Python machine learning

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Diggers are noisy, strong and big. Diggers can carry and push and dig.Be dazzled by the diggers and their daffy animal drivers in this bright and bouncy picture book.Zippy wordplay, zappy art, plus a helpful picture dictionary make learning about machines fun!

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Pizzoli’s Good Night Owl is a modern twist on the classic tale of not wanting to go to bed. Teaching the importance of learning to accept things beyond one’s control, YOTTOY’s Owl Soft Toy stays true

Mem Fox's poignant tale of what bonds us all, now available as a board bookEvery day all over the world, children are laughing and crying, playing and learning, eating and sleeping. They may not look

Hike through the rainforest, scale a mountain, swim across the ocean, and still make it home for dinner. Rhyme and repetition make learning fun in this terrain-traversing title that encourages movement and reinforces animal sounds.

Can you boogie? Down in the Indian jungle, the children and animals are learning about actions like leaping, stomping, shaking and flapping while meeting different jungle creatures.

Migrate from pandas and scikit-Learn to PySpark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools. Distributed Machine Learning with PySpark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn) to big data processing and machine learning with PySpark. You will learn to translate Python code from pandas/scikit-learn to PySpark to preprocess large volumes of data and build, train, test, and evaluate popular machine learning algorithms such as linear and logistic regression, decision trees, random forests, support vector machines, Na鴳e Bayes, and neural networks. After completing this book, you will understand the foundational concepts of data preparation and machine learning and will have the skills necessary to apply these methods using

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the ne

Python機器學習第三版（下） Python Machine Learning - Third Edition 第三版－最新修訂版，新增TensorFlow 2、GAN和強化學習等實用內容 使用Python的scikit-learn和TensorFlow 2融會貫通機器學習與深度學習 循序漸進、由淺入深，好評熱銷再進化！最新修訂的《Python機器學習第三版》是一本不容錯過的全方位指南，也是讀者

Deep learning is revolutionizing how machine translation systems are built today. This book introduces the challenge of machine translation and evaluation - including historical, linguistic, and applied context -- then develops the core deep learning methods used for natural language applications. Code examples in Python give readers a hands-on blueprint for understanding and implementing their own machine translation systems. The book also provides extensive coverage of machine learning tricks, issues involved in handling various forms of data, model enhancements, and current challenges and methods for analysis and visualization. Summaries of the current research in the field make this a state-of-the-art textbook for undergraduate and graduate classes, as well as an essential reference for researchers and developers interested in other applications of neural methods in the broader field of human language processing.

《Python機器學習》面向機器學習新手，主要內容如下： ● Python機器學習的一些基本庫，包括NumPy、Pandas和matplotlib庫 ● 常見的機器學習算法，包括回歸、聚類、分類和異常檢測 ● 使用Python和Scikit-learn庫進行機器學習 ● 將機器學習模型部署為Web服務 ● 使用Microsoft Azure Machine Learning Studio進行機器學

When machine learning engineers work with data sets, they may find the results aren't as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data's features to better capture the nature of the problem. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks.

Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabiliti

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Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It il

Machine learning applications autonomously reason about data at massive scale. It's important that they remain responsive in the face of failure and changes in load. But machine learning systems are d

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, inc

Tackle the real-world complexities of modern machine learning with innovative, cutting-edge, techniquesAbout This BookFully-coded working examples using a wide range of machine learning libraries and

By teaching you how to code machine-learning algorithms using a test-driven approach, this practical book helps you gain the confidence you need to use machine learning effectively in a business envir

The goal of machine learning is to program computers to use example data or pastexperience to solve a given problem. Many successful applications of machine learning exist already,including systems th

An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in r

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems

An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology a

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems

Machine Learning: Discriminative and Generative covers the main contemporary themes and tools in machine learning ranging from Bayesian probabilistic models to discriminative support-vector machines.

