The overwhelming majority of a software systemâ??s lifespan is spent in use, not in design or implementation. So, why does conventional wisdom insist that software engineers focus primarily on the design and development of large-scale computing systems? In this collection of essays and articles, key members of Googleâ??s Site Reliability Team explain how and why their commitment to the entire lifecycle has enabled the company to successfully build, deploy, monitor, and maintain some of the largest software systems in the world. Youâ??ll learn the principles and practices that enable Google engineers to make systems more scalable, reliable, and efficientâ??lessons directly applicable to your organization. This book is divided into four sections: Introductionâ??Learn what site reliability engineering is and why it differs from conventional IT industry practices Principlesâ??Examine the patterns, behaviors, and areas of concern that influence the work of a site reliability engineer (SRE) Practicesâ??Understand the theory and practice of an SREâ??s day-to-day work: building and operating large distributed computing systems Managementâ??Explore Google's best practices for training, communication, and meetings that your organization can use

Site Reliability Engineering

In Crush It!, online marketing trailblazer Gary Vaynerchuk tells business owners what they need to do to boost their sales using the internet—just as he has done to build his family’s wine store from a $4 million business to a $60 million one. Crush It! will show readers how to find their passion, then step by step how to turn it into a flourishing, monetized business.

Crush It!

**New York Times, Wall Street Journal, and USAToday Bestseller for May 2022** Tony Fadell led the teams that created the iPod, iPhone and Nest Learning Thermostat and learned enough in 30+ years in Silicon Valley about leadership, design, startups, Apple, Google, decision-making, mentorship, devastating failure and unbelievable success to fill an encyclopedia. So that’s what this book is. An advice encyclopedia. A mentor in a box. Written for anyone who wants to grow at work—from young grads navigating their first jobs to CEOs deciding whether to sell their company—Build is full of personal stories, practical advice and fascinating insights into some of the most impactful products and people of the 20th century. Each quick 5-20 page entry builds on the previous one, charting Tony’s personal journey from a product designer to a leader, from a startup founder to an executive to a mentor. Tony uses examples that are instantly captivating, like the process of building the very first iPod and iPhone. Every chapter is designed to help readers with a problem they’re facing right now—how to get funding for their startup, whether to quit their job or not, or just how to deal with the jerk in the next cubicle. Tony forged his path to success alongside mentors like Steve Jobs and Bill Campbell, icons of Silicon Valley who succeeded time and time again. But Tony doesn’t follow the Silicon Valley credo that you have to reinvent everything from scratch to make something great. His advice is unorthodox because it’s old school. Because Tony’s learned that human nature doesn’t change. You don’t have to reinvent how you lead and manage—just what you make. And Tony’s ready to help everyone make things worth making.

Build

Thinking in Systems is a concise and crucial book offering insight for problem-solving on scales ranging from the personal to the global. This essential primer brings systems thinking out of the realm of computers and equations and into the tangible world, showing readers how to develop the systems-thinking skills that thought leaders across the globe consider critical for 21st-century life. While readers will learn the conceptual tools and methods of systems thinking, the heart of the book is grander than methodology. Donella Meadows was known as much for nurturing positive outcomes as she was.

Thinking in Systems

Self-love is not merely a trend or a buzzword; it is a foundational aspect of personal growth and well-being. Embracing self-love allows individuals to cultivate a deeper understanding of themselves, nurturing their strengths while acknowledging their weaknesses. This practice fosters a sense of inner harmony that encourages individuals to live authentically and pursue their passions. By prioritizing self-love, one opens the door to a more fulfilling life, where the pursuit of happiness begins from within.

How To Be The Love You Seek

Describes why men are attracted to strong women and offers advice on ways a woman can relate to men and gain a man's love and respect.

Why Men Love Bitches

Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it.

Storytelling with Data

Presents a guide to artificial intelligence, covering such topics as intelligent agents, problem-solving, logical agents, planning, uncertainty, learning, and robotics.

Artificial Intelligence

From the back cover: Build a Large Language Model (From Scratch) is a practical and eminently-satisfying hands-on journey into the foundations of generative AI. Without relying on any existing LLM libraries, you'll code a base model, evolve it into a text classifier, and ultimately create a chatbot that can follow your conversational instructions. And you'll really understand it because you built it yourself! About the reader: Readers need intermediate Python skills and some knowledge of machine learning. The LLM you create will run on any modern laptop and can optionally utilize GPUs.

Build a Large Language Model (From Scratch)

Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

Generative Deep Learning

Learn to use generative AI techniques to create novel text, images, audio, and even music with this practical, hands-on book. Readers will understand how state-of-the-art generative models work, how to fine-tune and adapt them to their needs, and how to combine existing building blocks to create new models and creative applications in different domains. This go-to book introduces theoretical concepts followed by guided practical applications, with extensive code samples and easy-to-understand illustrations. You'll learn how to use open source libraries to utilize transformers and diffusion models, conduct code exploration, and study several existing projects to help guide your work. Build and customize models that can generate text and images Explore trade-offs between using a pretrained model and fine-tuning your own model Create and utilize models that can generate, edit, and modify images in any style Customize transformers and diffusion models for multiple creative purposes Train models that can reflect your own unique style

