberry Pi as a controller＜/li＞＜li＞Understand how to interface sensors, actuators, and LED displays work＜/li＞＜li＞Apply machine learning techniques to your models＜/li＞＜li＞Interface your robots with Bluetooth＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞This Learning Path is specially designed for Python developers who want to take their skills to the next level by creating robots that can enhance people’s lives. Familiarity with Python and electronics will aid understanding the concepts in this Learning Path.＜/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。	5,022円
Practical Docker with Python Build, Release and Distribute your Python App with Docker【電子書籍】[ Sathyajith Bhat ] 楽天Kobo電子書籍ストア	＜p＞Learn the key differences between containers and virtual machines. Adopting a project based approach, this book introduces you to a simple Python application to be developed and containerized with Docker.＜/p＞＜p＞After an introduction to Containers and Docker you'll be guided through Docker installation and configuration. You'll also learn basic functions and commands used in Docker by running a simple container using Docker commands.＜/p＞＜p＞The book then moves on to developing a Python based Messaging Bot using required libraries and virtual environment where you'll add Docker Volumes to your project, ensuring your container data is safe.＜/p＞＜p＞You'll create a database container and link your project to it and finally, bring up the Bot-associated database all at once with Docker Compose.＜/p＞＜p＞＜strong＞What You'll Learn＜/strong＞＜/p＞＜ul＞＜li＞＜p＞Build, run, and distribute Docker containers＜/p＞＜/li＞＜li＞＜p＞Develop a Python App and containerize it＜/p＞＜/li＞＜li＞＜p＞Use Dockerfile to run the Python App＜/p＞＜/li＞＜li＞＜p＞Define and run multi-container applications with Docker Compose＜/p＞＜/li＞＜li＞＜p＞Work with persisting data generated by and used by Docker containers＜/p＞＜/li＞＜/ul＞＜p＞＜strong＞Who This Book Is For＜/strong＞＜/p＞＜p＞Intermediate developers/DevOps practitioners who are looking to improve their build and release workflow by containerizing 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。	4,618円
Modern Data Architectures with Python A practical guide to building and deploying data pipelines, data warehouses, and data lakes with Python【電子書籍】[ Brian Lipp ] 楽天Kobo電子書籍ストア	＜p＞＜b＞Build scalable and reliable data ecosystems using Data Mesh, Databricks Spark, and Kafka＜/b＞＜/p＞＜h2＞Key Features＜/h2＞＜ul＞＜li＞Develop modern data skills used in emerging technologies＜/li＞＜li＞Learn pragmatic design methodologies such as Data Mesh and data lakehouses＜/li＞＜li＞Gain a deeper understanding of data governance＜/li＞＜li＞Purchase of the print or Kindle book includes a free PDF eBook＜/li＞＜/ul＞＜h2＞Book Description＜/h2＞Modern Data Architectures with Python will teach you how to seamlessly incorporate your machine learning and data science work streams into your open data platforms. You’ll learn how to take your data and create open lakehouses that work with any technology using tried-and-true techniques, including the medallion architecture and Delta Lake. Starting with the fundamentals, this book will help you build pipelines on Databricks, an open data platform, using SQL and Python. You’ll gain an understanding of notebooks and applications written in Python using standard software engineering tools such as git, pre-commit, Jenkins, and Github. Next, you’ll delve into streaming and batch-based data processing using Apache Spark and Confluent Kafka. As you advance, you’ll learn how to deploy your resources using infrastructure as code and how to automate your workflows and code development. Since any data platform's ability to handle and work with AI and ML is a vital component, you’ll also explore the basics of ML and how to work with modern MLOps tooling. Finally, you’ll get hands-on experience with Apache Spark, one of the key data technologies in today’s market. By the end of this book, you’ll have amassed a wealth of practical and theoretical knowledge to build, manage, orchestrate, and architect your data ecosystems.＜h2＞What you will learn＜/h2＞＜ul＞＜li＞Understand data patterns including delta architecture＜/li＞＜li＞Discover how to increase performance with Spark internals＜/li＞＜li＞Find out how to design critical data diagrams＜/li＞＜li＞Explore MLOps with tools such as AutoML and MLflow＜/li＞＜li＞Get to grips with building data products in a data mesh＜/li＞＜li＞Discover data governance and build confidence in your data＜/li＞＜li＞Introduce data visualizations and dashboards into your data practice＜/li＞＜/ul＞＜h2＞Who this book is for＜/h2＞＜p＞This book is for developers, analytics engineers, and managers looking to further develop a data ecosystem within their organization. While they’re not prerequisites, basic knowledge of Python and prior experience with data will help you to read and follow along with the examples.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	4,304円
Advanced Forecasting with Python With State-of-the-Art-Models Including LSTMs, Facebook’s Prophet, and Amazon’s DeepAR【電子書籍】[ Joos Korstanje ] 楽天Kobo電子書籍ストア	＜p＞Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook’s open-source Prophet model, and Amazon’s DeepAR model.＜/p＞＜p＞Rather than focus on a specific set of models, this book presents an exhaustive overview of all the techniques relevant to practitioners of forecasting. It begins by explaining the different categories of models that are relevant for forecasting in a high-level language. Next, it covers univariate and multivariate time series models followed by advanced machine learning and deep learning models. It concludes with reflections on model selection such as benchmark scores vs. understandability of models vs. compute time, and automated retraining and updating of models.＜/p＞＜p＞Each of the models presented in this book is covered in depth, with an intuitive simple explanation ofthe model, a mathematical transcription of the idea, and Python code that applies the model to an example data set.