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#新聞稿
重點就是
Parallels Desktop 16.5 for Mac 支援M1晶片了！
用戶只要擁有 Parallels Desktop 16 for Mac 授權，便可免費升級至 Parallels Desktop 16.5，而且無需支付額外費用。

＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝
Parallels Desktop 16.5 for Mac 支援 M1 和 Intel 晶片；
在 Mac 電腦以原生速度執行 Windows 10，享受深受數百萬人喜愛的

流暢體驗

Parallels Access 和 Parallels Toolbox 應用程式均為 M1 Mac 電腦提供原生支援

台北訊 – 2021 年 4 月 15 日 –
Parallels ® 是跨平台解決方案的全球領導者，今日隆重推出眾所期待的Parallels Desktop 16.5 for Mac (parallels.com/desktop)，無論是配備 Apple M1 或 Intel 晶片的 Mac電腦都能獲得完整的全球領導品牌原生支援。在 Apple M1 Mac 電腦上體驗 Windows 10 ARM Insider Preview 及其應用程式 2 。M1 晶片的卓越效能為個人電腦提供全球最快的整合式圖像處理、革命性能源效率，更以配合 macOS Big Sur 3 使用為設計宗旨，Parallels Desktop 16.5 提供流暢的使用體驗，也因此成為在 Mac 上執行 Windows 的最新標準。Parallels Desktop 16.5 也針對最受歡迎的 ARM Linux發行版提供 M1 支援。免費隨附於 Parallels Desktop 訂閱的獨立 Parallels Access(parallels.com/access) 以及剛經過重新設計及簡化的 Parallels Toolbox (parallels.com/toolbox) 應用程式現已提供 M1 Mac 電腦的原生支援。

Parallels 工程和支援資深副總裁 Nick Dobrovolskiy 表示：「Apple 的 M1 晶片對於 Mac 用戶來說是一項重大突破。所幸有 Rosetta 技術，大多數 Mac 應用程式的轉換過程都相當順利。不過虛擬機器卻是例外，正因如此，Parallels 的工程師針對配備 M1 晶片的 Mac 電腦引入了原生虛擬化支援。這種做法讓我們的用戶能享受在 Mac 上執行 Windows 的最佳體驗。」

已有超過 100,000 位 M1 Mac 用戶測試過 Parallels Desktop 16.5 for M1 Mac 的技術預覽版，並執行Microsoft 的 Windows 10 on ARM Insider Preview 以及成千上萬個以 Intel 為基礎的 Windows 應用程式，包括 Microsoft Office for Windows、Microsoft Visual Studio、SQL Server、Microsoft PowerBI 和MetaTrader。

Dobrovolskiy 說：「我們收到用戶的熱情回應，表示 Parallels Desktop 16 for M1 Mac 技術預覽版和
Windows 10 on ARM Insider Preview，以及 x86 應用程式和遊戲，包括《Rocket League》、《Among Us》、《The Elder Scrolls V: Skyrim》、《Sam & Max Save the World》等等都獲得優異效能。測試人員喜愛 Parallels Desktop 簡單易用的功能，以及 Windows 和 macOS Big Sur 之間的流暢整合，能讓他們提升生產力。」

觀看影片了解如何在 M1 Mac 上的 Parallels Desktop 16.5 中安裝虛擬機器： www.parallels.com/VMonM1Mac 。

Parallels Desktop 16.5 for Mac 的虛擬化做法能讓用戶從 Apple M1 晶片提升的效能中獲益，讓應用程式執行速度更快、效率更高。效能方面的觀察結果包括：

 減少高達 250% 的能源耗用：與 2020 年以 Intel 為基礎的 MacBook Air 電腦相比，在配備Apple M1 晶片的 Mac 電腦上，Parallels Desktop 16.5 的能源耗用減少 2.5 倍 4 。

 DirectX 11 效能提升高達 60%：與以 Intel 為基礎的 MacBook Pro (配備 Radeon Pro 555X GPU) 相比，在 M1 Mac 電腦上執行 Parallels Desktop 16.5 ，DirectX 11 的效能最高可提升
60% 5 。

 虛擬機器效能提升高達 30%：與以 Intel 為基礎的 MacBook Pro (配備 Intel Core i9 處理器) 執行 Windows 10 VM 相比，在 M1 Mac 電腦的 Parallels Desktop 16.5 上執行 Windows 10 on ARM Insider Preview 虛擬機器 (VM) 最高可提升效能達 30% 6 。

