A New York Times bestseller!
They burned her home.
They stole her brother and sister.
But vengeance is following.
Shy South hoped to bury her bloody past and ride away smiling, but she'll have to sharpen up some bad old ways to get her family back, and she's not a woman to flinch from what needs doing. She sets off in pursuit with only a pair of oxen and her cowardly old step father Lamb for company. But it turns out Lamb's buried a bloody past of his own. And out in the lawless Far Country the past never stays buried.
Their journey will take them across the barren plains to a frontier town gripped by gold fever, through feud, duel and massacre, high into the unmapped mountains to a reckoning with the Ghosts. Even worse, it will force them into alliance with Nicomo Cosca, infamous soldier of fortune, and his feckless lawyer Temple, two men no one should ever have to trust . . .

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 pre-trained 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 amounts of text documents; and use existing libraries and pre-trained 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
Understanding how different methods of fine-tuning optimize LLMs for specific applications (generative model fine-tuning, contrastive fine-tuning, in-context learning, etc.)
Optimize LLMs for specific applications with methods such as generative model fine-tuning, contrastive fine-tuning, and in-context learning

当我们谈到金钱时，每个人都想知道自己在储蓄、投资等财务决策上做得对不对。金融行业提供的许多答案常常基于观点和猜测，而不是数据和实证。 在本书中，数据科学家和投资专家尼克·马吉奥利通过数据分析引导我们客观地看待投资，构建正确的投资心态和理财方式。他建议我们不要只依靠直觉来投资或者盲目地接受投资建议，而要通过客观的数据分析来形成自己的投资策略。 作者提出了持续买入的投资策略，即什么时候买入资产、买多少、买什么资产都不重要，估值是高还是低不重要，处于牛市还是熊市也不重要——对积累财富来说，重要的是买入一组多样化的可创收资产，并且坚持持续买入。 只要持续买入，你的财富的雪球就会越滚越大。

想象一下，凌晨三点，你焦急地守着生病的孩子。如果这时候，手头有专家的建议能帮你度过这个夜晚，那该多好啊！
《西尔斯健康育儿百科》是一本关于儿童健康的综合指南。对于每一种儿童疾病和紧急情况，从普通感冒、耳朵痛、长牙到学习障碍、接种疫苗，从新生儿到青春期的所有发育过程，你都能从中找到对应的处理方案，包括什么时候你该担心、什么时候该去看医生、什么时候只需在家治疗。
西尔斯医生还为我们提供手把手的指导，告诉我们常规检查时应该注意哪些方面、如何促进孩子的健康成长、如何设计全家的健康计划、如何更有效率地与儿科医生合作。
《西尔斯健康育儿百科》以全面、可信、易于理解、可操作性强而著称，本书内容丰富翔实，每一个希望孩子健康快乐成长的家庭都应必备。

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 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 engineeringLearn the process for developing an AI application, the challenges at each step, and approaches to address themExplore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they workExamine the bottlenecks for latency and cost when serving foundation models and learn how to overcome themChoose the right model, dataset, evaluation benchmarks, and metrics for your needsChip 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) .

This revolutionary approach to cleaning and organizing helps free you from feeling ashamed or overwhelmed by a messy home.
If you’re struggling to stay on top of your to-do list, you probably have a good reason: anxiety, fatigue, depression, ADHD, or lack of support. For therapist KC Davis, the birth of her second child triggered a stress-mess cycle. The more behind she felt, the less motivated she was to start. She didn’t fold a single piece of laundry for seven months. One life-changing realization restored her sanity—and the functionality of her home: You don’t work for your home; your home works for you.
In other words, messiness is not a moral failing. A new sense of calm washed over her as she let go of the shame-based messaging that interpreted a pile of dirty laundry as “I can never keep up” and a chaotic kitchen as “I’m a bad mother.” Instead, she looked at unwashed clothes and thought, “I am alive,” and at stacks of dishes and thought, “I cooked my family dinner three nights in a row.”
Building on this foundation of self-compassion, KC devised the powerful practical approach that has exploded in popularity through her TikTok account, @domesticblisters. The secret is to simplify your to-do list and to find creative workarounds that accommodate your limited time and energy. In this book, you’ll learn exactly how to customize your cleaning strategy and rebuild your relationship with your home, including:
-How to see chores as kindnesses to your future self, not as a reflection of your worth
-How to start by setting priorities
-How to stagger tasks so you won’t procrastinate
-How to clean in quick bursts within your existing daily routine
-How to use creative shortcuts to transform a room from messy to functional
With KC’s help, your home will feel like a sanctuary again. It will become a place to rest, even when things aren’t finished. You will move with ease, and peace and calm will edge out guilt, self-criticism, and endless checklists. They have no place here.