Hands-On Generative AI with Transformers and Diffusion Models

AN INSTANT NEW YORK TIMES BESTSELLER ONE OF TIME’S 100 MOST INFLUENTUAL PEOPLE IN ARTIFICIAL INTELLIGENCE The noted inventor and futurist’s successor to his landmark book The Singularity Is Near explores how technology will transform the human race in the decades to come Since it was first published in 2005, Ray Kurzweil’s The Singularity Is Near and its vision of an exponential future have spawned a worldwide movement. Kurzweil's predictions about technological advancements have largely come true, with concepts like AI, intelligent machines, and biotechnology now widely familiar to the public. In this entirely new book Ray Kurzweil brings a fresh perspective to advances toward the Singularity—assessing his 1999 prediction that AI will reach human level intelligence by 2029 and examining the exponential growth of technology—that, in the near future, will expand human intelligence a millionfold and change human life forever. Among the topics he discusses are rebuilding the world, atom by atom with devices like nanobots; radical life extension beyond the current age limit of 120; reinventing intelligence by connecting our brains to the cloud; how exponential technologies are propelling innovation forward in all industries and improving all aspects of our well-being such as declining poverty and violence; and the growth of renewable energy and 3-D printing. He also considers the potential perils of biotechnology, nanotechnology, and artificial intelligence, including such topics of current controversy as how AI will impact employment and the safety of autonomous cars, and "After Life" technology, which aims to virtually revive deceased individuals through a combination of their data and DNA. The culmination of six decades of research on artificial intelligence, The Singularity Is Nearer is Ray Kurzweil’s crowning contribution to the story of this science and the revolution that is to come.

The Singularity Is Nearer

*The brand new thriller from Lucy Foley – THE MIDNIGHT FEAST – is available to buy now* The No.1 Sunday Times bestseller *Over 1 million copies sold worldwide* *One of The Times and Sunday Times Crime Books of the Year* *Goodreads Choice Awards winner for Crime & Mystery 2020*

The Guest List

English novelist, historian and science writer Herbert George Wells (1866–1946) abandoned teaching and launched his literary career with a series of highly successful science-fiction novels. The Time Machine was the first of a number of these imaginative literary inventions. First published in 1895, the novel follows the adventures of a hypothetical Time Traveller who journeys into the future to find that humanity has evolved into two races: the peaceful Eloi — vegetarians who tire easily — and the carnivorous, predatory Morlocks. After narrowly escaping from the Morlocks, the Time Traveller undertakes another journey even further into the future where he finds the earth growing bitterly cold as the heat and energy of the sun wane. Horrified, he returns to the present, but soon departs again on his final journey. While the novel is underpinned with both Darwinian and Marxist theory and offers fascinating food for thought about the world of the future, it also succeeds as an exciting blend of adventure and pseudo-scientific romance. Sure to delight lovers of the fantastic and bizarre, The Time Machine is a book that belongs on the shelf of every science-fiction fan.

The Time Machine

The Go Programming Language is the authoritative resource for any programmer who wants to learn Go. It shows how to write clear and idiomatic Go to solve real-world problems. The book does not assume prior knowledge of Go nor experience with any specific language, so you’ll find it accessible whether you’re most comfortable with JavaScript, Ruby, Python, Java, or C++. The first chapter is a tutorial on the basic concepts of Go, introduced through programs for file I/O and text processing, simple graphics, and web clients and servers. Early chapters cover the structural elements of Go programs: syntax, control flow, data types, and the organization of a program into packages, files, and functions. The examples illustrate many packages from the standard library and show how to create new ones of your own. Later chapters explain the package mechanism in more detail, and how to build, test, and maintain projects using the go tool. The chapters on methods and interfaces introduce Go’s unconventional approach to object-oriented programming, in which methods can be declared on any type and interfaces are implicitly satisfied. They explain the key principles of encapsulation, composition, and substitutability using realistic examples. Two chapters on concurrency present in-depth approaches to this increasingly important topic. The first, which covers the basic mechanisms of goroutines and channels, illustrates the style known as communicating sequential processes for which Go is renowned. The second covers more traditional aspects of concurrency with shared variables. These chapters provide a solid foundation for programmers encountering concurrency for the first time. The final two chapters explore lower-level features of Go. One covers the art of metaprogramming using reflection. The other shows how to use the unsafe package to step outside the type system for special situations, and how to use the cgo tool to create Go bindings for C libraries. The book features hundreds of interesting and practical examples of well-written Go code that cover the whole language, its most important packages, and a wide range of applications. Each chapter has exercises to test your understanding and explore extensions and alternatives. Source code is freely available for download from http://gopl.io/ and may be conveniently fetched, built, and installed using the go get command.

The Go Programming Language

In the author's point of view, a black swan is an improbable event with three principal characteristics - It is unpredictable; it carries a massive impact; and, after the fact, we concoct an explanation that makes it appear less random, and more predictable, than it was. Why do we not acknowledge the phenomenon of black swans until after they occur? Part of the answer, according to Taleb, is that humans are hardwired to learn specifics when they should be focused on generalities. We concentrate on things we already know and time and time again fail to take into consideration what we don't know. We are, therefore, unable to truly estimate opportunities, too vulnerable to the impulse to simplify, narrate, and categorize, and not open enough to rewarding those who can imagine the 'impossible'.

The Black Swan

“A must-read for any investor.”—Browser One of the “few great investment books” (Andrew Tobias) ever written, with 2 million copies in print. In a time of rampant misinformation about ways of growing your money, Burton G. Malkiel’s gimmick-free investment guide is more necessary than ever. Whether you’re considering your first 401k contribution or contemplating retirement, the fully updated, fiftieth anniversary edition of A Random Walk Down Wall Street remains the best investment guide money can buy. Drawing on his experience as an economist, financial adviser, and successful investor, Malkiel shows why an individual who saves consistently over time and buys a diversified set of index funds can achieve above-average investment results. He addresses current investment fads and critically analyzes cryptocurrencies, NFTs, and meme stocks. Malkiel reveals how to be a tax smart investor and how to make sense of recently popular investment management techniques, including factor investing, risk parity, and ESG portfolios. Investors of every age, experience level, and risk tolerance will find the step-by-step guidance they need to protect and grow their dollars.