＜/p＞＜p＞Reading this book will add a competitive edge to your current forecasting skillset. The book is also adapted to those who have recently started working on forecasting tasks and are looking for an exhaustive book that allows them to start with traditional models and gradually move into more and more advanced models.＜/p＞＜p＞＜strong＞What You Will Learn＜/strong＞＜/p＞＜ul＞＜li＞Carry out forecasting with Python＜/li＞＜li＞Mathematically and intuitively understand traditional forecasting models and state-of-the-art machine learning techniques＜/li＞＜li＞Gain the basics of forecasting and machine learning, including evaluation of models, cross-validation, and back testing＜/li＞＜li＞Select the right model for the right use case＜/li＞＜/ul＞＜p＞Who This Book Is For＜/p＞＜p＞The advanced nature of the later chapters makes the book relevant for appliedexperts working in the domain of forecasting, as the models covered have been published only recently. Experts working in the domain will want to update their skills as traditional models are regularly being outperformed by newer models.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	6,685円
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。	2,020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。	5,739円
Python Mastery A Step-by-Step Course to Master Web Development, Data Analysis, and Machine Learning with Python【電子書籍】[ Lena Neill ] 楽天Kobo電子書籍ストア	＜p＞Do you want to learn how to code from scratch?＜/p＞＜p＞Python is a simple and powerful programming language that you can use for various purposes, such as web development, data analysis, and machine learning. Its code is easy to read and write, and it avoids unnecessary complexity.＜/p＞＜p＞Python has many advantages over other languages, such as C++ or Java. It lets you write more with less code, thanks to its implicit and dynamic typing. It also checks the types of your variables, which are case-sensitive. Python does not need special characters to end statements, unlike other languages.＜/p＞＜p＞Python uses indentation to structure your code, and you need to use a colon to end statements. Python also allows you to assign multiple variables on the same line.＜/p＞＜p＞This book covers many important topics, such as:＜/p＞＜p＞The 7 main features of Python Why you should choose Python The best Python web app frameworks and their benefits The different data types in Python How to use conditional statements and loops How Python is used for Machine Learning and why it is popular And much more!＜/p＞＜p＞Python programming uses many keywords that are mostly in English. Knowing these keywords means that you understand the basics of Python programming. Before you learn these keywords, you need to master the fundamental concepts of Python, which will help you learn every aspect of this language.＜/p＞＜p＞This book is designed to help you start your Python programming journey, giving you a solid foundation to build on. Get your copy today and start coding!＜/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。	1,686円
Data-Centric Machine Learning with Python The ultimate guide to engineering and deploying high-quality models based on good data【電子書籍】[ Jonas Christensen ] 楽天Kobo電子書籍ストア	＜p＞＜b＞Join the data-centric revolution and master the concepts, techniques, and algorithms shaping the future of AI and ML development, using Python＜/b＞＜/p＞＜h2＞Key Features＜/h2＞＜ul＞＜li＞Grasp the principles of data centricity and apply them to real-world scenarios＜/li＞＜li＞Gain experience with quality data collection, labeling, and synthetic data creation using Python＜/li＞＜li＞Develop essential skills for building reliable, responsible, and ethical machine learning solutions＜/li＞＜li＞Purchase of the print or Kindle book includes a free PDF eBook＜/li＞＜/ul＞＜h2＞Book Description＜/h2＞In the rapidly advancing data-driven world where data quality is pivotal to the success of machine learning and artificial intelligence projects, this critically timed guide provides a rare, end-to-end overview of data-centric machine learning (DCML), along with hands-on applications of technical and non-technical approaches to generating deeper and more accurate datasets. This book will help you understand what data-centric ML/AI is and how it can help you to realize the potential of ‘small data’. Delving into the building blocks of data-centric ML/AI, you’ll explore the human aspects of data labeling, tackle ambiguity in labeling, and understand the role of synthetic data. From strategies to improve data collection to techniques for refining and augmenting datasets, you’ll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, you’ll get a roadmap for implementing data-centric ML/AI in diverse applications in Python. By the end of this book, you’ll have developed a profound understanding of data-centric ML/AI and the proficiency to seamlessly integrate common data-centric approaches in the model development lifecycle to unlock the full potential of your machine learning projects by prioritizing data quality and reliability.＜h2＞What you will learn＜/h2＞＜ul＞＜li＞Understand the impact of input data quality compared to model selection and tuning＜/li＞＜li＞Recognize the crucial role of subject-matter experts in effective model development＜/li＞＜li＞Implement data cleaning, labeling, and augmentation best practices＜/li＞＜li＞Explore common synthetic data generation techniques and their applications＜/li＞＜li＞Apply synthetic data generation techniques using common Python packages＜/li＞＜li＞Detect and mitigate bias in a dataset using best-practice techniques＜/li＞＜li＞Understand the importance of reliability, responsibility, and ethical considerations in ML/AI＜/li＞＜/ul＞＜h2＞Who this book is for＜/h2＞＜p＞This book is for data science professionals and machine learning enthusiasts looking to understand the concept of data-centricity, its benefits over a model-centric approach, and the practical application of a best-practice data-centric approach in their work. This book is also for other data professionals and senior leaders who want to explore the tools and techniques to improve data quality and create opportunities for small data ML/AI in their organizations.＜/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
