Python machine learning

A project-based approach to learning Python programming for beginners. Intriguing projects teach you how to tackle challenging problems with code.You've mastered the basics. Now you're ready to explor

AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. It comprises of bootstrapping to create multiple training and testing

This book introduces artificial neural networks to students and professionals. It covers the theory and applications in statistical learning methods with concrete Python code examples.

Present book covers new paradigms in Blockchain, Big Data and Machine Learning concepts including applications and case studies. It explains dead fusion in realizing the privacy and security of

《Python數據挖掘入門與實踐》一書作為數據挖掘入門讀物，介紹了數據挖掘的基礎知識、基本工具和實踐方法，通過循序漸進地講解算法，帶你輕鬆踏上數據挖掘之旅。本書採用理論與實踐相結合的方式，呈現瞭如何使用決策樹和隨機森林算法預測美國職業籃球聯賽比賽結果，如何使用親和性分析方法推薦電影，如何使用樸素貝葉斯算法進行社會媒體挖掘，等等。本書也涉及神經網絡、深度學習、大數據處理等內容。

Both a serious academic text and an intriguing story, this seventh edition reflects a significant update in research, theory, and applications in all areas. It presents a comprehensive view of the historical development of learning theories from behaviorist through to cognitive models. The chapters also cover memory, motivation, social learning, machine learning, and artificial intelligence. The author's highly entertaining style clarifies concepts, emphasizes practical applications, and presents a thought-provoking, narrator-based commentary. The stage is given to Mrs Gribbin and her swashbuckling cat, who both lighten things up and supply much-needed detail. These two help to explore the importance of technology for simulating human cognitive processes and engage with current models of memory. They investigate developments in, and applications of, brain-based research and plunge into models in motivation theory, to name but a few of the adventures they embark upon in this textbook.

Both a serious academic text and an intriguing story, this seventh edition reflects a significant update in research, theory, and applications in all areas. It presents a comprehensive view of the historical development of learning theories from behaviorist through to cognitive models. The chapters also cover memory, motivation, social learning, machine learning, and artificial intelligence. The author's highly entertaining style clarifies concepts, emphasizes practical applications, and presents a thought-provoking, narrator-based commentary. The stage is given to Mrs Gribbin and her swashbuckling cat, who both lighten things up and supply much-needed detail. These two help to explore the importance of technology for simulating human cognitive processes and engage with current models of memory. They investigate developments in, and applications of, brain-based research and plunge into models in motivation theory, to name but a few of the adventures they embark upon in this textbook.

Variational Bayesian learning is one of the most popular methods in machine learning. Designed for researchers and graduate students in machine learning, this book summarizes recent developments in the non-asymptotic and asymptotic theory of variational Bayesian learning and suggests how this theory can be applied in practice. The authors begin by developing a basic framework with a focus on conjugacy, which enables the reader to derive tractable algorithms. Next, it summarizes non-asymptotic theory, which, although limited in application to bilinear models, precisely describes the behavior of the variational Bayesian solution and reveals its sparsity inducing mechanism. Finally, the text summarizes asymptotic theory, which reveals phase transition phenomena depending on the prior setting, thus providing suggestions on how to set hyperparameters for particular purposes. Detailed derivations allow readers to follow along without prior knowledge of the mathematical techniques specific to

This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the w

本書作為數據挖掘的入門讀物，基於真實的數據集進行案例實戰，使用Python數據科學庫，從數據預處理開始一步步介紹數據建模和數據挖掘的過程。主要介紹了數據挖掘的基礎知識、基本工具和實踐方法，通過循序漸進地講解算法，帶領讀者輕鬆踏上數據挖掘之旅。本書採用理論與實踐相結合的方式，呈現了如何使用邏輯回歸進行環境數據檢測，如何使用HMM進行中文分詞，如何利用卷積神經網絡識別雷達剖面圖，如何使用循環神經網絡構建聊天機器人，如何使用樸素貝葉斯算法進行破產預測，如何使用DCGAN網絡進行人臉生成等。本書也涉及神經網絡、在線學習、強化學習、深度學習和大數據處理等內容。本書以人工智能主流編程語言Python 3版本作為數據分析與挖掘實戰的應用工具，從Pyhton的基礎語法開始，陸續介紹了NumPy數值計算、Pandas數據處理、Matplotlib數據可視化、爬蟲和Sklearn數據挖掘等內容。全書共涵蓋16個常用的數據挖掘算法和機器學習實戰項目。通過學習本書內容，讀者可以掌握數據分析與挖掘的理論知識及實戰技能。

本書涵蓋Kubernetes架構、部署、核心資源類型、系統擴縮容、存儲卷、網絡插件與網絡本書由淺入深地介紹了一系列常見的Python數據科學問題。書中介紹的實踐項目簡單明瞭，可作為模板快速啟動其他類似項目。通過本書，你將學習如何構建一個Web應用程序以進行數值或分類預測，如何理解文本分析，如何創建強大的交互界面，如何對數據訪問進行安全控制，以及如何利用Web插件實現信用卡付款和捐贈。每章都遵循三個

Learning Python just got fun for kids!Learning to code is just like playing a new sport or practicing an instrument—just get started! From the basic building blocks of programming to creating your ver