A Random Walk Down Wall Street: The Best Investment Guide That Money Can Buy (13th Edition)

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 -now revised in full color- 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 answering Learn how transformers can be used for cross-lingual transfer learning Apply transformers in real-world scenarios where labeled data is scarce Make transformer models efficient for deployment using techniques such as distillation, pruning, and quantization Train transformers from scratch and learn how to scale to multiple GPUs and distributed environments

Natural Language Processing with Transformers, Revised Edition

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

Mathematics for Machine Learning

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start. You'll discover how to: Apply DevOps best practices to machine learning Build production machine learning systems and maintain them Monitor, instrument, load-test, and operationalize machine learning systems Choose the correct MLOps tools for a given machine learning task Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware

Practical MLOps

Distributed systems have become more fine-grained as organizations shift from code-heavy monolithic applications to smaller, self-contained microservices. But developing these systems brings its own set of problems. With lots of examples and practical advice, this expanded second edition takes a holistic view of the topics system architects and administrators must consider when building, managing, and evolving microservices architectures. Author Sam Newman provides you with a firm grounding in the concepts while diving into the latest solutions for modeling, integrating, testing, deploying, and monitoring your own autonomous services. Through real-world examples, you'll learn how organizations worldwide are getting the most out of these architectures. Microservices technologies are moving quickly. This book brings you up to speed. Get new information on user interfaces, container orchestration, and serverless Use microservices to align system design with your organization's goals Explore options for integrating a service with the rest of your system Take an incremental approach when splitting monolithic codebases Deploy individual microservices through continuous integration Examine the complexities of testing and monitoring distributed services Manage security with expanded content around user-to-service and service-to-service models Understand the challenges of scaling microservices architectures.

Building Microservices

Master professional-level coding in Rust. For developers who’ve mastered the basics, this book is the next step on your way to professional-level programming in Rust. It covers everything you need to build and maintain larger code bases, write powerful and flexible applications and libraries, and confidently expand the scope and complexity of your projects. Author Jon Gjengset takes you deep into the Rust programming language, dissecting core topics like ownership, traits, concurrency, and unsafe code. You’ll explore key concepts like type layout and trait coherence, delve into the inner workings of concurrent programming and asynchrony with async/await, and take a tour of the world of no_std programming. Gjengset also provides expert guidance on API design, testing strategies, and error handling, and will help develop your understanding of foreign function interfaces, object safety, procedural macros, and much more. You'll Learn: How to design reliable, idiomatic, and ergonomic Rust programs based on best principles Effective use of declarative and procedural macros, and the difference between them How asynchrony works in Rust – all the way from the Pin and Waker types used in manual implementations of Futures, to how async/await saves you from thinking about most of those words What it means for code to be unsafe, and best practices for writing and interacting with unsafe functions and traits How to organize and configure more complex Rust projects so that they integrate nicely with the rest of the ecosystem How to write Rust code that can interoperate with non-Rust libraries and systems, or run in constrained and embedded environments Brimming with practical, pragmatic insights that you can immediately apply, Rust for Rustaceans helps you do more with Rust, while also teaching you its underlying mechanisms.

Rust for Rustaceans

Getting rich is not just about luck; happiness is not just a trait we are born with. These aspirations may seem out of reach, but building wealth and being happy are skills we can learn. So what are these skills, and how do we learn them? What are the principles that should guide our efforts? What does progress really look like? Naval Ravikant is an entrepreneur, philosopher, and investor who has captivated the world with his principles for building wealth and creating long-term happiness. The Almanack of Naval Ravikant is a collection of Naval's wisdom and experience from the last ten years, shared as a curation of his most insightful interviews and poignant reflections. This isn't a how-to book, or a step-by-step gimmick. Instead, through Naval's own words, you will learn how to walk your own unique path toward a happier, wealthier life.

The Almanack of Naval Ravikant

The #1 international bestseller, now in paperback! For more than four decades, Robin Sharma has crossed the globe helping people lead lives that inspire the world. Now, in The 5 AM Club, he shares his original insights and the battle-tested tools he has developed by working with the most successful people on the planet. Based on the key insight that winning starts at the beginning of the day, Sharma shows how you can reduce stress, improve focus, unleash uncommon creativity, achieve exponential productivity and restore balance. Just one hour a day can change your life.

The 5 Am Club

"Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you will learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available in the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You will understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, governance, and deployment that are critical in any data environment regardless of the underlying technology. This book will help you: Assess data engineering problems using an end-to-end data framework of best practices Cut through marketing hype when choosing data technologies, architecture, and processes Use the data engineering lifecycle to design and build a robust architecture Incorporate data governance and security across the data engineering lifecycle." - from Publisher.

Fundamentals of Data Engineering

“By far the best book on investing ever written.” — Warren Buffett The classic text of Benjamin Graham’s seminal The Intelligent Investor has now been revised and annotated to update the timeless wisdom for today’s market conditions. The greatest investment advisor of the twentieth century, Benjamin Graham, taught and inspired people worldwide. Graham's philosophy of "value investing"—which shields investors from substantial error and teaches them to develop long-term strategies—has made The Intelligent Investor the stock market bible ever since its original publication in 1949. Over the years, market developments have proven the wisdom of Graham's strategies. While preserving the integrity of Graham's original text, this revised edition includes updated commentary by noted financial journalist Jason Zweig, whose perspective incorporates the realities of today's market, draws parallels between Graham's examples and today's financial headlines, and gives readers a more thorough understanding of how to apply Graham's principles. Vital and indispensable, this revised edition of The Intelligent Investor is the most important book you will ever read on how to reach your financial goals.