GOLD PRICE ANALYSIS AND FORECASTING USING MACHINE LEARNING WITH PYTHON【電子書籍】[ Vivian Siahaan ] 楽天Kobo電子書籍ストア	＜p＞The challenge of this project is to accurately predict the future adjusted closing price of Gold ETF across a given period of time in the future. The problem is a regression problem, because the output value which is the adjusted closing price in this project is continuous value.＜/p＞＜p＞Data for this study is collected from November 18th 2011 to January 1st 2019 from various sources. The data has 1718 rows in total and 80 columns in total. Data for attributes, such as Oil Price, Standard and Poor’s (S&P) 500 index, Dow Jones Index US Bond rates (10 years), Euro USD exchange rates, prices of precious metals Silver and Platinum and other metals such as Palladium and Rhodium, prices of US Dollar Index, Eldorado Gold Corporation and Gold Miners ETF were gathered.＜/p＞＜p＞The dataset has 1718 rows in total and 80 columns in total. Data for attributes, such as Oil Price, Standard and Poor’s (S&P) 500 index, Dow Jones Index US Bond rates (10 years), Euro USD exchange rates, prices of precious metals Silver and Platinum and other metals such as Palladium and Rhodium, prices of US Dollar Index, Eldorado Gold Corporation and Gold Miners ETF were gathered.＜/p＞＜p＞To perform forecasting based on regression adjusted closing price of gold, you will use: Linear Regression, Random Forest regression, Decision Tree regression, Support Vector Machine regression, Na?ve Bayes regression, K-Nearest Neighbor regression, Adaboost regression, Gradient Boosting regression, Extreme Gradient Boosting regression, Light Gradient Boosting regression, Catboost regression, and MLP regression.＜/p＞＜p＞The machine learning models used predict gold daily returns as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, XGB classifier, MLP classifier, and Extra Trees classifier. Finally, you will plot boundary decision, distribution of features, feature importance, predicted values versus true values, confusion matrix, learning curve, performance of the model, and scalability of the model.＜/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。	893円
Math for Programmers: 3D Graphics, Machine Learning, and Simulations with Python MATH FOR PROGRAMMERS [ Paul Orland ] 楽天ブックス	MATH FOR PROGRAMMERS Paul Orland MANNING PUBN2021 Paperback English ISBN：9781617295355 洋書 Computers & Science（コンピューター＆科学） Computers	14,784円
Hands-On Docker for Microservices with Python Design, deploy, and operate a complex system with multiple microservices using Docker and Kubernetes【電子書籍】[ Jaime Buelta ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞A step-by-step guide to building microservices using Python and Docker, along with managing and orchestrating them with Kubernetes＜/strong＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞Learn to use Docker containers to create, operate, and deploy your microservices＜/li＞＜li＞Create workflows to manage independent deployments on coordinating services using CI and GitOps through GitHub, Travis CI, and Flux＜/li＞＜li＞Develop a REST microservice in Python using the Flask framework and Postgres database＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞＜p＞Microservices architecture helps create complex systems with multiple, interconnected services that can be maintained by independent teams working in parallel. This book guides you on how to develop these complex systems with the help of containers.＜/p＞＜p＞You'll start by learning to design an efficient strategy for migrating a legacy monolithic system to microservices. You'll build a RESTful microservice with Python and learn how to encapsulate the code for the services into a container using Docker. While developing the services, you'll understand how to use tools such as GitHub and Travis CI to ensure continuous delivery (CD) and continuous integration (CI). As the systems become complex and grow in size, you'll be introduced to Kubernetes and explore how to orchestrate a system of containers while managing multiple services. Next, you'll configure Kubernetes clusters for production-ready environments and secure them for reliable deployments. In the concluding chapters, you'll learn how to detect and debug critical problems with the help of logs and metrics. Finally, you'll discover a variety of strategies for working with multiple teams dealing with different microservices for effective collaboration.＜/p＞＜p＞By the end of this book, you'll be able to build production-grade microservices as well as orchestrate a complex system of services using containers.＜/p＞＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Discover how to design, test, and operate scalable microservices＜/li＞＜li＞Coordinate and deploy different services using Kubernetes＜/li＞＜li＞Use Docker to construct scalable and manageable applications with microservices＜/li＞＜li＞Understand how to monitor a complete system to ensure early detection of problems＜/li＞＜li＞Become well versed with migrating from an existing monolithic system to a microservice one＜/li＞＜li＞Use load balancing to ensure seamless operation between the old monolith and the new service＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞This book is for developers, engineers, or software architects who are trying to move away from traditional approaches for building complex multi-service systems by adopting microservices and containers. Although familiarity with Python programming is assumed, no prior knowledge of Docker is required.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちらをクリックして下さい。 ※このページからは注文できません。	4,160円
