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BioCoder is a quarterly newsletter for DIYbio, synthetic bio, and anything related. You'll discover:Articles about interesting projects and experiments, such as the glowing plantArticles about tools, both those you buy and those you buildVisits to DIYbio laboratoriesProfiles of key people in the communityAnnouncements of events and other items of interestSafety pointers and tips about good laboratory practiceAnything that's interesting or useful: you tell us!And BioCoder is free (for the time being), unless you want a dead-tree version. We'd like BioCoder to become self supporting (maybe even profitable), but we'll worry about that after we've got a few issues under our belt.If you'd like to contribute, send email to BioCoder@oreilly.com. Tell us what you'd like to do, and we'll get you started.

"When Isaac Newton developed calculus in the 1600s, he was trying to tie together math and physics in an intuitive, geometrical way. But over time math and physics teaching became heavily weighted toward algebra, and less toward geometrical problem solving. However, many practicing mathematicians and physicists will get their intuition geometrically first and do the algebra later. Make:Calculus imagines how Newton might have used 3D printed models, construction toys, programming, craft materials, and an Arduino or two to teach calculus concepts in an intuitive way. The book uses as little reliance on algebra as possible while still retaining enough to allow comparison with a traditional curriculum. This book is not a traditional Calculus I textbook. Rather, it will take the reader on a tour of key concepts in calculus that lend themselves to hands-on projects. This book also defines terms and common symbols for them so that self-learners can learn more on their own"

Edge AI is transforming the way computers interact with the real world, allowing IoT devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can capture human intuition and deploy it to any target--from ultra-low power microcontrollers to embedded Linux devices. This practical guide gives engineering professionals, including product managers and technology leaders, an end-to-end framework for solving real-world industrial, commercial, and scientific problems with edge AI. You'll explore every stage of the process, from data collection to model optimization to tuning and testing, as you learn how to design and support edge AI and embedded ML products. Edge AI is destined to become a standard tool for systems engineers. This high-level road map helps you get started. Develop your expertise in AI and ML for edge devices Understand which projects are best solved with edge AI Explore key design patterns for edge AI apps Learn an iterative workflow for developing AI systems Build a team with the skills to solve real-world problems Follow a responsible AI process to create effective products

Why are so many companies adopting GitOps for their DevOps and cloud native strategy? This reliable framework is quickly becoming the standard method for deploying apps to Kubernetes. With this practical, developer-oriented book, DevOps engineers, developers, IT architects, and SREs will learn the most useful recipes and examples for following GitOps practices.

The software development ecosystem is constantly changing, providing a constant stream of new tools, frameworks, techniques, and paradigms. Over the past few years, incremental developments in core engineering practices for software development have created the foundations for rethinking how architecture changes over time, along with ways to protect important architectural characteristics as it evolves. This practical guide ties those parts together with a new way to think about architecture and time.

While many introductory guides to AI are calculus books in disguise, this one mostly eschews the math. Instead, author Jeff Prosise helps engineers and software developers build an intuitive understanding of AI to solve business problems. Need to create a system to detect the sounds of illegal logging in the rainforest, analyze text for sentiment, or predict early failures in rotating machinery? This practical book teaches you the skills necessary to put AI and machine learning to work at your company.Applied Machine Learning and AI for Engineers provides examples and illustrations from the AI and ML course Prosise teaches at companies and research institutions worldwide. There's no fluff and no scary equationsjust a fast start for engineers and software developers, complete with hands-on examples.This book helps you:Learn what machine learning and deep learning are and what they can accomplishUnderstand how popular learning algorithms work and when to apply themBuild machine learning models in Python with Scikit-Learn, and neural networks with Keras and TensorFlowTrain and score regression models and binary and multiclass classification modelsBuild facial recognition models and object detection modelsBuild language models that respond to natural-language queries and translate text to other languagesUse Cognitive Services to infuse AI into the apps that you write