The Intelligent Investor, Rev. Ed

THE INSTANT NEW YORK TIMES AND USA TODAY BESTSELLER Reject the default path, define your priorities, and achieve lasting happiness with this transformative guide to your dream life—a life centered around the five types of wealth. “A powerful call to action to think deeply about what lights you up—and a guide for how to build a life of meaning and purpose.”—Tim Cook, CEO of Apple Throughout your life, you’ve been slowly indoctrinated to believe that money is the only type of wealth. In reality, your wealthy life may involve money, but in the end, it will be defined by everything else. After three years of research, personal experimentation, and thousands of interviews across the globe, Sahil Bloom has created a groundbreaking blueprint to build your life around five types of wealth: Time Wealth, Social Wealth, Mental Wealth, Physical Wealth, and Financial Wealth. A life of true fulfillment engages all five types—working dynamically, in concert across the seasons of your journey. Through powerful storytelling, science-backed practices, and actionable insights, in The 5 Types of Wealth, you’ll learn: • How to prioritize energy-creating tasks to unlock more time in your day • How to create deeper bonds and build a powerful network • How to engage your purpose to spark continuous growth • How to maximize health and vitality through three simple principles • How to achieve financial independence and define your version of “enough” No matter where you are on your path—a recent graduate, new parent, midlife warrior, retiree, or anything in between—The 5 Types of Wealth will help you act on your priorities to create an instant positive impact in your daily life, make better decisions, and design the life you’ve always dreamed of.

The 5 Types of Wealth

"Building a second brain is getting things done for the digital age. It's a ... productivity method for consuming, synthesizing, and remembering the vast amount of information we take in, allowing us to become more effective and creative and harness the unprecedented amount of technology we have at our disposal"--

Building a Second Brain

This is the second, revised and expanded edition. The first edition was published under the slightly longer title "How to Take Smart Notes. One Simple Technique to Boost Writing, Learning and Thinking - for Students, Academics and Nonfiction Book Writers". The key to good and efficient writing lies in the intelligent organisation of ideas and notes. This book helps students, academics and other knowledge workers to get more done, write intelligent texts and learn for the long run. It teaches you how to take smart notes and ensure they bring you and your projects forward. The Take Smart Notes principle is based on established psychological insight and draws from a tried and tested note-taking technique: the Zettelkasten. This is the first comprehensive guide and description of this system in English, and not only does it explain how it works, but also why. It suits students and academics in the social sciences and humanities, nonfiction writers and others who are in the business of reading, thinking and writing. Instead of wasting your time searching for your notes, quotes or references, you can focus on what really counts: thinking, understanding and developing new ideas in writing. Dr. Sönke Ahrens is a writer and researcher in the field of education and social science. He is the author of the award-winning book “Experiment and Exploration: Forms of World Disclosure” (Springer). Since its first publication, How to Take Smart Notes has sold more than 100,000 copies and has been translated into seven languages.

How to Take Smart Notes

A fresh, personal, and entertaining exploration of a topic that concerns all of us: how to be more productive at work and in every facet of our lives. Chris Bailey turned down lucrative job offers to pursue a lifelong dream—to spend a year performing a deep dive experiment into the pursuit of productivity, a subject he had been enamored with since he was a teenager. After obtaining his business degree, he created a blog to chronicle a year-long series of productivity experiments he conducted on himself, where he also continued his research and interviews with some of the world’s foremost experts, from Charles Duhigg to David Allen. Among the experiments that he tackled: Bailey went several weeks with getting by on little to no sleep; he cut out caffeine and sugar; he lived in total isolation for 10 days; he used his smartphone for just an hour a day for three months; he gained ten pounds of muscle mass; he stretched his work week to 90 hours; a late riser, he got up at 5:30 every morning for three months—all the while monitoring the impact of his experiments on the quality and quantity of his work. The Productivity Project—and the lessons Chris learned—are the result of that year-long journey. Among the counterintuitive insights Chris Bailey will teach you: · slowing down to work more deliberately; · shrinking or eliminating the unimportant; · the rule of three; · striving for imperfection; · scheduling less time for important tasks; · the 20 second rule to distract yourself from the inevitable distractions; · and the concept of productive procrastination. In an eye-opening and thoroughly engaging read, Bailey offers a treasure trove of insights and over 25 best practices that will help you accomplish more.

The Productivity Project

From a New York Times bestselling author, learn to master one of our economy’s rarest skills–the ability to focus–and achieve groundbreaking results. Deep work is the power to focus without distraction on a cognitively demanding task. It allows you to quickly master complicated information and produce better results in less time–a superpower in our increasingly competitive economy. In Deep Work, author and professor Cal Newport presents four rules for transforming your mind and habits to support this skill: 1. Work Deeply 2. Embrace Boredom 3. Quit Social Media 4. Drain the Shallows A mix of cultural criticism and actionable advice, Deep Work uses examples from Carl Jung to social media pioneers to demonstrate how great thinkers have focused their minds in a nonstop world.

Deep Work

Doing well with money isn’t necessarily about what you know. It’s about how you behave. And behavior is hard to teach, even to really smart people. Money—investing, personal finance, and business decisions—is typically taught as a math-based field, where data and formulas tell us exactly what to do. But in the real world people don’t make financial decisions on a spreadsheet. They make them at the dinner table, or in a meeting room, where personal history, your own unique view of the world, ego, pride, marketing, and odd incentives are scrambled together. In The Psychology of Money, award-winning author Morgan Housel shares 19 short stories exploring the strange ways people think about money and teaches you how to make better sense of one of life’s most important topics.