Network Science with Python and NetworkX Quick Start Guide Explore and visualize network data effectively【電子書籍】[ Edward L. Platt ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Manipulate and analyze network data with the power of Python and NetworkX＜/strong＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞Understand the terminology and basic concepts of network science＜/li＞＜li＞Leverage the power of Python and NetworkX to represent data as a network＜/li＞＜li＞Apply common techniques for working with network data of varying sizes＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞＜p＞NetworkX is a leading free and open source package used for network science with the Python programming language. NetworkX can track properties of individuals and relationships, find communities, analyze resilience, detect key network locations, and perform a wide range of important tasks. With the recent release of version 2, NetworkX has been updated to be more powerful and easy to use.＜/p＞＜p＞If you're a data scientist, engineer, or computational social scientist, this book will guide you in using the Python programming language to gain insights into real-world networks. Starting with the fundamentals, you'll be introduced to the core concepts of network science, along with examples that use real-world data and Python code. This book will introduce you to theoretical concepts such as scale-free and small-world networks, centrality measures, and agent-based modeling. You'll also be able to look for scale-free networks in real data and visualize a network using circular, directed, and shell layouts.＜/p＞＜p＞By the end of this book, you'll be able to choose appropriate network representations, use NetworkX to build and characterize networks, and uncover insights while working with real-world systems.＜/p＞＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Use Python and NetworkX to analyze the properties of individuals and relationships＜/li＞＜li＞Encode data in network nodes and edges using NetworkX＜/li＞＜li＞Manipulate, store, and summarize data in network nodes and edges＜/li＞＜li＞Visualize a network using circular, directed and shell layouts＜/li＞＜li＞Find out how simulating behavior on networks can give insights into real-world problems＜/li＞＜li＞Understand the ongoing impact of network science on society, and its ethical considerations＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞If you are a programmer or data scientist who wants to manipulate and analyze network data in Python, this book is perfect for you. Although prior knowledge of network science is not necessary, some Python programming experience will help you understand the concepts covered in the book easily.＜/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。	3,156円
Hands-On Penetration Testing with Python Enhance your ethical hacking skills to build automated and intelligent systems【電子書籍】[ Furqan Khan ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞Implement defensive techniques in your ecosystem successfully with Python＜/strong＞＜/p＞＜h4＞Key Features＜/h4＞＜ul＞＜li＞Identify and expose vulnerabilities in your infrastructure with Python＜/li＞＜li＞Learn custom exploit development .＜/li＞＜li＞Make robust and powerful cybersecurity tools with Python＜/li＞＜/ul＞＜h4＞Book Description＜/h4＞＜p＞With the current technological and infrastructural shift, penetration testing is no longer a process-oriented activity. Modern-day penetration testing demands lots of automation and innovation; the only language that dominates all its peers is Python. Given the huge number of tools written in Python, and its popularity in the penetration testing space, this language has always been the first choice for penetration testers.＜/p＞＜p＞Hands-On Penetration Testing with Python walks you through advanced Python programming constructs. Once you are familiar with the core concepts, you'll explore the advanced uses of Python in the domain of penetration testing and optimization. You'll then move on to understanding how Python, data science, and the cybersecurity ecosystem communicate with one another. In the concluding chapters, you'll study exploit development, reverse engineering, and cybersecurity use cases that can be automated with Python.＜/p＞＜p＞By the end of this book, you'll have acquired adequate skills to leverage Python as a helpful tool to pentest and secure infrastructure, while also creating your own custom exploits.＜/p＞＜h4＞What you will learn＜/h4＞＜ul＞＜li＞Get to grips with Custom vulnerability scanner development＜/li＞＜li＞Familiarize yourself with web application scanning automation and exploit development＜/li＞＜li＞Walk through day-to-day cybersecurity scenarios that can be automated with Python＜/li＞＜li＞Discover enterprise-or organization-specific use cases and threat-hunting automation＜/li＞＜li＞Understand reverse engineering, fuzzing, buffer overflows , key-logger development, and exploit development for buffer overflows.＜/li＞＜li＞Understand web scraping in Python and use it for processing web responses＜/li＞＜li＞Explore Security Operations Centre (SOC) use cases＜/li＞＜li＞Get to understand Data Science, Python, and cybersecurity all under one hood＜/li＞＜/ul＞＜h4＞Who this book is for＜/h4＞＜p＞If you are a security consultant , developer or a cyber security enthusiast with little or no knowledge of Python and want in-depth insight into how the pen-testing ecosystem and python combine to create offensive tools , exploits , automate cyber security use-cases and much more then this book is for you. Hands-On Penetration Testing with Python guides you through the advanced uses of Python for cybersecurity and pen-testing, helping you to better understand security loopholes within your infrastructure .＜/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
Cracking Codes with Python An Introduction to Building and Breaking Ciphers【電子書籍】[ Al Sweigart ] 楽天Kobo電子書籍ストア	＜p＞Learn how to program in Python while making and breaking ciphersーalgorithms used to create and send secret messages!＜/p＞＜p＞After a crash course in Python programming basics, you’ll learn to make, test, and hack programs that encrypt text with classical ciphers like the transposition cipher and Vigen?re cipher. You’ll begin with simple programs for the reverse and Caesar ciphers and then work your way up to public key cryptography, the type of encryption used to secure today’s online transactions, including digital signatures, email, and Bitcoin.＜/p＞＜p＞Each program includes the full code and a line-by-line explanation of how things work. By the end of the book, you’ll have learned how to code in Python and you’ll have the clever programs to prove it!＜/p＞＜p＞You’ll also learn how to:＜/p＞＜p＞- Combine loops, variables, and flow control statements into real working programs＜br /＞ - Use dictionary files to instantly detect whether decrypted messages are valid English or gibberish＜br /＞ - Create test programs to make sure that your code encrypts and decrypts correctly＜br /＞ - Code (and hack!) a working example of the affine cipher, which uses modular arithmetic to encrypt a message＜br /＞ - Break ciphers with techniques such as brute-force and frequency analysis＜/p＞＜p＞There’s no better way to learn to code than to play with real programs. ＜em＞Cracking Codes with Python＜/em＞ makes the learning fun!＜/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。	3,235円
