Deep Learning with Python

★ 探討「生物設計×人工智慧」如何重塑建築與城市未來★ 從都市微生物到地質尺度，拓展設計的時間與空間想像★ 結合實務案例與學術研究，提出設計作為「共生系統」的全新觀點在人工智慧與氣候危機交織的當代，設計的角色正快速轉變。本書《Biodesign in the Age of Artificial Intelligence: Deep Green》由創新設計團隊 ecoLogicStudio 主導，延續其前作《Systemic Architecture》，進一步提出「設計智慧」不僅屬於數位領域，也深植於生物系統之中。全書分為兩大部分：第一部分「PhotoSynthetica™」聚焦於都市尺度的應用，透過介入城市微生物生態系，發展能即時回應環境的設計方案；第二部分「Deep Green」則將視野擴展至更宏觀的時空尺度，探索合成地景與長時間演化過程，將設計視為一種影響地理與地質的力量。作者結合於 University of Innsbruck 與 UCL Bartlett School of Architecture 的研究成果，提出設計應被理解為一種延伸的認知介面——一個具有感知能力、能與地球生命系統共演化的存在。在無法簡單「逆轉」氣候變遷的現實下，本書主張我們應建立新的正向變化動能，讓建築與設計成為維繫地球美感與生命力的重要角色。這不僅是一本關於未來設計的書，更是一份重新思考人類、科技與自然關係的深刻提案。Biodesign in the Age of Artificial Intelligence: Deep Green investigates the potential of nature-based technology for shaping the evolution of contemporary architecture and design. It takes on the now pervasive topic of design intelligence, extending its definition to encompass both biological and digital realms.As in their first title, Systemic Architecture: Operating Manual for the Se

Designed as a textbook for a one or two-term introduction to mathematical statistics for students training to become data scientists, Foundations of Statistics for Data Scientists: With R and Python is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modelling. The book assumes knowledge of basic calculus, so the presentation can focus on 'why it works' as well as 'how to do it.' Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python.The book also introduces modern topics that do not normally appear in mathematical statistics texts but ar

This book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; uni