The Psychology of Money

The #1 New York Times bestseller. Over 20 million copies sold! Translated into 60+ languages! Tiny Changes, Remarkable Results No matter your goals, Atomic Habits offers a proven framework for improving--every day. James Clear, one of the world's leading experts on habit formation, reveals practical strategies that will teach you exactly how to form good habits, break bad ones, and master the tiny behaviors that lead to remarkable results. If you're having trouble changing your habits, the problem isn't you. The problem is your system. Bad habits repeat themselves again and again not because you don't want to change, but because you have the wrong system for change. You do not rise to the level of your goals. You fall to the level of your systems. Here, you'll get a proven system that can take you to new heights. Clear is known for his ability to distill complex topics into simple behaviors that can be easily applied to daily life and work. Here, he draws on the most proven ideas from biology, psychology, and neuroscience to create an easy-to-understand guide for making good habits inevitable and bad habits impossible. Along the way, readers will be inspired and entertained with true stories from Olympic gold medalists, award-winning artists, business leaders, life-saving physicians, and star comedians who have used the science of small habits to master their craft and vault to the top of their field. Learn how to: make time for new habits (even when life gets crazy); overcome a lack of motivation and willpower; design your environment to make success easier; get back on track when you fall off course; ...and much more. Atomic Habits will reshape the way you think about progress and success, and give you the tools and strategies you need to transform your habits--whether you are a team looking to win a championship, an organization hoping to redefine an industry, or simply an individual who wishes to quit smoking, lose weight, reduce stress, or achieve any other goal.

Atomic Habits

A new edition of the bestseller that has helped aspiring leaders worldwide advance their careers and step up to larger leadership roles. You aspire to lead with greater impact. The problem is you're busy executing on today's demands. You know you have to carve out time from your "day job" to build your leadership skills, but it’s easy to let immediate problems and old mindsets get in the way. Herminia Ibarra—one of the world's foremost experts on leadership—shows how individuals at all levels can step up to leadership by making small but crucial changes in their jobs, their networks, and themselves. In Act Like a Leader, Think Like a Leader, Ibarra offers advice to: Redefine your job in order to make more strategic contributions Diversify your network so that you connect to, and learn from, a wider range of stakeholders Become more playful with your self-concept, allowing your familiar—and possibly outdated—leadership style to evolve Ibarra turns the usual leadership advice—generate insight about yourself through reflection and analysis of your strengths and weaknesses—on its head by arguing that you must first act and experiment your way into trying new things. The valuable external perspective you gain from direct experiences and experimentation—which Ibarra calls outsight—provides new and critical information on what kind of work is important to you, how you should invest your time, why and which relationships matter, and, ultimately, who you want to become. Updated with new examples and self-assessments, this book gives you the tools to start acting like a leader and advancing your career to the next level.

Act Like a Leader, Think Like a Leader, Updated Edition of the Global Bestseller, With a New Preface

Most startups fail. But many of those failures are preventable. The Lean Startup is a new approach being adopted across the globe, changing the way companies are built and new products are launched. Eric Ries defines a startup as an organization dedicated to creating something new under conditions of extreme uncertainty. This is just as true for one person in a garage or a group of seasoned professionals in a Fortune 500 boardroom. What they have in common is a mission to penetrate that fog of uncertainty to discover a successful path to a sustainable business. The Lean Startup approach fosters companies that are both more capital efficient and that leverage human creativity more effectively. Inspired by lessons from lean manufacturing, it relies on “validated learning,” rapid scientific experimentation, as well as a number of counter-intuitive practices that shorten product development cycles, measure actual progress without resorting to vanity metrics, and learn what customers really want. It enables a company to shift directions with agility, altering plans inch by inch, minute by minute. Rather than wasting time creating elaborate business plans, The Lean Startup offers entrepreneurs—in companies of all sizes—a way to test their vision continuously, to adapt and adjust before it’s too late. Ries provides a scientific approach to creating and managing successful startups in a age when companies need to innovate more than ever.

The Lean Startup

The winner of the UK's Business Book of the Year Award for 2021, this is a groundbreaking exposé of the myths behind startup success and a blueprint for harnessing the things that really matter. What is the difference between a startup that makes it, and one that crashes and burns? Behind every story of success is an unfair advantage. But an Unfair Advantage is not just about your parents' wealth or who you know: anyone can have one. An Unfair Advantage is the element that gives you an edge over your competition. This groundbreaking book shows how to identify your own Unfair Advantages and apply them to any project. Drawing on over two decades of hands-on experience, Ash Ali and Hasan Kubba offer a unique framework for assessing your external circumstances in addition to your internal strengths. Hard work and grit aren't enough, so they explore the importance of money, intelligence, location, education, expertise, status, and luck in the journey to success. From starting your company, to gaining traction, raising funds, and growth hacking, The Unfair Advantage helps you look at yourself and find the ingredients you didn't realize you already had, to succeed in the cut-throat world of business.

The Unfair Advantage

Anyone who develops software for a living needs a proven way to produce it better, faster, and cheaper. The Productive Programmer offers critical timesaving and productivity tools that you can adopt right away, no matter what platform you use. Master developer Neal Ford not only offers advice on the mechanics of productivity-how to work smarter, spurn interruptions, get the most out your computer, and avoid repetition-he also details valuable practices that will help you elude common traps, improve your code, and become more valuable to your team. You'll learn to: Write the test before you write the code Manage the lifecycle of your objects fastidiously Build only what you need now, not what you might need later Apply ancient philosophies to software development Question authority, rather than blindly adhere to standards Make hard things easier and impossible things possible through meta-programming Be sure all code within a method is at the same level of abstraction Pick the right editor and assemble the best tools for the job This isn't theory, but the fruits of Ford's real-world experience as an Application Architect at the global IT consultancy ThoughtWorks. Whether you're a beginner or a pro with years of experience, you'll improve your work and your career with the simple and straightforward principles in The Productive Programmer.