Data Structures and Algorithms with Python【電子書籍】[ Ken Schmidt ] 楽天Kobo電子書籍ストア	＜p＞Dive into the world of data structures and algorithms with this comprehensive guide tailored for Python programmers. "Data Structures and Algorithms with Python: Elevating Your Coding Skills with Data Structures and Algorithms" is an essential resource for anyone looking to deepen their understanding of computational methods and enhance their problem-solving skills.＜/p＞＜p＞Starting with the basics of Python and progressing through to complex data structures and algorithmic techniques, this book offers a structured approach to learning. It not only demystifies the theoretical aspects of the topics but also provides practical implementations and real-world applications, ensuring that readers gain hands-on experience.＜/p＞＜p＞＜strong＞Key Features:＜/strong＞＜/p＞＜ul＞＜li＞＜strong＞Comprehensive Coverage:＜/strong＞ From basic data types and control structures in Python to advanced topics like graph algorithms and dynamic programming, this book covers a wide range of fundamental and complex concepts.＜/li＞＜li＞＜strong＞Hands-On Learning:＜/strong＞ Each chapter includes Python code examples, exercises, and challenges that encourage active participation and reinforce learning.＜/li＞＜li＞＜strong＞Complexity Analysis:＜/strong＞ Understand how to evaluate the efficiency of algorithms and data structures using Big O notation, enabling you to select the most appropriate tool for your coding projects.＜/li＞＜li＞＜strong＞Real-World Applications:＜/strong＞ Discover how data structures and algorithms are applied in various fields such as web development, data analysis, machine learning, and more, through case studies and projects.＜/li＞＜li＞＜strong＞Resources for Further Learning:＜/strong＞ An extensive list of resources, including books, online courses, and tutorials, is provided to support continued learning and exploration beyond the book.＜/li＞＜/ul＞＜p＞"Data Structures and Algorithms with Python" is more than just a book; it's a pathway to enhancing your coding expertise, improving your analytical thinking, and solving complex computational problems with confidence. Embark on this educational journey to unlock new possibilities in your programming career or hobbyist endeavors.＜/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。	800円
Learn Enough Python to Be Dangerous Software Development, Flask Web Apps, and Beginning Data Science with Python【電子書籍】[ Michael Hartl ] 楽天Kobo電子書籍ストア	＜p＞＜strong＞All You Need to Know, and Nothing You Don't, to Solve Real Problems with Python＜/strong＞＜/p＞＜p＞Python is one of the most popular programming languages in the world, used for everything from shell scripts to web development to data science. As a result, Python is a great language to learn, but you don't need to learn "everything" to get started, just how to use it efficiently to solve real problems. In ＜em＞＜strong＞Learn Enough Python to Be Dangerous＜/strong＞＜/em＞, renowned instructor Michael Hartl teaches the specific concepts, skills, and approaches you need to be professionally productive.＜/p＞＜p＞Even if you've never programmed before, Hartl helps you quickly build technical sophistication and master the lore you need to succeed. Hartl introduces Python both as a general-purpose language and as a specialist tool for web development and data science, presenting focused examples and exercises that help you internalize what matters, without wasting time on details pros don't care about. Soon, it'll be like you were born knowing this stuff--and you'll be suddenly, seriously ＜em＞dangerous＜/em＞.＜/p＞＜p＞＜em＞Learn enough about . . .＜/em＞＜/p＞＜ul＞＜li＞Applying core Python concepts with the interactive interpreter and command line＜/li＞＜li＞Writing object-oriented code with Python's native objects＜/li＞＜li＞Developing and publishing self-contained Python packages＜/li＞＜li＞Using elegant, powerful functional programming techniques, including Python comprehensions＜/li＞＜li＞Building new objects, and extending them via Test-Driven Development (TDD)＜/li＞＜li＞Leveraging Python's exceptional shell scripting capabilities＜/li＞＜li＞Creating and deploying a full web app, using routes, layouts, templates, and forms＜/li＞＜li＞Getting started with data-science tools for numerical computations, data visualization, data analysis, and machine learning＜/li＞＜li＞Mastering concrete and informal skills every developer needs＜/li＞＜/ul＞＜p＞Michael Hartl's Learn Enough Series includes books and video courses that focus on the most important parts of each subject, so you don't have to learn everything to get started--you just have to learn enough to be dangerous and solve technical problems yourself.＜/p＞＜p＞＜em＞Like this book?＜/em＞ Don't miss Michael Hartl's companion video tutorial, ＜em＞Learn Enough Python to Be Dangerous LiveLessons＜/em＞.＜/p＞＜p＞＜em＞Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.＜/em＞＜/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。	5,132円