只要看看資料，就能預測未來？時間序列分析，讓數據告訴你答案。銷售額為什麼忽高忽低？廣告真的有效，還是只是運氣好？預算該怎麼分配，才能帶來最大的成效？這些問題，其實都藏在「隨時間變化的資料」之中★東大專家親授！第一本專為「商務實戰」打造的時序分析漫畫教程。★拒絕死背公式！從 ARIMA 模型到 mROI 評估，看漫畫就能學會預測未來的技術。從基礎觀念到 ARIMA、預測建模一次學會銷售分析、廣告成效評估與預算最佳化Python 實作 × 商務情境 × 圖像化解說「數據真的能預測未來嗎？」家電大廠資料分析師帶你從零開始，把冰冷數據變成獲利決策！銷售額增減、庫存變動、廣告成效……我們的身邊充滿了「隨時間變化」的資料，但你懂得如何利用它們嗎？ 時間序列分析，常被認為是困難又抽象的統計領域，但其實，它正是解決現代商務問題的關鍵工具本書透過有趣的家電大廠漫畫故事，帶領讀者深入淺出地掌握「時間序列分析」，從最基礎的圖表化觀察，到進階的 ARIMA 模型建立，甚至連業界最關心的「廣告延續效應」與「預算分配最佳化」都一次講清楚！【本書特色】•掌握趨勢： 拆解數據中的季節性與趨勢，不再被短期波動誤導。•精準預測： 運用過往資料建模，科學化預測未來銷售走向。•廣告優化： 分析不同媒介的廣告貢獻度，找出最佳投放時機與預算比例。•完整實務流程：從資料觀察 → 建模 → 驗證 → 預測 → 最佳化，讓理論直接轉化為工作生產力。•視覺化圖解： 複雜的迴歸與建模過程轉化為圖表，用圖表與插圖理解趨勢、季節性與模型概念，一眼掌握性質與重點。•商務導向： 內容涵蓋廣告成效驗證、因果推論與預算最佳化，聚焦銷售，極具商務價值。•學習檢測： 每一章結尾設有重點延伸說明，且附完整 Notebook 與資料檔，可直接動手實作，引導你從初學者走向中級。•名師加持： 專家級作者親力親為，細緻講解如何深入挖掘數據價值。數據分析真的能改善現況嗎？這是一本為「想解決問題的人」寫的書！很多人看到「時間序列分析」就聯想到複雜的微積分與統計術語，因而望而卻步。《圖解時間序列分析》是一本專為不擅長數學、卻想把資料分析用在實務中的讀者所寫的入門到應用書籍。透過漫畫故事與大量圖解，帶你一步步理解時間序列分析的核心概念，並實際應用在銷售分析、廣告成效評估、未來預測與廣告預算最佳化等真實商務場景本書透過圖解＋漫畫＋Python 實作的方式

"《生成式AI × 穩健提示爬蟲技術 I》數據抓取篇★從Colab、Anaconda 到 Jupyter Kernel，一步步把 Python 環境建好、跑起來。★用Pandas × PyArrow × Polars，把資料整理做得更快、更穩，也更貼近實務。★透過提示工程和情境工程，讓 ChatGPT、Claude 成為你的程式開發夥伴。★結合Perplexity Comet＋爬蟲＋寄信＋行事曆，把資訊蒐集到自動化流程一次串起。★多模態OCR × Gemini和Groq API，把圖片文字直接變成可分析的結構化資料。你學過Python，卻常卡在「環境裝不好、資料處理很慢、爬蟲一下就被擋、AI 工具只會聊天」嗎？本書以「做得出成果」為主軸，帶你從 Google Colab、Anaconda、Jupyter Notebook 與 Kernel 建置開始，快速建立穩定、可重現的開發環境。接著用 Pandas 打底，涵蓋多欄多列取值、資料框合併、字串提取與條件篩選，並延伸到 PyArrow 與 Polars，讓你面對較大的資料量也能跑得快、跑得順。更關鍵的是，本書把提示工程與情境工程結合「直覺式開發」：教你如何用 ChatGPT和Claude 協作寫程式、除錯；再搭配 Perplexity Comet 進行摘要與探勘，串接 Gmail、Google 行事曆、SMTP、Resend 寄信，打造可實際應用的自動化資訊流程。最後以多模態 API（Gemini、Groq）＋中文 OCR（PaddleOCR、EasyOCR）＋爬蟲實戰進行總結，範例涵蓋電商、股價、ESG、重大訊息、交易指標等案例，並附上Gemini AI 教育版證照準備重點，適合資料分析初學者、稽核、採購、研究人員、跨域工作者，以及想把 AI 變成生產力的人。專業推薦崑山科技大學 電機工程系系主任─林俊良 博士住宅消保會創會理事長─吳翃毅博士錸成科技有限公司執行長、嘉南藥理大學兼任助理教授─凃春愛 博士伽碩職訓中心執行長─郭明洽昱創企管顧問有限公司總經理─裴有恆元大銀行系統開發部專業副理─陳尚瑋極策網路行銷有限公司總經理、崑山科技大學房地產開發與管理系講師─蔡宗甫"