美國新墨西哥州阿爾伯克基的 Daniel Dudley 表示：「我是一位數學老師，由於我使用的幾個軟體應用程式不再支援 M1 Mac，所以我在 M1 Mac Mini 上安裝了 Parallels Desktop 16.5 for Mac 和Windows 10 on ARM Insider Preview，在 M1 Mac 的 Parallels Desktop 16.5 執行 Windows 的體驗非常良好，是我用過最快的 Windows 版本！」

Parallels Desktop 16.5 for Mac 也支援 M1 Mac 上的客體作業系統 (OS)，包括 Linux 發行版 Ubuntu 20.04、Kali Linux 2021.1、Debian 10.7 及 Fedora Workstation 33-1.2。

英國的 Darren Paxton 說：「我超愛在 M1 Mac 的 Parallels Desktop 16.5 中執行 Linux 環境，效能真的非常驚人。」

Parallels Desktop 16 for Mac 的所有出色功能都已針對 Apple M1 晶片而重新設計，包括：

 連貫模式：可在 Mac 上以有如原生 Mac 應用程式的方式執行 Windows 應用程式，不必管理兩個獨立桌面或重新啟動。

 共用設定檔：與虛擬機器共用 Mac 桌面、圖片、文件和其他資料夾，以便輕鬆存取。

 Touch Bar 控制項：在虛擬機器中快速存取應用程式，以及自訂 Touch Bar 快捷鍵以顯示您最常用的應用程式和指令。

 Mac 鍵盤配置：輕鬆自訂鍵盤功能表和快捷鍵，獲得更加個人化的體驗。

 包括數百種其他 Parallels Desktop 功能，可協助用戶輕鬆提升生產力。

 Parallels 希望在今年稍後於 Parallels Desktop 新增在虛擬機器中使用 macOS Big Sur 這項功能的支援。

您可在這篇新聞稿了解適用於以 Intel 為基礎的 Mac 電腦的 Parallels Desktop 16 各種主要功能。以Intel 為基礎的 Parallels Desktop for Mac 客戶可執行超過 50 種支援的客體 OS，包括數個 Windows版本、macOS、OS X、Linux，以及 Android OS 和其他數十種作業系統。

銷售方式和定價
所有 Parallels Desktop 16.5 版本的更新都支援 M1 和 Intel 版的 Mac 電腦 (Standard Edition、ProEdition 和 Business Edition)。您可在網上 (parallels.com/desktop) 購買各種版本 (提供為期 14 天的完整功能免費試用版)，也可在全球各地的零售店或網上商店購買。Parallels Desktop 訂閱免費隨附Parallels Access 以及剛經過重新設計和簡化的 Parallels Toolbox for Mac and Windows 的並行訂閱，您可單獨於 parallels.com 免費試用及訂閱。

用戶只要擁有 Parallels Desktop 16 for Mac 授權，便可免費升級至 Parallels Desktop 16.5，而且無需支付額外費用。此外，每年全新完整版本 Parallels Desktop for Mac 推出時，訂閱的用戶也可免費升級，以確保支援最新的 OS、硬體，以及 Parallels Desktop 的創新功能。」如要在 Apple M1 Mac 電腦的 Parallels Desktop 16.5 執行 VM，您需要有以 ARM 為基礎的作業系統(OS)。在 Parallels Desktop 虛擬機器安裝客體作業系統的客戶有責任遵守各 OS 的終端用戶授權合約(EULA)。

建議零售價 (RRP)
Parallels Desktop 16.5 for Mac
 將 Parallels Desktop 14 或 15 升級至永久授權 –$49.99美金
 新訂閱 – 每年 $79.99美金
 新版永久授權 – $99.99美金

Parallels Desktop for Mac Pro Edition
 從任何版本升級至永久授權 – 每年 $49.99美金
 新訂閱 – 每年 $99.99美金