Expert advice on C programming is hard to find. While much help is available for object-oriented programming languages, there's surprisingly little for the C language. With this hands-on guide, beginners and experienced C programmers alike will find guidance about design decisions, including how to apply them bit by bit to running code examples when building large-scale programs.Christopher Preschern, a leading member of the design patterns community, answers questions such as how to structure C programs, cope with error handling, or design flexible interfaces. Whether you're looking for one particular pattern or an overview of design options for a specific topic, this book shows you how to implement hands-on design knowledge specifically for the C programming language.You'll find design patterns for:Error handlingReturning error informationMemory managementReturning data from C functionsData lifetime and ownershipFlexible APIsFlexible iterator interfacesOrganizing files in modular programsEscaping #ifdef Hell

Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library.Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve.Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answeringLearn how transformers can be used for cross-lingual transfer learningApply transformers in real-world scenarios where labeled data is scarceMake transformer models efficient for deployment using techniques such as distillation, pruning, and quantizationTrain transformers from scratch and learn how to scale to multiple GPUs and distributed environments

Candlesticks have become a key component of platforms and charting programs for financial trading. With these charts, traders can learn underlying patterns for interpreting price action history and forecasts. This A-Z guide shows portfolio managers, quants, strategists, and analysts how to use Python to recognize, scan, trade, and back-test the profitability of candlestick patterns.Financial author, trading consultant, and institutional market strategist Sofien Kaabar shows you how to create a candlestick scanner and indicator so you can compare the profitability of these patterns. With this hands-on book, you'll also explore a new type of charting system similar to candlesticks, as well as new patterns that have never been presented before.With this book, you will:Create and understand the conditions required for classic and modern candlestick patternsLearn the market psychology behind themUse a framework to learn how back-testing trading strategies are conductedExplore different charting systems and understand their limitationsImport OHLC historical FX data in Python in different time framesUse algorithms to scan for and reproduce patternsLearn a pattern's potential by evaluating its profitability and predictability

Author Rukmani Gopalan, a product management leader and data enthusiast, guides data architects and engineers through the major aspects of working with a cloud data lake, from design considerations and best practices to data format optimizations, performance optimization, cost management, and governance.

Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does.Explainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability. Experienced machine learning engineers and data scientists will learn hands-on how these techniques work so that you'll be able to apply these tools more easily in your daily workflow.This essential book provides:A detailed look at some of the most useful and commonly used explainability techniques, highlighting pros and cons to help you choose the best tool for your needsTips and best practices for implementing these techniquesA guide to interacting with explainability and how to avoid common pitfallsThe knowledge you need to incorporate explainability in your ML workflow to help build more robust ML systemsAdvice about explainable AI techniques, including how to apply techniques to models that consume tabular, image, or text dataExample implementation code in Python using well-known explainability libraries for models built in Keras and TensorFlow 2.0, PyTorch, and HuggingFace

Microsoft Power BI is a data analytics and visualization tool powerful enough for the most demanding data scientists, but accessible enough for everyday use for anyone who needs to get more from data. The market has many books designed to train and equip professional data analysts to use Power BI, but few of them make this tool accessible to anyone who wants to get up to speed on their own.This streamlined intro to Power BI covers all the foundational aspects and features you need to go from "e;zero to hero"e; with data and visualizations. Whether you work with large, complex datasets or work in Microsoft Excel, author Jeremey Arnold shows you how to teach yourself Power BI and use it confidently as a regular data analysis and reporting tool.You'll learn how to:Import, manipulate, visualize, and investigate data in Power BIApproach solutions for both self-service and enterprise BIUse Power BI in your organization's business intelligence strategyProduce effective reports and dashboardsCreate environments for sharing reports and managing data access with your teamDetermine the right solution for using Power BI offerings based on size, security, and computational needs