https://youtu.be/9E_99Kmgdj0☆★☆好評熱銷再上市☆★☆【書籍特點】● x86組合語言基礎知識，通用暫存器、常用指令，記憶體分頁● 指標的實現原理，指標組成、相關操作，以及Go語言的unsafe套件等。● 函數、堆疊框佈局、呼叫約定、變數逃逸、Function Value、閉包、defer和panic● 方法，實現原理，接收者類型、Method Value和組合式繼承● 介面，Go語言動態特性，括裝箱、方法集、動態派發、類型斷言、類型系統和反射● goroutine實現，GMP模型、goroutine的建立與退出、排程迴圈、先佔式排程、timer、netpoller和監控執行緒● 同步，記憶體亂數、原子指令、自旋鎖、runtime互斥鎖和訊號量、ync.Mutex、hannel● 堆積記憶體管理，heapArena、mspan，mallocgc函數、C的三色抽象、寫入屏障● 堆疊記憶體管理，goroutine堆疊的分配、增長、收縮和釋放【內容簡介】想要有C語言的高速和Python的便利性嗎？Go是最棒的選擇，Go語言在語言層面上支援程式碼協同（goroutine）和通道（channel），這使得併發程式設計變得非常簡單和高效。在多核CPU時代，高效的併發程式設計已經成為軟體開發的必備技能之一。此外Go語言的語法設計簡單清晰，注重可讀性和簡潔性，使得初學者能夠快速入門。Go語言的標準函數庫提供了豐富的功能，可以直接使用，避免了一些繁瑣的工作。另外在跨平臺支援方面，Go語言的編譯器可以將程式編譯成機器碼，支援跨平臺編譯，可以在不同的作業系統和硬體平臺上運行，這對於開發跨平臺應用程式非常有幫助。Go最有名的就是其高性能。Go語言的編譯速度非常快，生成的二進位檔案也很小，運行速度也非常快。Go語言在一些性能要求較高的應用領域，如Web服務、巨量資料處理、容器等方面表現出色。另外其開發效率高，Go語言在一些方面與Python、Ruby等動態語言相似，但又具有靜態語言的優點。Go語言的編譯器能夠在編譯期間捕捉一些錯誤，這使得程式的穩固性更高。同時，Go語言的工具鏈非常完善，如go fmt、go vet等工具可以幫助開發者規範程式，提高開發效率。

☆★☆好評熱銷再上市☆★☆★打不過AI就加入他！建立自己的GPT產品★手刻ChatGPT從NLP基礎開始★PyTorch親手打造RNN、LSTM、GRU、BERT、GPT★Attention、Transformer詳解★用GPT做出自動詩詞創作平台全書分為4篇：「自然語言處理基礎篇」、「PyTorch入門篇」、「用PyTorch完成自然語言處理任務篇」和「實戰篇」。第1篇包含自然語言處理的背景知識、常用的開放資源、架設Python環境以及使用Python完成自然語言處理的基礎任務。第2篇包含PyTorch環境設定和PyTorch的基本使用，以及機器學習的一些基本原理和工作方法。第3篇介紹如何使用PyTorch完成自然語言處理任務，各介紹一種模型，包括分詞(又稱斷詞)、RNN、詞嵌入、Seq2seq、注意力機制、Transformer、預訓練語言模型。第4篇是實戰篇，第分別講解自然語言理解的任務和自然語言生成的任務，即「中文地址解析」和「詩句補充」。這兩個任務綜合了前面各章的知識，並展示了從資料下載、處理、模型到使用者互動介面開發的全部流程。從入門到專案實戰，打下你在NLP這一門最紅技術上的紮實基礎。? 目標讀者■有一定程式設計基礎的電腦同好。■希望學習機器學習和自然語言處理的人。■電腦及其相關專業的學生。■對自然語言處理領域感興趣的研究者。■對自然語言處理感興趣並樂於實踐的人。