Ultimate ChatGPT Handbook for Enterprises Transform the Enterprise Landscape by Leveraging AI Capabilities, Prompt Engineering, GPT Solution-Cycles of ChatGPT with Python and Java【電子書籍】[ Dr. Harald Gunia ] 楽天Kobo電子書籍ストア	＜p＞Empowering the Global Workforce with ChatGPT Expertise.Book Description“Ultimate ChatGPT Handbook for Enterprises” is your indispensable resource for navigating the transformative world of ChatGPT within the enterprise domain. It provides a deep dive into ChatGPT's evolution, capabilities, and its potential to democratize technology interactions through natural language.Throughout its chapters, you'll embark on a journey that spans from comprehending the lineage of GPT models to mastering advanced prompt engineering techniques. It will help you take a step into a futuristic enterprise landscape where ChatGPT seamlessly collaborates with human intelligence, fundamentally transforming daily work routines across various enterprise roles.The latter chapters will help you attain proficiency in managing GPT projects and discovering the agile and iterative approach to GPT solution life cycles using real-world scenarios. You will also be introduced to practical GPT implementation frameworks for both Python and Java.This book offers practical insights and applicable skills, fostering informed dialogue and active participation in the ongoing enterprise AI revolution.Table of Contents1. ??From GPT-1 to ChatGPT-4: The Evolution Towards Generative AI2. CapabilityGPT An Enterprise AI-Capability Framework for ChatGPT3. The Impact of ChatGPT on the Enterprise4. Architecture Patterns enabled by GPT-Models5. Advanced GPT Prompt Engineering Techniques6. Designing Prompt-based Intelligent Assistants7. Mastery of GPT-Projects8. LangChain: GPT Implementation Framework for Python9. predictive-powers: GPT Implementation Framework for JavaAppendix AAppendix B＜/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。	4,213円
【中古】【未使用・未開封品】Beginning Sensor Networks with XBee, Raspberry Pi, and Arduino: Sensing the World with Python and MicroPython AJIMURA-SHOP	【中古】【未使用・未開封品】Beginning Sensor Networks with XBee, Raspberry Pi, and Arduino: Sensing the World with Python and MicroPython【メーカー名】【メーカー型番】【ブランド名】Apress MySQL, Internet & Networking, Paperback Store, Amazon Student ポイント還元(洋書), Amazonアプリキャンペーン対象商品(洋書), 洋書（アダルト除く） Bell, Charles: Author【商品説明】Beginning Sensor Networks with XBee, Raspberry Pi, and Arduino: Sensing the World with Python and MicroPython【注意】こちらは輸入品となります。当店では初期不良に限り、商品到着から7日間は返品を受付けております。こちらは当店海外ショップで一般の方から買取した未使用・未開封品です。買取した為、中古扱いとしております。他モールとの併売品の為、完売の際はご連絡致しますのでご了承ください。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、当店海外倉庫から当店日本倉庫を経由しお届けしますので10〜30営業日程度でのお届けとなります。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、メールにてお問合せお願い致します。営業時間　月〜金　10:00〜17:00お客様都合によるご注文後のキャンセル・返品はお受けしておりませんのでご了承下さい。	25,314円
AIRLINE PASSENGER SATISFACTION Analysis and Prediction Using Machine Learning and Deep Learning with Python【電子書籍】[ Vivian Siahaan ] 楽天Kobo電子書籍ストア	＜p＞In the project "Airline Passenger Satisfaction Analysis and Prediction Using Machine Learning and Deep Learning with Python," the aim was to analyze and predict passenger satisfaction in the airline industry. The project began with an extensive data exploration phase, wherein the dataset containing various features related to passenger experiences was thoroughly examined. The dataset was then preprocessed, ensuring data cleanliness and preparing it for further analysis.＜/p＞＜p＞One of the initial steps involved understanding the distribution of categorized features within the dataset. By visualizing the distribution of these features, insights were gained into the prevalence of different categories, providing a preliminary understanding of passenger preferences and experiences.＜/p＞＜p＞For the prediction aspect, machine learning models were employed, and a Grid Search approach was implemented to fine-tune hyperparameters and optimize model performance. This process allowed the identification of the best-performing model configuration, enhancing the accuracy of passenger satisfaction predictions. The models used are Logistic Regression, Support Vector Machines, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting.＜/p＞＜p＞Going beyond traditional machine learning, a Deep Learning approach was introduced using an Artificial Neural Network (ANN). This model, designed to capture intricate patterns and relationships within the data, showcased the potential of deep learning for improving predictive accuracy.＜/p＞＜p＞The evaluation of both machine learning and deep learning models was centered around key metrics. The accuracy score was a primary indicator of model performance, reflecting the ratio of correctly predicted passenger satisfaction outcomes. Additionally, the Classification Report provided a comprehensive overview of precision, recall, and F1-score for each category, shedding light on the model's ability to classify passenger satisfaction levels accurately.＜/p＞＜p＞Visualizing the results played a pivotal role in the project. The plotted Training and Validation Accuracy and Loss graphs offered insights into the convergence and generalization capabilities of the models. These visualizations helped in understanding potential overfitting or underfitting issues and guided the fine-tuning process.＜/p＞＜p＞To assess the models' predictive performance, a Confusion Matrix was constructed. This matrix presented a clear breakdown of correct and incorrect predictions, facilitating an understanding of where the model excelled and where it struggled. Furthermore, scatter plots were utilized to visually compare the predicted values against the actual true values, offering a tangible representation of the models' effectiveness.＜/p＞＜p＞Throughout the project, rigorous data preprocessing and feature engineering were integral to improving model accuracy. Features were appropriately scaled, and categorical variables were transformed using techniques like one-hot encoding, enabling models to efficiently learn from the data.＜/p＞＜p＞The project also focused on the interpretability of the models, enabling stakeholders to comprehend the factors influencing passenger satisfaction predictions. This interpretability was essential for making informed business decisions based on the model insights.＜/p＞＜p＞In conclusion, the project showcased a comprehensive approach to analyzing and predicting airline passenger satisfaction. Through meticulous data exploration, feature distribution analysis, machine learning model selection, hyperparameter tuning, and deep learning implementation, the project provided valuable insights for the airline industry. By utilizing a combination of machine learning and deep learning techniques, the project demonstrated a holistic approach to understanding and enhancing passenger experiences and satisfaction levels.＜/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。	1,686円