Why is Google Analytics 4 the most modern data model available for digital marketing analytics? Because rather than simply report what has happened, GA4's new cloud integrations enable more data activationlinking online and offline data across all your streams to provide end-to-end marketing data. This practical book prepares you for the future of digital marketing by demonstrating how GA4 supports these additional cloud integrations.Author Mark Edmondson, Google Developer Expert for Google Analytics and Google Cloud, provides a concise yet comprehensive overview of GA4 and its cloud integrations. Data, business, and marketing analysts will learn major facets of GA4's powerful new analytics model, with topics including data architecture and strategy, and data ingestion, storage, and modeling. You'll explore common data activation use cases and get guidance on how to implement them.You'll learn:How Google Cloud integrates with GA4The potential use cases that GA4 integrations can enableSkills and resources needed to create GA4 integrationsHow much GA4 data capture is necessary to enable use casesThe process of designing dataflows from strategy though data storage, modeling, and activation

Companies are scrambling to integrate AI into their systems and operations. But to build truly successful solutions, you need a firm grasp of the underlying mathematics. This accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory. Engineers, data scientists, and students alike will examine mathematical topics critical for AI--including regression, neural networks, optimization, backpropagation, convolution, Markov chains, and more--through popular applications such as computer vision, natural language processing, and automated systems. And supplementary Jupyter notebooks shed light on examples with Python code and visualizations. Whether you're just beginning your career or have years of experience, this book gives you the foundation necessary to dive deeper in the field. Understand the underlying mathematics powering AI systems, including generative adversarial networks, random graphs, large random matrices, mathematical logic, optimal control, and more Learn how to adapt mathematical methods to different applications from completely different fields Gain the mathematical fluency to interpret and explain how AI systems arrive at their decisions.

If you're interested in applying FP concepts to your Java code, this book is for you. You'll learn how, when, and why to use FP concepts such as immutability and pure functions to write more concise, reasonable, and future-proof code.

This practical book demonstrates why C++ is still one of the dominant production-quality languages for financial applications and systems. Many programmers believe that C++ is too difficult to learn. Author Daniel Hanson demonstrates that this is no longer the case.

With technological advancements, fast markets, and higher complexity of systems, software engineers tend to skip the uncomfortable topic of software efficiency. However, tactical, observability-driven performance optimizations are vital for every product to save money and ensure business success.With this book, any engineer can learn how to approach software efficiency effectively, professionally, and without stress. Author Bartomiej Potka provides the tools and knowledge required to make your systems faster and less resource-hungry. Efficient Go guides you in achieving better day-to-day efficiency using Go. In addition, most content is language-agnostic, allowing you to bring small but effective habits to your programming or product management cycles.This book shows you how to: Clarify and negotiate efficiency goalsOptimize efficiency on various levelsUse common resources like CPU and memory effectivelyAssess efficiency using observability signals like metrics, logging, tracing, and (continuous) profiling via open source projects like Prometheus, Jaeger, and ParcaApply tools like go test, pprof, benchstat, and k6 to create reliable micro and macro benchmarksEfficiently use Go and its features like slices, generics, goroutines, allocation semantics, garbage collection, and more!

The cloud promises cost savings, agility, and more. But the increasing complexity of modern IT systems often prevents businesses from realizing the outcomes they sought by moving to the cloud in the first place. At the core of this complexity is technical debt. Ad hoc decisions, traditional approaches, and "e;quick and dirty"e; solutions add up to fragile, more complex systems (and organizations)--reducing the pace of change, driving up costs, and degrading security. To succeed in the cloud, you first have to manage the problems IT complexity creates. This practical guide will help.If you're tasked with designing, delivering, or approving IT systems that support business growth, this book will teach you the principles and strategies you need to break out of silos, reassess traditional thinking, provide the business a better competitive position, and achieve cloud success across the company.You'll learn:How to assess your current IT environment to identify complexity-centered risksHow to structure cross-functional teams to reduce friction pointsHow to create a framework for IT investments that's aligned with your business goalsHow to measure your outcomes for continued success

In this special re-release, all 4 volumes of MAKE's fifth year are combined in this special 4-Volume Collector's Set.

Wikipedia Reader's Guide: The Missing Manual gives you the essential tools for getting the most out of Wikipedia. As a supplement to Wikipedia: The Missing Manual, this handbook provides a basic road map to the largest online collaborative encyclopedia. You'll learn the best ways to search Wikipedia for the information you need, how to navigate the encyclopedia by category, and what to do if you spot an error in an article.