「這本書正是我當年提出『資料科學家』這個職稱時，所希望能擁有的一本書。如果您希望投身資料科學╱工程、人工智慧，或機器學習領域，就該從這裡開始。」 ── DJ Patil 博士，美國首任首席資料科學家 身為一位有志成為資料科學家的讀者，能夠理解各類組織為何仰賴資料來做出關鍵決策──無論是公司在設計網站、還是市政府在改善公共服務，或者是科學家在致力於阻止疾病擴散。而您也希望具備將雜亂資料整理為可行洞見的能力。我們將這整個過程稱為「資料科學生命週期」：也就是從資料的收集、整理、分析，到導出結論的完整流程。 本書是第一本涵蓋程式設計與統計兩大基礎技能、並貫穿整個資料科學生命週期的書籍。本書的對象包括希望成為資料科學家的人、與資料科學家共事的專業人士，以及希望跨越「技術╱非技術」界線的資料分析師。只要具備基本的 Python 程式設計知識，便可學習如何透過業界標準工具（如 pandas）來處理資料： ．將感興趣的問題精煉為可透過資料探究的研究問題 ．執行資料蒐集，其中可能涉及文字處理、網頁爬蟲等技術 ．透過資料清理、探索與視覺化，萃取出有價值的洞見 ．學會使用建模來描述資料特性 ．推廣研究結果，進行超出資料本身的推論

An innovative board book design that lets toddlers place three chunky shapes into matching slots. This bold, bright board book makes learning about shapes and first words endlessly fun. Three chunky circles, featuring 6 bright colours, attach to the book with colourful ribbons to create an engaging colour-sorting game, perfect for children from one to three years.The shapes slot into a die-cut on the page, with satisfying ease, completing a variety of images, from an ice cream cone to a branch full of bugs. Matching the colours aids children in their fine motor skills, hand-eye co-ordination and problem-solving skills. Each spread also introduces popular first words, making this the ideal book to learn from while having fun.Illustrated in vivid colour and effortless style by Polish artist Susie Hammer. Also available: Match the Shape

"These twins make learning to read double the fun." - Kirkus From award winning author, Grace Lin, comes another installment of the irresistible early reader series, Ling & Ting! Join these iconic twins as as they celebrate Lunar New Year! Ling and Ting are twins. They are Chinese and American. They celebrate the new year two times. Now they are preparing for Lunar New Year. They want a lucky year. What happens when Ting makes a mistake? Can their year still be lucky? * Don't miss Ling and Ting's other adventures!: Ling & Ting: Not Exactly the Same Ling & Ting: Twice as Silly Ling & Ting: Together in All Weather Ling & Ting Share a Birthday

本書精選IOI、NOIP、USACO、CCC、CCO、ICPC、DWITE等競賽中28個富有趣味與挑戰性的算法競賽題，採用“問題描述 – 輸入 – 輸出 – 解決方案”的結構，引導讀者在實戰中培養算法思維，牢固掌握數據結構與算法的核心知識。全書共分10章，涵蓋哈希表、樹與遞歸、記憶化與動態規劃、圖與廣度優先搜索、加權圖中的最短路徑、二分搜索、堆與線段樹、並查集以及隨機化算法等主題。所有示例均用C語言實現，但無論你使用C++、Java還是Python，都能輕鬆理解並遷移所學思想與方法。