ADVANCED VIDEO PROCESSING PROJECTS WITH PYTHON AND TKINTER【電子書籍】[ Vivian Siahaan ] 楽天Kobo電子書籍ストア	＜p＞The book focuses on developing Python-based GUI applications for video processing and analysis, catering to various needs such as object tracking, motion detection, and frame analysis. These applications utilize libraries like Tkinter for GUI development and OpenCV for video processing, offering user-friendly interfaces with interactive controls. They provide functionalities like video playback, frame navigation, ROI selection, filtering, and histogram analysis, empowering users to perform detailed analysis and manipulation of video content.＜/p＞＜p＞Each project tackles specific aspects of video analysis, from simplifying video processing tasks through a graphical interface to implementing advanced algorithms like Lucas-Kanade, Kalman filter, and Gaussian pyramid optical flow for optical flow computation and object tracking. Moreover, they integrate features like MD5 hashing for video integrity verification and filtering techniques such as bilateral filtering, anisotropic diffusion, and denoising for enhancing video quality and analysis accuracy. Overall, these projects demonstrate the versatility and effectiveness of Python in developing comprehensive tools for video analysis, catering to diverse user needs in fields like computer vision, multimedia processing, forensic analysis, and content verification.＜/p＞＜p＞The first project aims to simplify video processing tasks through a user-friendly graphical interface, allowing users to execute various operations like filtering, edge detection, hashing, motion analysis, and object tracking effortlessly. The process involves setting up the GUI framework using tkinter, adding descriptive titles and containers for buttons, defining button actions to execute Python scripts, and dynamically generating buttons for organized presentation. Functionalities cover a wide range of video processing tasks, including frame operations, motion analysis, and object tracking. Users interact by launching the application, selecting an operation, and viewing results. Advantages include ease of use, organized access to functionalities, and extensibility for adding new tasks. Overall, this project bridges Python scripting with a user-friendly interface, democratizing advanced video processing for a broader audience.＜/p＞＜p＞The second project aims to develop a video player application with advanced frame analysis functionalities, allowing users to open video files, navigate frames, and analyze them extensively. The application, built using tkinter, features a canvas for video display with zoom and drag capabilities, playback controls, and frame extraction options. Users can jump to specific times, extract frames for analysis, and visualize RGB histograms while calculating MD5 hash values for integrity verification. Additionally, users can open multiple instances of the player for parallel analysis. Overall, this tool caters to professionals in forensic analysis, video editing, and educational fields, facilitating comprehensive frame-by-frame examination and evaluation.＜/p＞＜p＞The third project is a robust Python tool tailored for video frame analysis and filtering, employing Tkinter for the GUI. Users can effortlessly load, play, and dissect video files frame by frame, with options to extract frames, implement diverse filtering techniques, and visualize color channel histograms. Additionally, it computes and exhibits hash values for extracted frames, facilitating frame comparison and verification. With an array of functionalities, including OpenCV integration for image processing and filtering, alongside features like wavelet transform and denoising algorithms, this application is a comprehensive solution for users requiring intricate video frame scrutiny and manipulation.＜/p＞＜p＞The fourth project is a robust application designed for edge detection on video frames, featuring a Tkinter-based GUI for user interaction. It facilitates video loading, frame navigation, and application of various edge detection algorithms, alongside offering analyses like histograms and hash values. With functionalities for frame extraction, edge detection selection, and interactive zooming, the project provides a comprehensive solution for users in fields requiring detailed video frame analysis and processing, such as computer vision and multimedia processing.＜/p＞＜p＞The fifth project presents a sophisticated graphical application tailored for video frame processing and MD5 hashing. It offers users a streamlined interface to load videos, inspect individual frames, and compute hash values, crucial for tasks like video forensics and integrity verification. Utilizing Python libraries such as Tkinter, PIL, and moviepy, the project ensures efficient video handling, metadata extraction, and histogram visualization, providing a robust solution for diverse video analysis needs. With its focus on frame-level hashing and extensible architecture, the project stands as a versatile tool adaptable to various applications in video analysis and content verification.＜/p＞＜p＞The sixth project presents a robust graphical tool designed for video analysis and frame extraction. By leveraging Python and key libraries like Tkinter, PIL, and imageio, users can effortlessly open videos, visualize frames, and extract specific frames for analysis. Notably, the application computes hash values using eight different algorithms, including MD5, SHA-1, and SHA-256, enhancing its utility for tasks such as video forensics and integrity verification. With features like frame zooming, navigation controls, and support for multiple instances, this project offers a versatile platform for comprehensive video analysis, catering to diverse user needs in fields like content authentication and forensic investigation.