Parallels Desktop for Mac Business Edition
 每年$99.99美金

附註說明:
1. 在 Mac 上以原生速度執行 Windows 視硬體設定而定。Parallels Desktop for Mac 擁有超過七百萬名客戶。
2. 如要在 Apple M1 Mac 電腦的 Parallels Desktop 16.5 執行 VM，您需要有以 ARM 為基礎的作業系統 (OS)。在 Parallels Desktop
虛擬機器安裝客體作業系統的客戶有責任遵守各 OS 的終端用戶授權合約 (EULA)。
3. Apple M1 的晶片效能、能源效率和資料來自https://www.apple.com/mac/m1/。
4. 效能測量由 Parallels 針對執行 Windows 10 的 Mac 電腦耗電量進行。測試是以預發行版本的 Parallels Desktop 16.5 在配備 Apple M1 晶片和 16GB RAM 的 MacBook Air 10,1 以及配備 Intel Core i5-8210Y 和 8GB RAM 的 MacBook Air 8,2 上進行。此效能將根據使用情況、系統設定和其他因素而有所不同。
5. 效能測量由 Parallels 針對 DirectX 11 API 執行 Unigine Valley Benchmark 並比較每部電腦五次重複測試的平均分數進行。測試是以預發行版本的 Parallels Desktop 16.5 在配備 Apple M1 晶片和 16GB RAM 的 MacBook Pro 17,1 以及配備 Intel Core i9-
8950HK、16GB RAM 和 Radeon Pro 555X GPU 的 MacBook Pro 15,1 上進行。此效能將根據使用情況、系統設定和其他因素而
有所不同。
6. 效能測量由 Parallels 執行 Geekbench 5 Benchmark 並比較每部電腦五次重複測試的平均分數來進行。測試是以預發行版本的
Parallels Desktop 16.5 在配備 Apple M1 晶片和 16GB RAM 的 MacBook Pro 17,1 以及配備 Intel Core i9-8950HK、32GB RAM 和
Radeon Pro Vega 20 GPU 的 MacBook Pro 15,3 上進行。此效能將根據使用情況、系統設定和其他因素而有所不同。

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sql world 在 โปรแกรมเมอร์ไทย Thai programmer Facebook 的最讚貼文