本書深入解析大數據的發展沿革、4V特徵、行業價值與企業管理模式，更重要的是，它引導讀者從商業應用情境出發，思考並解決真實的數據分析問題，如群組、分類、推薦與關聯等。建立數據驅動思維，與數據科學家進行更有效的溝通，加速智慧應用發展。在實作層面，本書提供Python語言範例，循序漸進地引領讀者了解分析與模型建立步驟。提供房價預測、電影推薦、圖片辨識及自然語言解析等貼近真實的案例。本書更納入AI先進技術，介紹LangChain基礎運用，發展簡單的AI代理程式。這是一本橫跨大數據基礎、預測分析方法，到文本挖掘、圖像辨識、生成式AI代理的完整教材，是商管學生與商業人士理解AI實務與應用的最佳起點！

線性代數是一門高度通用且具備廣泛應用的基礎工具，無論是在人工智慧、數據分析或是各類工程領域，專業人士都需要對其具有紮實素養，方能在實際問題中建立準確的數學模型。本書的核心特色是擺脫傳統冗長而抽象的理論推導，轉而以直覺化的概念闡釋和高效的實戰應用為主軸。在解題風格上，本書不同於一般教科書完全按照理論框架進行推導。作者憑藉多年的實戰教學經驗，在許多計算技巧上提供了獨家捷徑，幫助學生有效提升解題速度，達到事半功倍的學習效果。同時，本書強調與現代科技趨勢的接軌，於附錄中提供 MATLAB 與 Python 雙程式平台的程式碼範例與腳本教學，專門針對複雜的線性代數計算提供電腦輔助實作指導。

本書專為資料科學與人工智慧專業的初學者設計，內容通俗易懂，系統性講解學習AI所需的基礎數學知識。全書涵蓋理論與實踐，結合推導、計算與Python程式實作，幫助讀者靈活掌握核心概念並提升實戰能力。無論是作為相關課程教材，或是從業者的自學參考書籍，本書皆能有效協助讀者打下紮實的數學基礎，為人工智慧領域做好準備。AI時代來臨，你的數學準備好了嗎？打好基礎，推開智慧之門邁向未來，就從這本書開始！初學者友善的入門指南本書專為資料科學與人工智慧（AI）相關領域的學生與初學者量身打造，旨在幫助讀者建立學習AI所需的基礎數學概念。不同於傳統艱澀的數學教材，本書以通俗易懂、由淺入深的方式進行講解，讓即便是數學基礎薄弱的讀者，也能輕鬆理解並掌握內容。透過清晰的結構與大量實例說明，本書化繁為簡，降低學習門檻，是通往AI世界的重要敲門磚。理論與實踐兼備，學以致用本書的內容設計強調理論與實務並重，既有嚴謹的數學推導，也有豐富的應用實例。除了傳統的計算練習，還融合Python程式語言進行實作，讓讀者能夠同步訓練邏輯思維與程式技能。透過實際編程演練，不僅有助於加深對數學原理的理解，也提升將理論應用於資料分析與AI開發的實務能力。這樣的設計不僅符合現代科技教育的需求，也貼近業界實際工作情境。教材與自學兼用，應用範圍廣泛本書不僅可作為大專院校資料科學與人工智慧相關課程的教材，也非常適合自學者與職場從業人員作為參考書使用。內容涵蓋機率與統計、線性代數、微積分、最佳化等AI核心數學領域，系統性地建立完整知識架構。無論是準備進一步修習機器學習、深度學習等進階課程，或是需要在實務工作中應用資料分析與模型建構能力，本書都是不可或缺的重要資源。奠定未來AI學習的基礎人工智慧的發展日新月異，對數學的依賴與日俱增。唯有具備紮實的數學基礎，才能真正理解演算法的原理與模型背後的邏輯，進而靈活應用於各種情境中。本書不僅是一部數學入門書，更是一座銜接AI世界的橋樑。透過理論與實務交織的學習旅程，幫助讀者在AI領域立足，迎接更廣闊的未來發展機會。本書特色：本書專為資料科學與人工智慧專業的初學者設計，內容通俗易懂，系統性講解學習AI所需的基礎數學知識。全書涵蓋理論與實踐，結合推導、計算與Python程式實作，幫助讀者靈活掌握核心概念並提升實戰能力。無論是作為相關課程教材，或是從業者的自學參考書籍，本書皆能有效協助讀者打下紮實的