検索キーワード： プログラミング・ソフトウェア開発

Large language models (LLMs) and generative AI are rapidly changing the healthcare industry. These technologies have the potential to revolutionize healthcare by improving the efficiency, accuracy, and personalization of care. This practical book shows healthcare leaders, researchers, data scientists, and AI engineers the potential of LLMs and generative AI today and in the future, using storytelling and illustrative use cases in healthcare.

Designers and managers hope their products become essential for users-integrated into their lives like Instagram, Lyft, and others have become. Such deep integration isn't accidental: it's a process of careful design and iterative learning, especially for technology companies. This guide shows you how to apply behavioral science—research that supports many products—to help your users achieve their goals using your product.

As data floods into your company, you need to put it to work right away—and SQL is the best tool for the job. With the latest edition of this introductory guide, author Alan Beaulieu helps developers get up to speed with SQL fundamentals for writing database applications, performing administrative tasks, and generating reports. You'll find new chapters on SQL and big data, analytic functions, and working with very large databases.

When it comes to choosing, using, and maintaining a database, understanding its internals is essential. But with so many distributed databases and tools available today, it's often difficult to understand what each one offers. With this practical guide, Alex Petrov guides developers through the concepts behind modern database and storage engine internals.

You've played around with ChatGPT. Asked it to write an email. Maybe summarize a few articles. And the same question keeps circling: "People are making real money with this. Why can't I figure out how?" It's not that you haven't tried. But every time, you hit the same wall. Written by Milo Foster, author of The AI Workshop series, this book gives you a complete system for building AI-powered income using nothing but ChatGPT and plain English. No coding. EVER.

Want to learn quick data types? Need examples on data types? How about variables? Or need to know how to manipulate variables with Fortran programming? Every type of intrinsic function in Fortran is right here!

From the bestselling author of Blood, Sweat, and Pixels comes the next definitive, behind-the-scenes account of the video game industry: how some of the past decade's most renowned studios fell apart—and the stories, both triumphant and tragic, of what happened next. Jason Schreier's...

One key responsibility of product designers and UX practitioners is to conduct formal and informal research to clarify design decisions and business needs. But there’s often mystery around product research, with the feeling that you need to be a research Zen master to gather anything useful. But anyone can conduct product research. With this quick reference guide, you’ll learn a common language and set of tools to help you carry out research in an informed and productive manner. 

At some point in your career, you'll realize there's more to being a software engineer than dealing with code. With more than forty stand-alone stories, Lopp walks through a complete job lifecycle, starting with the interview and ending with the realization that it might be time to move on. You'll learn how to handle baffling circumstances in your job, understand what you want from your career, and discover how to thrive in your workplace.

Listen to Quality Assurance to discover everything you need to know about testing and software quality, step by step to increase your software skill set. Learn how to dominate computer systems. All your basic knowledge in one purchase!

Transform your career in just a few hours with Python data science mastery—even if you've never written a line of code before! Have you ever felt overwhelmed at the thought of learning data analysis in Python? Do you worry that without a computer science degree, you'll never be able to visualize and analyze data effectively? Are you eager to bring data-driven insights into your career but don’t know where to start?

Practical techniques for writing code that is robust, reliable, and easy for team members to understand and adapt. In Good Code, Bad Code you’ll learn how to: Think about code like an effective software engineer, write functions that read like well-structured sentences, ensure code is reliable and bug free, effectively unit test code, identify code that can cause problems and improve it, write code that is reusable and adaptable to new requirements, improve your medium and long-term productivity, and save yourself and your team time.

Data scientists, data analysts, and fraud analysts will learn how to identify and quickly respond to attacks. You'll get a comprehensive view of typical incursions as well as recommended detection methods. Online fraud is constantly evolving. This book helps experienced researchers safely guide and protect their organizations in this ever-changing fraud landscape.

The Software Engineering industry often evaluates engineers based on how many lines of code they write, how many years of experience they have, or how many features they ship. But I think a more powerful metric is the number of bugs they encountered, reproduced, traced to their root cause and fixed. Those experiences shape an engineer far more than raw output or time served in my opinion, let me elaborate. Bugs hinder the stability of software, which affects the user experience.

To ensure that applications are reliable and always available, more businesses today are moving applications to AWS. But many companies still struggle to design and build these cloud applications effectively. With this practical guide, software, DevOps, and cloud engineers will learn how to implement resilient designs and configurations in the cloud using hands-on independent labs.

At most technology companies, you'll reach Senior software engineer, the career level for software engineers, in five to eight years. At the career level, your company's career ladder won't require that you work towards the next promotion; being promoted further is an exception rather than expected. This is also when many engineers are first given an opportunity to move into engineering management.

As organizations shift from monolithic applications to smaller, self-contained microservices, distributed systems have become more fine-grained. But developing these new systems brings its own host of problems. This expanded second edition takes a holistic view of topics that you need to consider when building, managing, and scaling microservices architectures. Through clear examples and practical advice, author Sam Newman gives everyone from architects and developers to testers and IT operators a firm grounding in the concepts.

A decade ago, developers built information-sharing software by writing a lot of custom code in a single code base, then adding layers of caching. Now we build information systems: interdependent software and services, data platforms, and event streams. Many of our software-to-systems initiatives fail, though, and when they do, we double down on traditional linear approaches. But linear thinking cannot resolve systems challenges. We need to shift to systems thinking.

We're at an inflection point in data, where our data management solutions no longer match the complexity of organizations, the proliferation of data sources, and the scope of our aspirations to get value from data with AI and analytics. In this practical book, Zhamak Dehghani introduces data mesh, a decentralized sociotechnical paradigm drawn from modern distributed architecture that provides a new approach to sourcing, sharing, accessing, and managing analytical data at scale.

Most people don’t need “better AI.” They need control. Right now, AI feels random. Sometimes it works. Sometimes it gives you something amazing. Most of the time… it misses the mark and you don’t know why. That’s not an AI problem. That’s a communication problem. Nano Banana Pro was written to fix that. This is not a theory book. It’s not a hype book. And it’s definitely not a “here’s what AI might do someday” book.