The Productive Programmer

Practical Software Architecture Solutions from the Legendary Robert C. Martin (“Uncle Bob”) By applying universal rules of software architecture, you can dramatically improve developer productivity throughout the life of any software system. Now, building upon the success of his best-selling books Clean Code and The Clean Coder, legendary software craftsman Robert C. Martin (“Uncle Bob”) reveals those rules and helps you apply them. Martin’s Clean Architecture doesn’t merely present options. Drawing on over a half-century of experience in software environments of every imaginable type, Martin tells you what choices to make and why they are critical to your success. As you’ve come to expect from Uncle Bob, this book is packed with direct, no-nonsense solutions for the real challenges you’ll face–the ones that will make or break your projects. Learn what software architects need to achieve–and core disciplines and practices for achieving it Master essential software design principles for addressing function, component separation, and data management See how programming paradigms impose discipline by restricting what developers can do Understand what’s critically important and what’s merely a “detail” Implement optimal, high-level structures for web, database, thick-client, console, and embedded applications Define appropriate boundaries and layers, and organize components and services See why designs and architectures go wrong, and how to prevent (or fix) these failures Clean Architecture is essential reading for every current or aspiring software architect, systems analyst, system designer, and software manager–and for every programmer who must execute someone else’s designs. Register your product for convenient access to downloads, updates, and/or corrections as they become available.

Clean Architecture

Even bad code can function. But if code isn’t clean, it can bring a development organization to its knees. Every year, countless hours and significant resources are lost because of poorly written code. But it doesn’t have to be that way. Noted software expert Robert C. Martin presents a revolutionary paradigm with Clean Code: A Handbook of Agile Software Craftsmanship. Martin has teamed up with his colleagues from Object Mentor to distill their best agile practice of cleaning code “on the fly” into a book that will instill within you the values of a software craftsman and make you a better programmer–but only if you work at it. What kind of work will you be doing? You’ll be reading code–lots of code. And you will be challenged to think about what’s right about that code, and what’s wrong with it. More importantly, you will be challenged to reassess your professional values and your commitment to your craft. Clean Code is divided into three parts. The first describes the principles, patterns, and practices of writing clean code. The second part consists of several case studies of increasing complexity. Each case study is an exercise in cleaning up code–of transforming a code base that has some problems into one that is sound and efficient. The third part is the payoff: a single chapter containing a list of heuristics and “smells” gathered while creating the case studies. The result is a knowledge base that describes the way we think when we write, read, and clean code. Readers will come away from this book understanding How to tell the difference between good and bad code How to write good code and how to transform bad code into good code How to create good names, good functions, good objects, and good classes How to format code for maximum readability How to implement complete error handling without obscuring code logic How to unit test and practice test-driven development This book is a must for any developer, software engineer, project manager, team lead, or systems analyst with an interest in producing better code.

Clean Code

There are no easy decisions in software architecture. Instead, there are many hard parts--difficult problems or issues with no best practices--that force you to choose among various compromises. With this book, you'll learn how to think critically about the trade-offs involved with distributed architectures. Architecture veterans and practicing consultants Neal Ford, Mark Richards, Pramod Sadalage, and Zhamak Dehghani discuss strategies for choosing an appropriate architecture. By interweaving a story about a fictional group of technology professionals--the Sysops Squad--they examine everything from how to determine service granularity, manage workflows and orchestration, manage and decouple contracts, and manage distributed transactions to how to optimize operational characteristics, such as scalability, elasticity, and performance. By focusing on commonly asked questions, this book provides techniques to help you discover and weigh the trade-offs as you confront the issues you face as an architect. Analyze trade-offs and effectively document your decisions Make better decisions regarding service granularity Understand the complexities of breaking apart monolithic applications Manage and decouple contracts between services Handle data in a highly distributed architecture Learn patterns to manage workflow and transactions when breaking apart applications

Software Architecture: The Hard Parts

Today, software engineers need to know not only how to program effectively but also how to develop proper engineering practices to make their codebase sustainable and healthy. This book emphasizes this difference between programming and software engineering. How can software engineers manage a living codebase that evolves and responds to changing requirements and demands over the length of its life? Based on their experience at Google, software engineers Titus Winters and Hyrum Wright, along with technical writer Tom Manshreck, present a candid and insightful look at how some of the worldâ??s leading practitioners construct and maintain software. This book covers Googleâ??s unique engineering culture, processes, and tools and how these aspects contribute to the effectiveness of an engineering organization. Youâ??ll explore three fundamental principles that software organizations should keep in mind when designing, architecting, writing, and maintaining code: How time affects the sustainability of software and how to make your code resilient over time How scale affects the viability of software practices within an engineering organization What trade-offs a typical engineer needs to make when evaluating design and development decisions

Software Engineering at Google

MIT presents a concise primer on machine learning—computer programs that learn from data and the basis of applications like voice recognition and driverless cars. No in-depth knowledge of math or programming required! Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition—as well as some we don’t yet use every day, including driverless cars. It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpaydin offers a concise and accessible overview of “the new AI.” This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias. Alpaydin explains that as Big Data has grown, the theory of machine learning—the foundation of efforts to process that data into knowledge—has also advanced. He covers: • The evolution of machine learning • Important learning algorithms and example applications • Using machine learning algorithms for pattern recognition • Artificial neural networks inspired by the human brain • Algorithms that learn associations between instances • Reinforcement learning • Transparency, explainability, and fairness in machine learning • The ethical and legal implicates of data-based decision making A comprehensive introduction to machine learning, this book does not require any previous knowledge of mathematics or programming—making it accessible for everyday readers and easily adoptable for classroom syllabi.

Machine Learning, revised and updated edition

Endorsed by top AI authors, academics and industry leaders, The Hundred-Page Machine Learning Book is the number one bestseller on Amazon and the most recommended book for starters and experienced professionals alike.

