3 books I recommend for software project management

  1. The mythical man-month (人月神話)
  2. 走出軟體工廠
  3. 編程創藝
廣告

Yahoo shareholders approve sale of internet business to Verizon

Yahoo ! 6月19 從 S&P500 下市.

Yahoo! 在網路發展史上開創性的貢獻很大.

從Yahoo !, 才開始有免費入口網站.  記得90年代年前搜尋資料是要收錢的.

“免費" 的商業模式是從Yahoo! 開始,

我們都受Yahoo! “開放" “免費" 的 商業模式創新的恩惠而不自覺.

楊致遠並不是以賺錢作為創業的初衷, 讓大量用戶透過Yahoo! 門戶訪問網路. 入口網站就發揮類似作業系統的作用.

“網站目錄向全世界開放,  無私為全世界網頁建立索引"

紀念這家公司

(https://www.ft.com/content/6cb09db6-4c6d-11e7-919a-1e14ce4af89b)

基隆

基隆適合獨遊.

驅車前往, 走國道一, 下高速,  右轉進信二路,  過醫院, 左轉上壽山路, 上山後, 即見基隆另一平靜舒適風景,  中正公園, 活水會館, 可散步, 慢跑, 游泳, 或祈福拜拜, 這山上是市民生活休閒運動的好地方. 中正公園往前走, 漫步至二沙灣砲台, 拍照留念並了解鴉片戰爭期間炮台歷史.

傍晚下山, 基隆市區小, 散步倒滿適合,多來幾次 可感受到基隆的生活步調與情趣.  我喜歡小市區的溫暖,  轉進一小巷, 隨選一家庭麵店,  端上面來的青年認真而尊敬, 見他手臂滿手刺青, 與這知足而崇敬之心, 有很大的對比,  這體驗跟轉變,  讓吃麵也珍惜這樣的緣分和體悟.

散步至孝二路,  同學的黑膠唱片店已倒閉,

音樂這一行,  有它難的部分,  創業也不應受媒體影響聞雞起舞, 瞎聽,或盲目崇拜異國文化也不是辦法.

至孝三路, 魚丸伯仔, 但好奇這家跟另一巷口的大白鯊魚丸是否同一老闆?

飲食文化跟地理環境很有關係, 海, 是基隆生活的一部份, 也是創作的泉源.

keelung

5

10

672

二沙灣 from Joe cc on Vimeo.

(參: 二沙灣砲台 // https://zh.wikipedia.org/wiki/%E4%BA%8C%E6%B2%99%E7%81%A3%E7%A0%B2%E8%87%BA)

創作者的日常生活

能保持創作不綴的人, 幾乎都是過著規律的生活, 不靠曇花一現的靈感,而是生活中養成一種生活習慣, 解放心智, 合理的分配時間,早起, 謝絕無意義社交, 適當飲食,飲酒或咖啡, 找出自己的最佳時間創作, 其餘時間, 生活,閱讀,散步, 接近自然, 時時保持創作的靈感延續成為一種日常生活.

2

Guitar chord chart


guitar chord chart

I was quite good at playing guitar as young.  I reopen my guirar box and bought a small amp for practicing. I still rememeber everything.

Guitar chord is very intersting. It is like  the math. It need to calculte the Appegio, which is composite notes of each cord.  I review the chord and scale and practice it on my Fender guitar.

After many years, I feel more matured on music compose. I feel my ears  become very clear when listening music. In the past I spent much time on catch the notes on guitar from songs. It took time to analysis and catch it.  Afer years, all including theory and  style seem mix into my life. I still can feel it and analysis the structure of song, finding out the meaning of creativity.  However, music industry is huge impacted by digitalization and piracy in the past decades. This industry becomes very weak because of techbology innovation, all friends about music are disapeared.  But, music still does not change fundamentally, I think .

I remember this song, which had been played by my band two decades ago. (note1) , But nowadays I listen much on classical music.

(note1:Queensrÿche – The Mission)

資料科學週期表

 

table of data science

資料科學週期表

table down right

社群 Q&A

table up

資料管理視覺化

table down

table right

資料科學相關新聞

table left

資料科學相關課程會議

 

居然已把資料科學做成一個週期表,想像力視覺化還真豐富.

最左邊是課程, 最右邊是新聞, 我平常已有在讀KDnuggets 和HackerNews.

(Source : https://www.datacamp.com/community/blog/data-science-periodic-table#gs.FeNjSMA)

The comparative advantage of human and computer

MaxthonSnap20170316073020

(Source from MIT SMR)

 

Google, AI, Machine Learning

Google cloud platform (note5)

Target : the enterprise cloud market

  • Position- To be a developer-friendly platform
  • Weakness

1) Not strong and no impression  on cloud service and the enterprise segment

2) No contribution to the open source community before.

(note1)

  • Stength : ASIC, GPU and TPU hardware in its cloud
  • Opportunity

1) begin to work with open source projects (note3)

  • Cloud Native Computing Foundation- the open-source container management tool

run by the Linux Foundation (note4)

Other partners in the new foundation include AT&T, Box, Cisco, Cloud Foundry Foundation, CoreOS, Cycle Computing, Docker, eBay, Goldman Sachs, Huawei, IBM, Intel, Joyent, Kismatic, Mesosphere, Red Hat, Switch SUPERNAP, Twitter, Univa, VMware and Weaveworks.