Between 2012 and 2023, Ian Graham worked as Liverpool FC's Director of Research. His tenure coincided with the club’s greatest period of success since the 1980s, including winning the Premier League in 2020 – Liverpool’s first league title after an agonising three decades.
Here for the first time, Graham reveals the fascinating data that informed some of the club’s most pivotal moments of the past decade, from the appointment of Jurgen Klopp as manager in 2015 to the signing of Mohamed Salah in 2017. Along the way, he shares groundbreaking insight into the modern game, including how a season largely played behind closed doors transformed our understanding of home team advantage, or why the GOAT (greatest of all time) might not be who you think. And, in a game that is increasingly dominated by an elite few, Graham charts a path for the future where a data-savvy competitor will always find the edge.

《巴別塔學院》奇幻敘事＋《黃色臉孔》犀利諷刺 「入此門者，必當放棄一切希望，」 不論這道門究竟是通往地獄，或是人間的學術殿堂── 英國圖書獎＋星雲獎＋軌跡獎得主、TIME 100 Next次世代百大人物 匡靈秀 全球矚目最新大作 ✪臺灣譯本與美國原書同步出版 ✪全球最大閱讀社群Goodreads網站23萬位讀者加入書單待閱 ✪2025年美國奇幻文學類最高期待度新書 ✫故事簡介✫ Katabasis /ˌkatəˈbeɪsɪs/（名詞，古希臘語） 釋義：英雄前往地下世界的旅程 「我是愛麗絲‧羅，我是劍橋大學的研究生，我主修分析魔法， 我現在人在地獄裡，一切都很順利，很順利，很順利……」 魔法是研究世上萬物運行法則的艱深學門，也是大學內深受重視且充滿菁英色彩的科系。博士生愛麗絲在劍橋大學的分析魔法學研究所勤奮地修課、寫論文、做助教，沒有休閒興趣、不知假期為何物，終於苦盡甘來快要熬到畢業。 然而，她的指導老師、德高望重的格萊姆斯教授偏偏在此時因為魔法實驗中的失誤而慘死，肉體四分五裂，靈魂則落入地獄。眼見自己的學術生涯恐怕就要無疾而終，愛麗絲決定不擇手段把教授帶回人世，好主持她的論文口試、替她寫完求職推薦信。 「魔法學者最不擅長通過地獄的關卡。他們從來不認為自己有錯， 自以為與眾不同，只想要比別人更優秀，現在他們有機會證明了── 地獄對身處其中的人來說並沒有那麼糟，這裡就是他們心之所向。」 所上的天才型學生彼得也想要出一份力拯救教授，儘管摸不清他的動機，兩人的關係又劍拔弩張，愛麗絲仍勉為其難讓彼得加入這趟旅程。地獄分為傲慢、欲望、貪婪、憤怒等八殿，各自收容不同種類的罪人，有的看似與大學校園一樣和樂，有的卻是布滿殘虐機關的荒漠。他們必須在其中通過層層考驗，才能找到教授的靈魂，請求冥王准許他回到人間；若不能在十四天內歸來，他們自己也將永遠受困地獄。 不論挑戰多大、風險多高，愛麗絲都相信這是維繫魔法學者職涯、應用畢生所學的唯一出路，所以絕對值得不計代價走完。然而，地獄深處時而熟悉、時而扭曲的異變景象，不僅逐漸動搖他們的初衷，也衝擊他們對魔法、世界與自我的理解…… ✫首波好評✫ 「匡靈秀創造出一趟處處機鋒、殘酷駭人的旅程，充滿豐富的典故與幻象，直擊學術界內的各種風險與權力結構，但也從不忽視擁有一顆脆弱而人性的心靈對我們而言的意義。」──莉‧巴度格／《太陽召喚》作者 「探索了理想抱負、悲傷情緒、人際關係，讓這部小說能夠迎接更廣大普世的讀者群。」──《圖書館期刊》（星號推薦） 「一部結構細密的小說，清楚對焦於學術界裡的醜惡與美善。『發現』所帶來的振奮是無所不在的吸引力，即使愛麗絲在此發現的是地獄。一份學識與文采並具的宣言，探討的對象既是學術界，也是它的黑暗面。」──《柯克斯書評》（星號推薦）