The Hundred-page Machine Learning Book

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning

Kubernetes is the operating system of the cloud native world, providing a reliable and scalable platform for running containerized workloads. In this friendly, pragmatic book, cloud experts John Arundel and Justin Domingus show you what Kubernetes can do—and what you can do with it. You’ll learn all about the Kubernetes ecosystem, and use battle-tested solutions to everyday problems. You’ll build, step by step, an example cloud native application and its supporting infrastructure, along with a development environment and continuous deployment pipeline that you can use for your own applications. Understand containers and Kubernetes from first principles; no experience necessary Run your own clusters or choose a managed Kubernetes service from Amazon, Google, and others Use Kubernetes to manage resource usage and the container lifecycle Optimize clusters for cost, performance, resilience, capacity, and scalability Learn the best tools for developing, testing, and deploying your applications Apply the latest industry practices for security, observability, and monitoring Adopt DevOps principles to help make your development teams lean, fast, and effective

Cloud Native DevOps with Kubernetes

***Over a half-million sold! And available now, the Wall Street Journal Bestselling sequel The Unicorn Project*** “Every person involved in a failed IT project should be forced to read this book.”—TIM O'REILLY, Founder & CEO of O'Reilly Media “The Phoenix Project is a must read for business and IT executives who are struggling with the growing complexity of IT.”—JIM WHITEHURST, President and CEO, Red Hat, Inc. Five years after this sleeper hit took on the world of IT and flipped it on it's head, the 5th Anniversary Edition of The Phoenix Project continues to guide IT in the DevOps revolution. In this newly updated and expanded edition of the bestselling The Phoenix Project, co-author Gene Kim includes a new afterword and a deeper delve into the Three Ways as described in The DevOps Handbook. Bill, an IT manager at Parts Unlimited, has been tasked with taking on a project critical to the future of the business, code named Phoenix Project. But the project is massively over budget and behind schedule. The CEO demands Bill must fix the mess in ninety days or else Bill's entire department will be outsourced. With the help of a prospective board member and his mysterious philosophy of The Three Ways, Bill starts to see that IT work has more in common with a manufacturing plant work than he ever imagined. With the clock ticking, Bill must organize work flow streamline interdepartmental communications, and effectively serve the other business functions at Parts Unlimited. In a fast-paced and entertaining style, three luminaries of the DevOps movement deliver a story that anyone who works in IT will recognize. Readers will not only learn how to improve their own IT organizations, they'll never view IT the same way again. “This book is a gripping read that captures brilliantly the dilemmas that face companies which depend on IT, and offers real-world solutions.”—JEZ HUMBLE, Co-author of Continuous Delivery, Lean Enterprise, Accelerate, and The DevOps Handbook

The Phoenix Project

Learning to build distributed systems is hard, especially if they are large scale. It's not that there is a lack of information out there. You can find academic papers, engineering blogs, and even books on the subject. The problem is that the available information is spread out all over the place, and if you were to put it on a spectrum from theory to practice, you would find a lot of material at the two ends, but not much in the middle. That is why I decided to write a book to teach the fundamentals of distributed systems so that you don’t have to spend countless hours scratching your head to understand how everything fits together. This is the guide I wished existed when I first started out, and it's based on my experience building large distributed systems that scale to millions of requests per second and billions of devices. If you develop the back-end of web or mobile applications (or would like to!), this book is for you. When building distributed systems, you need to be familiar with the network stack, data consistency models, scalability and reliability patterns, and much more. Although you can build applications without knowing any of that, you will end up spending hours debugging and re-designing their architecture, learning lessons that you could have acquired in a much faster and less painful way.

Understanding Distributed Systems

Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI. Learn how to empower AI to work for you. This book explains: The structure of the interaction chain of your program's AI model and the fine-grained steps in between How AI model requests arise from transforming the application problem into a document completion problem in the model training domain The influence of LLM and diffusion model architecture—and how to best interact with it How these principles apply in practice in the domains of natural language processing, text and image generation, and code

Prompt Engineering for Generative AI

Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. This book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, evaluating, deploying, and updating models Developing a monitoring system to quickly detect and address issues your models might encounter in production Architecting an ML platform that serves across use cases Developing responsible ML systems

Designing Machine Learning Systems

Step into the world of LLMs with this practical guide that takes you from the fundamentals to deploying advanced applications using LLMOps best practices Key Features Build and refine LLMs step by step, covering data preparation, RAG, and fine-tuning Learn essential skills for deploying and monitoring LLMs, ensuring optimal performance in production Utilize preference alignment, evaluation, and inference optimization to enhance performance and adaptability of your LLM applications Book DescriptionArtificial intelligence has undergone rapid advancements, and Large Language Models (LLMs) are at the forefront of this revolution. This LLM book offers insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps best practices. The guide walks you through building an LLM-powered twin that’s cost-effective, scalable, and modular. It moves beyond isolated Jupyter notebooks, focusing on how to build production-grade end-to-end LLM systems. Throughout this book, you will learn data engineering, supervised fine-tuning, and deployment. The hands-on approach to building the LLM Twin use case will help you implement MLOps components in your own projects. You will also explore cutting-edge advancements in the field, including inference optimization, preference alignment, and real-time data processing, making this a vital resource for those looking to apply LLMs in their projects. By the end of this book, you will be proficient in deploying LLMs that solve practical problems while maintaining low-latency and high-availability inference capabilities. Whether you are new to artificial intelligence or an experienced practitioner, this book delivers guidance and practical techniques that will deepen your understanding of LLMs and sharpen your ability to implement them effectively.What you will learn Implement robust data pipelines and manage LLM training cycles Create your own LLM and refine it with the help of hands-on examples Get started with LLMOps by diving into core MLOps principles such as orchestrators and prompt monitoring Perform supervised fine-tuning and LLM evaluation Deploy end-to-end LLM solutions using AWS and other tools Design scalable and modularLLM systems Learn about RAG applications by building a feature and inference pipeline Who this book is for This book is for AI engineers, NLP professionals, and LLM engineers looking to deepen their understanding of LLMs. Basic knowledge of LLMs and the Gen AI landscape, Python and AWS is recommended. Whether you are new to AI or looking to enhance your skills, this book provides comprehensive guidance on implementing LLMs in real-world scenarios