Absent : Microsoft, Amazon, Pivotal, and  Taiwanese tech companies)

the popular open source container orchestration system

  • TensorFlow for machine learning

-Spanner for launching massive distributed databases

-Draco for 3D graphics compression

2) To be a  developer-friendly platform

  • “OPENNESS" 

1)letting customers run whatever open source stack they choose on Google’s infrastructure,

2)releasing and supporting open source projects and making the ecosystem

3)the partners who build tools and technologies on top of GCP, a first class citizen on the platform.

4) treating them as part of the whole and the net is bringing the tech you want and using Google technology or using any of the [partner] services

The KSF :

1)Being open to win the mind shares of developers

2) much more supportive of the open source community makes people feel better about Google and makes developers feel better about working with their tools because they can avoid lock-in

  • Threat : peers: AWS (2006. 1st public cloud, market leader, 1st mover), Microsoft, IBM
  • Strategy

UsingKubernetes, the popular open source container orchestration system offer robust open source tools, something that surprised some people in this market.

  • 4 ways Google will enable enterprises to adopt machine learning and AI (note2)

1). Machine learning computing in Google Cloud

a deep learning algorithm can have tens of millions of parameters, training these machine learning models requires enormous computational resource

the Cloud Machine Learning Engine. This capability is designed for companies with data scientists and machine learning experts who are able to build their own unique machine learning models with libraries such as Tensorflow.

Google’s infrastructure as the solution to speed training times and improve the return on investment. Google has specialized ASIC, GPU and TPUhardware in its cloud to accelerate training and improve the ROI with on-demand cloud resource utilization. After the model is trained, it is deployed in range of platforms—from on-premise to mobile devices.

2. Algorithms and pretrained machine learning models

建ML model 需用 the machine learning engine, 用 Google’s pre-trained models (full list) using APIs to add machine learning capability to their applications, such as understanding natural language, images and natural language.

An API beta for understanding videos

demo: This 3-minute video of the demonstration of the Cloud Video Intelligence beta

3. Google acquires Kaggle for data

Google acquired Kaggle for data sets and talent. Kaggle, founded in 2010, is a community of 850,000 data scientists from around the world that hosts competitions to create the most accurate predictive models and market models, as well as to acquire new public data sets in a variety of fields.

4. Expertise

the Advanced Solutions Lab for customers with ambitious goals to develop machine learning to solve complex problems.

(note1: https://techcrunch.com/2017/03/09/google-in-the-cloud/)

(note2: http://www.networkworld.com/article/3179127/cloud-computing/4-ways-google-cloud-will-bring-ai-machine-learning-to-the-enterprise.html)

(note3:https://techcrunch.com/2015/07/21/as-kubernetes-hits-1-0-google-donates-technology-to-newly-formed-cloud-native-computing-foundation-with-ibm-intel-twitter-and-others/)

(note4: The mission of Linux foundation :The mission of this new foundation is to “help facilitate collaboration among developers and operators on common technologies for deploying cloud native applications and services,” )

(note5: https://cloud.google.com/)

(Reference : Google Cloud Platform 入門)

(Reference: https://technews.tw/tag/google-cloud-platform/)

 

Machine Learning for Marketing

The marketing big data ecosystem being impacted by machine learning in four major areas:

  1. Automated data visualization (including ML results) will become more rich, and user-friendly.
  2. Content analysis (textual, lexical, multimedia/rich) will be used to drive better marketing conversations.
  3. Incremental ML techniques will become more prevalent, leading to real-time, not just on-going and automated, changes in marketing execution.
  4. Learning from ML results will accelerate the growth and skills of marketing professionals.
  • Automated Data Visualization tools: Tableau and Qlikview

Predictive model : The objective of ML is to build predictive model for forecast.

the ability to modify a solution that is already in place by introducing new data rather than having to stop using the current solution before building a new model from scratch.

(Source from How Machine Learning Will Be Used For Marketing In 2017)

當票房很差的時候


Photo credit: fulvio di marco via VisualHunt.com / CC BY-NC-ND

一片導演太多, 供需失衡. Over Supply. 若一部電影週營收只有幾百萬, 比7/11 週營業額還小, 基本上這導演已經結束了.

誰要你當初選擇媚俗?

迎合觀眾卻得到這樣的結果, 還有多少內涵可供消費. Content 也不會是King 了

電影重複操作是走不通的,  精明人很快就看出你玩完了. 沒有深思創作, 只想重複老梗, 妥協求全, 被唾棄也是剛好.

好的作品 對觀眾而言載體規格不是重點, 重點是時間有限,只能取捨,只取好作品. 多裝置的對應策略是什麼?  土法煉鋼有用嗎? 有多少做法是重複?

現在國片是兩頭空, 票房爛, 沒藝術,

不如拍短片, 找小眾, 找到知音,而非取寵, 重要的是培養出自己的電影語言,能存在於影史的脈落, 成為作者,  拍很多片不見得等於是作者,  是否能醞釀出自己的電影語言?   作者論雖很多問題,但以台灣環境還算適合的.

很多基本的東西, 是否在媚俗的過程中失去了?

還有多少時間可以找回來 或再發現新的價值?

以為電影能是經濟火車頭, 現今是全軍覆沒

當票房很差的時候, 應該回到學生的初衷, 像創業失敗一樣, 重新學習,

電影是什麼? 本質是什麼?  從經典去觀摩, 什麼是永恆的價值,什麼是浮光掠影,  小沒有關係, 如果小的有價值,有內涵.  把所有要素一一檢討清楚. 如果是環境已經改變, 就要重新認清現實, 

藝術家一但媚俗,往往很難再堅持藝術了.

導演, Stay Hungry, Stay Foolish.

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