烟囱林立，机器轰鸣，联合王国迎来崭新时代，旧的恩怨却依然根深蒂固。
本的狂欢背后，大变革的种子已经埋下，贫民窟怒潮暗涌，倾尽天下财富都扑之不灭，随时可能烧光腐朽的政权和文明的果实。魔法势力不甘退场，第一法师仍在幕后操弄王国的命脉。群山之巅，有人睁开预见未来的千里眼，但改变未来谈何容易？刀剑铮鸣，血肉横飞，生死决斗。新旧交替之时，命运翻开了未知的一页

颠覆传统的新式奇幻，走在大势潮流的最尖端，以独有的尖锐讥讽笔调，谱写震撼人心的奇幻巨作！《纽约时报》畅销书作家乔·阿克罗比最新力作——“第一律法”三部曲续作“疯狂年代”强势来袭！
阴谋似蛛网密布，背叛如家常便饭，和平成了另一种形式的战场。
年轻的至高王奥索戴上沉重的王冠，在政治迷宫中摸索出路，撞得头破血流；商海奇女子瑟雯·唐·格洛塔在投资失败、声誉扫地的泥淖中挣扎，以图东山再起；幼狮和魁狼暗通款曲，各取所需；瑞卡不得不面对天赐和天谴的残酷考验——若不能驯服千里眼，精神与肉体必遭毁灭性的反噬；破坏者仍在暗影中蛰伏，大变革的幕后黑手慢慢揭开面纱一角......
日秩序面临颠覆，权力的玩家终将发现:世间从无恒久的盟约、不灭的友谊，亦无永续的太平。

Putting a Witcher twist on family life, this delightful collection of comics features Geralt of Rivia as your average monster-slaying dad, trying to raise young Ciri to be a good kid while teaching her all about life as a witcher—a perfect gift for parents and The Witcher fans of all ages.

Young Ciri's extensive training at Kaer Morhen, the witchers' stronghold, includes everything she needs to learn to survive the many threats in her world . . . as well as all the antics and fun of a little girl bonding with her adoptive dad and family.

With help from Geralt and Ciri's closest companions—including the motherly magic of Yennefer of Vengerberg and the wisdom of uncle Vesemir—these adorable tales of a non-traditional family will make you laugh, make you sigh, and make you realize that raising a Little Witcher is not that different from raising any other kid. Sure, bedtime stories might include warnings of monsters who fart when surprised, and Geralt might invoke the Witcher Code to get Ciri to brush her teeth or clean her room, but even the formidable White Wolf knows to surrender when it comes to bedtime battles or Afternoon Tea with the toys.

The Little Witcher is perfect

The Witcher fans raising little Ciris of their ownReaders who believe in the power of chosen familiesBaby showers (don’t forget Dad!)Father’s Day or Mother’s Day giftsPlayers looking for a light-hearted twist on their favorite gameFamilies who like cute, magical comics The Witcher fans looking to spend more time in Kaer Morhen

本书是青年学者林垚近年来哲学、科普、学术与公共讨论文章汇集，也是作者的第一部独立文集。全书由三个版块组成，上卷《究穷象塔屠龙术》集中于道德哲学及政治哲学方面的探讨，中卷《搅梦频劳西海月》聚焦美国政治，下卷《蛇毛兔角多鸡犬》漫谈与科学哲学或宗教哲学相关的话题。作者行文逻辑严密，文风犀利，从哲学思辩的高度对诸多公共话题进行了深入浅出的讲解，直戳要点，并且呈现出多重的思考维度。

PySpark in Action is a carefully engineered tutorial that helps you use PySpark to deliver your data-driven applications at any scale. This clear and hands-on guide shows you how to enlarge your processing capabilities across multiple machines with data from any source, ranging from Hadoop-based clusters to Excel worksheets. You’ll learn how to break down big analysis tasks into manageable chunks and how to choose and use the best PySpark data abstraction for your unique needs. By the time you’re done, you’ll be able to write and run incredibly fast PySpark programs that are scalable, efficient to operate, and easy to debug.
what's inside
Packaging your PySpark code
Managing your data as it scales across multiple machines
Re-writing Pandas, R, and SAS jobs in PySpark
Troubleshooting common data pipeline problems
Creating reliable long-running jobs