＜/p＞＜p＞The seventh project offers a graphical user interface (GUI) for computing hash values of video files, ensuring their integrity and authenticity through multiple hashing algorithms. Key features include video playback controls, hash computation using algorithms like MD5, SHA-1, and SHA-256, and displaying and saving hash values for reference. Users can open multiple instances to handle different videos simultaneously. The tool is particularly useful in digital forensics, data verification, and content security, providing a user-friendly interface and robust functionalities for reliable video content verification.＜/p＞＜p＞The eighth project aims to develop a GUI application that lets users interact with video files through various controls, including play, pause, stop, frame navigation, and time-specific jumps. It also offers features like zooming, noise reduction via a mean filter, and the ability to open multiple instances. Users can load videos, adjust playback, apply filters, and handle video frames dynamically, enhancing video viewing and manipulation.＜/p＞＜p＞The ninth project aims to develop a GUI application for filtering video frames using anisotropic diffusion, allowing users to load videos, apply the filter, and interact with the frames. The core component, AnisotropicDiffusion, handles video processing and GUI interactions. Users can control playback, zoom, and navigate frames, with the ability to apply the filter dynamically. The GUI features panels for video display, control buttons, and supports multiple instances. Event handlers enable smooth interaction, and real-time updates reflect changes in playback and filtering. The application is designed for efficient memory use, intuitive controls, and a responsive user experience.＜/p＞＜p＞The tenth project involves creating a GUI application that allows users to filter video frames using a bilateral filter. Users can load video files, apply the filter, and interact with the filtered frames. The BilateralFilter class handles video processing and GUI interactions, initializing attributes like the video source and GUI elements. The GUI includes panels for displaying video frames and control buttons for opening files, playback, zoom, and navigation. Users can control playback, zoom, pan, and apply the filter dynamically. The application supports multiple instances, efficient rendering, and real-time updates, ensuring a responsive and user-friendly experience.＜/p＞＜p＞The twelfth project involves creating a GUI application for filtering video frames using the Non-Local Means Denoising technique. The NonLocalMeansDenoising class manages video processing and GUI interactions, initializing attributes like video source, frame index, and GUI elements. Users can load video files, apply the denoising filter, and interact with frames through controls for playback, zoom, and navigation. The GUI supports multiple instances, allowing users to compare videos. Efficient rendering ensures smooth playback, while adjustable parameters fine-tune the filter's performance. The application maintains aspect ratios, handles errors, and provides feedback, prioritizing a seamless user experience.＜/p＞＜p＞The thirteenth performs Canny edge detection on video frames. It allows users to load video files, view original frames, and see Canny edge-detected results side by side. The VideoCanny class handles video processing and GUI interactions, initializing necessary attributes. The interface includes panels for video display and control buttons for loading videos, adjusting zoom, jumping to specific times, and controlling playback. Users can also open multiple instances for comparing videos. The application ensures smooth playback and real-time edge detection with efficient rendering and robust error handling.＜/p＞＜p＞The fourteenth project is a GUI application built with Tkinter and OpenCV for real-time edge detection in video streams using the Kirsch algorithm. The main class, VideoKirsch, initializes the GUI components, providing features like video loading, frame display, zoom control, playback control, and Kirsch edge detection. The interface displays original and edge-detected frames side by side, with control buttons for loading videos, adjusting zoom, jumping to specific times, and controlling playback. Users can play, pause, 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。	1,180円
Python GUI with PyQt Learn to build modern and stunning GUIs in Python with PyQt5 and Qt Designer (English Edition)【電子書籍】[ Saurabh Chandrakar ] 楽天Kobo電子書籍ストア	＜p＞Python GUI with PyQt is an invaluable guide for both novice and experienced programmers who want to master the art of creating visually appealing and interactive graphical user interfaces (GUIs) for Python applications using the PyQt5 library and the Qt Designer visual layout tool. It covers all the essential concepts of PyQt5 GUI programming in a comprehensive and beginner-friendly manner.The book starts with the basics of GUI programming, such as creating windows, using layout managers, and managing signals and events. It then covers more advanced topics, such as creating dialog and message boxes, using container, input, item views, and display widgets. Then it teaches you to connect signals to slots, the cornerstone of event-driven programming, and discover how to utilize Qt Designer, a visual GUI design tool, to streamline the development process. As you progress, delve into the realm of containers, learning to organize and manage widgets with finesse. Explore the vast array of input widgets, enabling users to seamlessly provide data. You finally conclude your journey by mastering the art of display widgets, empowering you to present information with clarity and elegance. The book is also highly interactive, with practical examples and exercises at the end of each chapter. These help you solidify your understanding of the concepts and gain practical experience in PyQt5 GUI programming.Overall, this is an excellent book for anyone who wants to learn how to create GUI applications in Python using PyQt5 and Qt Designer.＜/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。	2,695円