蔡明燁譯，英國製造：國家如何維繫經濟命脈，立緒，2017
當經濟不斷受挫之際，市場上很少出現正面的經濟觀。產業外移、房市泡沫、物價齊漲，薪資水平卻長期低迷甚至倒退，除了籠統歸因大環境不景氣之外，人們也開始質疑自己究竟能夠產製或銷售什麼有價值的東西，而未來的經濟又該走向何方？
他山之石，可以攻錯，在思考我們國家的產業發展時，或許可以看看英國如何度過金融海嘯，重新調整經濟體質，站穩腳步迎向國際新局的例子。而隨著篇章開展，讀者亦能逐漸將書中分析套用在台灣的經濟發展上，當在面對國內經濟轉型的各種挑戰時，不再如無頭蒼蠅般惶惶不安。
作者伊凡．戴維斯為英國經濟學者，也是長期深入觀察當地產業的財經記者，書中對英國經濟的分析採取正向樂觀的論點，但絕不盲目，而是就事論事，從嚴謹的數據與比較分析中得出持平而論的根據，並做出有力的提醒和檢討，目的是要說服讀者，一個正常開放國家的謀生實力，其實比我們所想像中要強得多。
Gilbert Strang. 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning. Spring 2018. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA. (book)
Strang 教授教這門課的時候 83 歲，真的是終身學習的好典範。在美國，這種對專業的執著 (Tapley 教授) 令人欽佩。另外一個例子是 Breiman 教授，71 歲投稿隨機森林 (Random forests)，成為經典論文；其他幾篇重要論文，大都在 65 歲以後以單一作者發表！
Srini Devadas, Programming for the Puzzled: Learn to Program While Solving Puzzles, The MIT Press, 2017.
Learning programming with one of “the coolest applications around”: algorithmic puzzles ranging from scheduling selfie time to verifying the six degrees of separation hypothesis.
AWS re:Invent 2019 – Rob Smedley From Formula 1 Talks About Using AWS to Improve the Fan Experience, 2019/12/4
Formula 1 has been using Amazon EC2 for Computational Fluid Dynamics (CFD) to simulate race car aerodynamics, achieving the performance of a super computer at a much lower cost and reducing simulation time by an average of 70% — from 60 hours down to 18 hours. With the CFD project, Formula 1 used over 500 million data points to study downforce loss when two vehicles race in close proximity. (A car’s downforce increases its tire grip and cornering speed and reduces lap time.) Based on its CFD simulations, Formula 1 has designed a car for the 2021 racing season that reduces downforce loss in wheel-to-wheel racing from 50% to 15% — and offers a more exciting experience for fans.
Andrew Ng, A prosthetic leg that learns from the user's motion could help amputees walk more naturally, The Batch, August 05, 2020.
What’s new: Researchers from the University of Utah designed a robotic leg that uses machine learning to generate a human-like stride. It also helps wearers step over obstacles in a natural way.
Jules S. Damji, Brooke Wenig, Tathagata Das, and Denny Lee, Learning Spark: Lightning-Fast Data Analytics, 2nd edition, 2020, O’Reilly Media. (code)
We welcome you to the second edition of Learning Spark. It’s been five years since the first edition was published in 2015, originally authored by Holden Karau, Andy Konwinski, Patrick Wendell, and Matei Zaharia. This new edition has been updated to reflect Apache Spark’s evolution through Spark 2.x and Spark 3.0, including its expanded ecosystem of built-in and external data sources, machine learning, and streaming technologies with which Spark is tightly integrated.
Over the years since its first 1.x release, Spark has become the de facto big data unified processing engine. Along the way, it has extended its scope to include support for various analytic workloads. Our intent is to capture and curate this evolution for readers, showing not only how you can use Spark but how it fits into the new era of big data and machine learning. Hence, we have designed each chapter to build progressively on the foundations laid by the previous chapters, ensuring that the content is suited for our intended audience....
Most of the examples in the chapters are written in Scala, Python, and SQL. Where necessary, we have infused a bit of Java.The ebook is available for download once you fill in your information at Databrick.
工程師在波特蘭，Jim Keller來了，2020 年 08 月 12 日
JK的第一個改革非常符合邏輯, 簡單來說就是兩個重點: IP re-use (重複使用), 還有在IP部門的開發時程和產品部門的整合時程上盡可能的重疊. 他下達的新指令就是, IP team以後不負責hardening, 由產品部門負責, 但是IP team要確保IP是可以很容易的驗證 (verifiable), 而且介面要很乾淨. 這樣一來產品部門可以在很早期就開始驗證, 然後因為hardening統一由產品部門負責, 所以操作條件也一致, 實作起來也比較有效率. 為了完成這個任務, JK在他自己加入五個月後, 從外面挖來了有個人師徒情誼的Netspeed的CEO Sundari Mitra來負責統整所有IP方面的業務.
黃俊堯，數位轉型全攻略：虛實整合的 WHAT，WHY 與 HOW，商業周刊，2019
不論領域、大公司、中小企業都在談數位轉型，但想要轉跟怎麼轉永遠是兩回事，不要讓數位轉型成為你們公司的痛點。
台大商學院教授黃俊堯現身說法，分享數位轉型的眉角！
David B. Yoffie, Annabelle Gawer, and Michael A. Cusumano, A Study of More Than 250 Platforms Reveals Why Most Fail, HBR, May 29, 2019.
Platforms have become one of the most important business models of the 21st century. In our newly-published book, we divide all platforms into two types: Innovation platforms enable third-party firms to add complementary products and services to a core product or technology. Prominent examples include Google Android and Apple iPhone operating systems as well as Amazon Web Services. The other type, transaction platforms, enable the exchange of information, goods, or services. Examples include Amazon Marketplace, Airbnb, or Uber.
未來流通研究所，年度30強，誰是台灣連鎖企業坪效王，2020 / 08 / 04
「坪效」是實體通路有別於其他行業別最特殊的經營指標，也是攸關連鎖企業經營績效的關鍵數據。在全球實體消費市場遭逢疫情衝擊以及電子商務爆發性成長的當下，觀測各類型實體通路企業的坪效指標，不僅可做為企業經營健全強度的分析與衡量基準，也能夠為疫情後台灣實體通路的變革思維帶來一些線索。
未來流通研究所團隊抓取2019年台灣連鎖實體通路企業的全年營收、店鋪數量、營運坪數等數據，彙整成為連鎖企業坪效指標，並羅列前30強企業名單供讀者參考。從名單中可以看出，餐飲業為進榜家數最多的產業別，且當中涵蓋餐館、中式速食、日式速食、西式速食、咖啡飲料店等各種類型，而台灣餐飲業知名品牌鼎泰豐的坪效表現更是大幅領先，穩坐台灣連鎖企業坪效王的制霸地位。
這次要介紹的是一種台灣自行開發的工程最佳化軟體，這種軟體是 CAE (Computer Aided Engineering) 軟體的一種。要設計一個工業產品，通常都要有一個模擬軟體(simulation software)，也就是說，我們要測試一下所設計的產品能否使用。比方說，我們設計了一個馬達，當然要測試這個馬達能不能轉，這可以用模擬軟體來測驗。如果我們設計了一個電子電路，要知道這個電子電路是否符合要求，也可以用模擬軟體來測驗。