LLM Engineer's Handbook

Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures

Designing Data-Intensive Applications

“This is the most comprehensive textbook to date on building LLM applications - all essential topics in an AI Engineer's toolkit." - Jerry Liu, Co-founder and CEO of LlamaIndex (THE BOOK WAS UPDATED ON OCTOBER 2024) With amazing feedback from industry leaders, this book is an end-to-end resource for anyone looking to enhance their skills or dive into the world of AI and develop their understanding of Generative AI and Large Language Models (LLMs). It explores various methods to adapt "foundational" LLMs to specific use cases with enhanced accuracy, reliability, and scalability. Written by over 10 people on our Team at Towards AI and curated by experts from Activeloop, LlamaIndex, Mila, and more, it is a roadmap to the tech stack of the future. The book aims to guide developers through creating LLM products ready for production, leveraging the potential of AI across various industries. It is tailored for readers with an intermediate knowledge of Python. What's Inside this 470-page Book (Updated October 2024)? - Hands-on Guide on LLMs, Prompting, Retrieval Augmented Generation (RAG) & Fine-tuning - Roadmap for Building Production-Ready Applications using LLMs - Fundamentals of LLM Theory - Simple-to-Advanced LLM Techniques & Frameworks - Code Projects with Real-World Applications - Colab Notebooks that you can run right away Community access and our own AI Tutor Table of Contents - Chapter I Introduction to Large Language Models - Chapter II LLM Architectures & Landscape - Chapter III LLMs in Practice - Chapter IV Introduction to Prompting - Chapter V Retrieval-Augmented Generation - Chapter VI Introduction to LangChain & LlamaIndex - Chapter VII Prompting with LangChain - Chapter VIII Indexes, Retrievers, and Data Preparation - Chapter IX Advanced RAG - Chapter X Agents - Chapter XI Fine-Tuning - Chapter XII Deployment and Optimization Whether you're looking to enhance your skills or dive into the world of AI for the first time as a programmer or software student, our book is for you. From the basics of LLMs to mastering fine-tuning and RAG for scalable, reliable AI applications, we guide you every step of the way.

Building LLMs for Production

AI has acquired startling new language capabilities in just the past few years. Driven by the rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend enables the rise of new features, products, and entire industries. With this book, Python developers will learn the practical tools and concepts they need to use these capabilities today. You'll learn how to use the power of pretrained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; build systems that classify and cluster text to enable scalable understanding of large numbers of text documents; and use existing libraries and pretrained models for text classification, search, and clusterings. This book also shows you how to: Build advanced LLM pipelines to cluster text documents and explore the topics they belong to Build semantic search engines that go beyond keyword search with methods like dense retrieval and rerankers Learn various use cases where these models can provide value Understand the architecture of underlying Transformer models like BERT and GPT Get a deeper understanding of how LLMs are trained Optimize LLMs for specific applications with methods such as generative model fine-tuning, contrastive fine-tuning, and in-context learning

Hands-On Large Language Models

Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models. The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach. AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You'll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications. Understand what AI engineering is and how it differs from traditional machine learning engineering Learn the process for developing an AI application, the challenges at each step, and approaches to address them Explore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they work Examine the bottlenecks for latency and cost when serving foundation models and learn how to overcome them Choose the right model, dataset, evaluation benchmarks, and metrics for your needs Chip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She's the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI. AI Engineering builds upon and is complementary to Designing Machine Learning Systems (O'Reilly).

AI Engineering

Books, a collection of books created by Amogh AN. With books like Site Reliability Engineering, Crush It!, Build, Thinking in Systems, How To Be The Love You Seek, Why Men Love Bitches, Storytelling with Data, Artificial Intelligence, Build a Large Language Model (From Scratch), Generative Deep Learning, Hands-On Generative AI with Transformers and Diffusion Models, The Singularity Is Nearer, The Guest List, The Time Machine, The Go Programming Language, The Black Swan, A Random Walk Down Wall Street: The Best Investment Guide That Money Can Buy (13th Edition), Natural Language Processing with Transformers, Revised Edition, Mathematics for Machine Learning, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Practical MLOps, Building Microservices, Rust for Rustaceans, The Almanack of Naval Ravikant, The 5 Am Club, Fundamentals of Data Engineering, The Intelligent Investor, Rev. Ed, The 5 Types of Wealth, Building a Second Brain, How to Take Smart Notes, The Productivity Project, Deep Work, The Psychology of Money, Atomic Habits, Act Like a Leader, Think Like a Leader, Updated Edition of the Global Bestseller, With a New Preface, The Lean Startup, The Unfair Advantage, The Productive Programmer, Clean Architecture, Clean Code, Software Architecture: The Hard Parts, Software Engineering at Google, Machine Learning, revised and updated edition, The Hundred-page Machine Learning Book, Deep Learning, Cloud Native DevOps with Kubernetes, The Phoenix Project, Understanding Distributed Systems, Prompt Engineering for Generative AI, Designing Machine Learning Systems, LLM Engineer's Handbook, Designing Data-Intensive Applications, Building LLMs for Production, Hands-On Large Language Models, AI Engineering