By โปรแกรมเมอร์ไทย Thai programmer

2020-09-13 13:37:54 有 286 人按讚

🤓 Data mining คืออะไรในวิชาทางคอม
มาเดี่ยวเล่าให้ฟัง
วิชานี้แปลตรงตัวก็คือ "การทำเหมืองข้อมูล"
อุปมาก็คล้ายกับเราไปทำเหมืองแร่ที่ขุดมาจากใต้ดินแหละครับ
.
แต่เหมืองที่ว่าเป็นเหมืองของข้อมูลดิบที่มีขนาดใหญ่นั่นเอง
.
คราวนี้จะให้ลองนึกถึงเวลาใช้คำสั่ง SQL เราจะรู้อยู่แล้วว่า
ข้อมูลมันอยู่ตรงไหนใน (Table) ใช่มั๊ยละ
พอเราใช้คำสั่ง select * from .... where...
ก็เลือกข้อมูลมาได้ตามต้องการ
.
แต่ถ้าเป็นคลังของข้อมูลดิบบบบบบบบบ
ที่มีปริมาณมาก ๆ เช่น
ข้อมูลเว็บไซต์ในโลกอินเตอร์เนตที่มีเยอะมาก ๆ
ข้อมูลพวกนี้จะใช้ SQL ค้นหาไม่ได้หรอกครับ
ข้อมูลยังไม่แยกเป็นตารางเลย
.
แล้วอย่างนี้ข้อมูลที่เราต้องการอยากรู้
ได้แก่ "ความรู้" หรือ "Knowledge"
ซึ่งความรู้ที่ว่า ไม่ใช้ข้อมูลแบบที่คิวรี่โดย SQL นะ
แต่จะได้เป็นความรู้ในฐานข้อมูล (Knowledge Discovery in Database - KDD)
ซึ่งก็คือ รูปแบบ (pattern) และความสัมพันธ์ (associations) ที่แอบซ่อนอยู่ในชุดข้อมูลนั้นๆ
.
🤔 ซึ่งคำถาม แล้วเราจะหาเจอได้ยังไง?
ภายใต้ข้อมูลดิบที่มีเยอะเหลือเกิ๊น
แถมหายากอีกต่างหากละพี่น้อง?
.
ดังนั้นเราจึงหนีไม่พ้นต้องมาทำเหมืองข้อมูลกัน
ช่างไม่ต่างอะไรกับทำเหมืองแร่
กว่าเราจะเจอ ดีบุก ถ่านหิน เพชร พลอย ทอง ฯลฯ
โอ๋ต้องใช้เทคโนโลยี ขุดเจาะหากว่าจะเจอ
ไม่ใช่ของกล้วย ๆ เลยนะครับ
เพราะกว่าจะได้สิ่งมีค่าออกมา
ก็เจอแต่เศษหิน เศษกราด เยอะแยะเต็มไปได้
สิ่งที่ต้องการอยากได้มีน้อยนิดเหลือเกิน
.
Data mining ก็เช่นกัน
กว่าจะทำการขุดๆ และขุด
จนได้ความรู้ออกมา
มันทำไม่ได้ง่ายๆ ต้องใช้คอมทำ
ต้องมีอัลกอริทึมให้ทำงานอัตโนมัติ
ทำด้วยมือคนไม่ไหว ตายลูกเดียว
.
ด้วยเหตุนี้วิชา Data mining
จึงถือกำเนิดอุแว้ขึ้นมาในยุค 1970
ซึ่งมันเป็นศาสตร์ที่ต้องเอาหลาย ๆ วิชารวมกัน
หลัก ๆ ก็วิชา -> Machine Learning(สาขาหนึ่งของวิชา AI) + วิชาสถิติ + ฐานข้อมูล
.
สำหรับประโยชน์ของ Data mining
ทำให้การค้นหาความรู้จากข้อมูล
มันอิจฉริยะขึ้นครับ ไม่ต้องทำมือเอง
แต่ใช้คอมทำให้อัตโนมัติ ซึ่งจะมีประโยชน์มาก ๆ ตัวอย่าง เช่น
.
-ใช้จัดระดับความเสี่ยงของผู้ขอเครดิต เป็นระดับต่ำ กลาง และสูง
-ทำนายพฤติกรรมการซื้อสินค้า ว่าลูกค้าจะหยิบอะไรใส่รถเข็น
-หาความเสี่ยงของผู้ป่วยเป็นโรงมะเร็ง จากผู้ติดเชื้อไวรัสโคโรน่า
-หาเนื้อคู่เรา (อันนี้ล้อเล่นนะ ไม่รู้ว่ามีใครสนใจทำจริงเปล่า อิ ๆ ๆ)
และประโยชน์อื่น ๆ อีกเยอะมาก เป็นต้น
.
** หมายเหตุ
Data mining มันมีมานานแล้ว สมัยยังไม่ดังเท่าไร
ไม่เหมือน Data Science ซึ่งจะรู้จักมากกว่าในยุคนี้
เพราะเด็กม. 5 ก็จะได้เรียน Data Science กันแหละ
ในวิชาหลักสูตรใหม่แกะกล่องชื่อ "วิทยาการคำนวณ"
.
ถ้าเปรียบเทียบกับงาน
Data analysis กับ Data science
มันก็คือซับเซทหนึ่งนั่นเองตามรูปที่โพสต์
ซึ่งวิชาพวกนี้หนีไม่พ้นโยงใยไปในเรื่อง Machine Learning(สาขาหนึ่งของวิชา AI)
.
✍เขียนโดย โปรแกรมเมอร์ไทย thai progammer
.
.
++++++++++++++++++++++++++++
+++++ต่อไปขออนุญาติขายของนะ +++++
📔 หนังสือ "ปัญญาประดิษฐ์ (AI) ไม่ยาก" เข้าใจได้ด้วยเลขม. ปลาย (เนื้อหาภาษาไทย)
.
ถ้าสนใจสั่งซื้อเล่ม 1 ก็สั่งซื้อได้ที่
👉 https://www.mebmarket.com/web/index.php…
.
ขออภัยยังไม่มีเล่มกระดาษจำหน่าย มีแต่ ebook
.
ส่วนตัวอย่างหนังสือ ก็ดูได้ลิงค์นี้
👉 https://www.dropbox.com/s/fg8l38hc0k9b…/chapter_example.pdf…
.