Ready to put Intuit's QuickBase to work? Our new Missing Manual shows you how to capture, modify, share, and manage data and documents with this web-based data-sharing program quickly and easily. No longer do you have to coordinate your team through a blizzard of emails or play frustrating games of "e;guess which document is the right one."e;QuickBase saves your organization time and money, letting you manage and share the information that makes your business tick: sales figures, project timelines, drafts of documents, purchase or work requests--whatever information you need to keep business flowing smoothly.QuickBase: The Missing Manual shows you how to choose among QuickBase's dozens of ready-made applications (mini-databases, essentially) and how to customize one to fit your needs exactly. You'll also learn to assign people different roles within the application. The guide also shows you how to:Capture and modify data: Whatever kind of data you need to store--sales leads, catalog listings, project milestones, workflow checklists--you can use QuickBase's forms to record and organize that data so it makes sense to you.Filter, sort, and group data: Easily find the records that match your criteria, and then sort those records into groups that make their relationships clear.Display your data: QuickBase uses different views (Table, Grid Edit, Summary/Crosstab, Calendar, Chart, and Timeline) to display and summarize data. Switching between them is easy, like taking tasks listed in a table and displaying them as a timeline.Create reports: Print out a hard copy, embed charts in the annual report, or email this month's sales numbers.Because Intuit frequently introduces new features to QuickBase, you'll find updates to this book at our Missing Manual web site so you can benefit from the latest technology and user suggestions right away.

The X Resource is a quarterly working journal for X programmers that provides practical, timely information about the programming, administration, and use of the X Window System. It is published quarterly in January, April, July, and October by O'Reilly and Associates, Inc. The journal is the Official Publisher of the X Consortium Technical Conference Proceedings, which form the January issue. Articles for Issue 10 include:* "From the X Consortium: What's New in R6"* "The X User: TkMan-A Man Born Again"* "The X Administrator: Keeping Start-up Files Simple in a Mixed xdm/xinit Environment"* "Best of Netnews"* "The One-Minute Manager: Custom Motif Layout Widgets Made Easy"* "The Motif 2.0 Uniform Transfer Model: Unifying Selection, Clipboard, and Drag and Drop"* "The HBF Font Format: Optimizing Fixed-Pitch Font Support"* "Overview of the Input Method Protocol"* "An X Print Server: Bringing WYSIWYG to X"* "Automatic Pop-up Menus in Motif"* "Implementing Cut and Paste in the X Environment"

Device drivers literally drive everything you're interested in--disks, monitors, keyboards, modems--everything outside the computer chip and memory. And writing device drivers is one of the few areas of programming for the Linux operating system that calls for unique, Linux-specific knowledge. For years now, programmers have relied on the classic Linux Device Drivers from O'Reilly to master this critical subject. Now in its third edition, this bestselling guide provides all the information you'll need to write drivers for a wide range of devices.Over the years the book has helped countless programmers learn:how to support computer peripherals under the Linux operating systemhow to develop and write software for new hardware under Linuxthe basics of Linux operation even if they are not expecting to write a driverThe new edition of Linux Device Drivers is better than ever. The book covers all the significant changes to Version 2.6 of the Linux kernel, which simplifies many activities, and contains subtle new features that can make a driver both more efficient and more flexible. Readers will find new chapters on important types of drivers not covered previously, such as consoles, USB drivers, and more.Best of all, you don't have to be a kernel hacker to understand and enjoy this book. All you need is an understanding of the C programming language and some background in Unix system calls. And for maximum ease-of-use, the book uses full-featured examples that you can compile and run without special hardware.Today Linux holds fast as the most rapidly growing segment of the computer market and continues to win over enthusiastic adherents in many application areas. With this increasing support, Linux is now absolutely mainstream, and viewed as a solid platform for embedded systems. If you're writing device drivers, you'll want this book. In fact, you'll wonder how drivers are ever written without it.