✍เขียนโดย โปรแกรมเมอร์ไทย thai programmer
🤓 What is Data mining in computer subjects?
I'll tell you about it.
This subject translates right to ′′ data mining
The metaphor is similar to us to a mining that digs from underground.
.
But the mine is mine of raw data that is large.
.
This time I think about SQL order time. We will know that.
Where is the information in (Table)?
When we use select khả s̄ạ̀ng from.... where...
You can choose the information you want.
.
But if it's a treasury of raw data.
With lots of quantities like
There are a lot of websites in the internet world.
These information will be used by SQL.
Info is not separate yet.
.
And this is what we need to know.
′′ Knowledge ′′ or ′′ Knowledge ′′
Knowledge of not using Cory by SQL
Knowledge Discovery in Database - KDD)
Which is a pattern (pattern) and a relationship (associations) hidden in that data set.
.
🤔 which is the question, how can we find it?
Under raw data, there are plenty of them left.
Plus, it's hard to find, brothers?
.
So we can't escape. Let's do data mines.
What a difference to mining
Until we meet, coal tin, diamonds, gold, etc.
Oh, it takes a drilling technology to find it.
It's not a banana item.
Because it takes worth to get out of stock.
I have found a lot of rubble, rubble, and graphic. I can get full of it.
Wishlist is so little
.
Data mining as well.
Until I do the digging and digging.
I got the knowledge.
It can't be done easily. It takes a computer to do.
Algorithm needs to be automated
Handmade. I can't take it. I die.
.
For this reason, Data mining subject.
So it was born in the 1970 s.
It's a science that takes many subjects together.
Mainly subject -> Machine Learning (one branch of AI subject) + statistics wichā database
.
For the sake of Data mining
Make a knowledge search from information
It's a genius. Don't have to do it yourself.
But using a computer to automate which is very useful. Samples like.
.
- Take the risk of creditors low, middle and high level
- predict product buying behavior that customers will add something to cart
- Finding the risk of cancer patients from coronavirus infected people.
- Looking for my soulmate (I'm kidding. I don't know if anyone wants to do it for real. Haha.)
And many other benefits etc.
.
** Note **
Data mining. It has been a long time. When I wasn't famous.
Unlike Data Science which will know more in this era.
Because of the middle school boy. 5 I will be studying Data Science.
In a new course, unpacking the name ′′ Computational Science ′′
.
If compare to the work
Data analysis กับ Data science
It's a subtitle, one according to the photos posted.
The subjects are unavailable to machine learning (one branch of AI)
.
✍ Written by Thai programmer thai progammer
.
.
++++++++++++++++++++++++++++
+++++ Next, I ask for permission to sell items +++++
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✍ Written by Thai programmer thai coderTranslated

Tags: sql world

โปรแกรมเมอร์ไทย Thai programmer

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เพจนี้เปิดใช้งานเมื่อ 22 มิถุนายน 2014 เพจนี้เน้นหนักไป ทางการเขียนโปรแกรม คอมพิวเตอร์ และไอที ล้วน ๆ แบบฮาร์ดคอ หรือจะไร้สาระ มุกตลกขำ

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sql world 在 prasertcbs Youtube 的最佳貼文

By prasertcbs

2018-11-19 17:43:14 有 1,671 人看過有 15 人喜歡

ดาวน์โหลดไฟล์ที่ใช้ในคลิปได้ที่ ► https://github.com/Microsoft/sql-server-samples/releases/tag/wide-world-importers-v1.0
เชิญสมัครเป็นสมาชิกของช่องนี้ได้ที่ ► https://www.youtube.com/subscription_center?add_user=prasertcbs
สอน MySQL ► https://www.youtube.com/playlist?list=PLoTScYm9O0GFmJDsZipFCrY6L-0RrBYLT
สอน PostgreSQL ► https://www.youtube.com/playlist?list=PLoTScYm9O0GGi_NqmIu43B-PsxA0wtnyH
สอน Microsoft SQL Server 2012, 2014, 2016, 2017 ► https://www.youtube.com/playlist?list=PLoTScYm9O0GH8gYuxpp-jqu5Blc7KbQVn
สอน SQLite ► https://www.youtube.com/playlist?list=PLoTScYm9O0GHjYJA4pfG38M5BcrWKf5s2
สอน SQL สำหรับ Data Science ► https://www.youtube.com/playlist?list=PLoTScYm9O0GGq8M6HO8xrpkaRhvEBsQhw
การเชื่อมต่อกับฐานข้อมูล (SQL Server, MySQL, SQLite) ด้วย Python ► https://www.youtube.com/playlist?list=PLoTScYm9O0GEdZtHwU3t9k3dBAlxYoq59
การใช้ Excel ในการทำงานร่วมกับกับฐานข้อมูล (SQL Server, MySQL, Access) ► https://www.youtube.com/playlist?list=PLoTScYm9O0GGA2sSqNRSXlw0OYuCfDwYk
#prasertcbs_SQL #prasertcbs #prasertcbs_MySQL

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สอนการใช้งาน Excel, VBA, Macro, Word, PowerPoint, SQL, การเขียนโปรแกรมด้วยภาษาต่าง ๆ Python, PHP, Java, C, C# พร้อมตัวอย่างการประยุกต์ การทำเหมืองข้อมูล Data mining, Machine learning การใช้โปรแกรม R, ggplot2 สื่อการสอนโดย รศ.ดร. ประเสริฐ คณาวัฒนไชย คณะพาณิชศาสตร์และการบัญชี จุฬาลงกรณ์มหาวิทยาลัย Assoc.Prof. Dr. Prasert Kanawattanachai Chulalongkorn Business School

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