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在introduction to computer vision這個產品中，有6篇Facebook貼文，粉絲數超過10萬的網紅BorntoDev，也在其Facebook貼文中提到， 🔥 ด่วนนน !! เด็ดกว่านี้ไม่มีอีกแล้ว กับ คลาส "Introduction to Deep Learning" จาก MIT ที่เปิดให้ทางบ้านเข้าเรียนกันได้ !! . หากใครสนใจสาย AI จนไปถึง De...
　同時也有1部Youtube影片，追蹤數超過1萬的網紅translation，也在其Youtube影片中提到，#'94年にコナミが発売したアクション作品であり、悪魔城ドラキュラシリーズ唯一のMD作品でもある。1917年の第一次大戦中ヨーロッパを舞台とし、外伝作の位置付けだったが、後に本作の主人公やその血縁が出るシリーズ作も発売されるなど、現在では正統なドラキュラシリーズの一作に数えられる。 制作時、ゲーム誌...

introduction to computer vision 在 BorntoDev Facebook 的精選貼文

BorntoDev By BorntoDev

2020-12-01 09:00:02 有 347 人按讚
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🔥 ด่วนนน !! เด็ดกว่านี้ไม่มีอีกแล้ว กับ คลาส "Introduction to Deep Learning" จาก MIT ที่เปิดให้ทางบ้านเข้าเรียนกันได้ !!
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หากใครสนใจสาย AI จนไปถึง Deep Learning แบบลึกลงเท่าไหร่ก็ไม่มืดมน แอดแนะนำหลักสูตรนี้เป็นอย่างยิ่ง
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✅ เพราะอะไรหนะหรือออ เพราะว่า เขาไม่ได้แค่สอน Concept แต่พาไปลุยจริงในการประยุกต์ทั้ง Computer vision, natural language processing, biology และ อื่น ๆ อีกเพียบบ <3
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เอาเป็นว่าใครอยากลอง ก็มาเรียนกันได้ มีทั้ง Lecture และ Lab ให้มาเรียนกัน บอกเลยว่าตื่นเต้นจัดด ๆ
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👉 สนใจลุยได้ที่นี่เลยยย > http://introtodeeplearning.com/
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"เรียนแล้วฟีดแบคเป็นยังไงบอกแอดด้วยนะคร้าบบ :D"
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#borntoDev - 🦖 สร้างการเรียนรู้ที่ดีสำหรับสายไอทีในทุกวัน

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BorntoDev (www.BorntoDev.com) เว็บไซต์ที่เกี่ยวกับการสอนการพัฒนาโปรแกรมหรือผลงานทางด้านคอมพิวเตอร์ จัดทำขึ้นเมื่อ 27 ตุลาคม 2556 โดยมีจุดมุ่งหมายเพื่อต้องการให้ผู้ที่สนใจพัฒนาซอฟต์แวร์ แอปพลิเคชั่น หรือ ผลงานทางด้านคอมพิวเตอร์ได้มีแหล่งในการเรียนรู้ที่ง่าย และ สนุก ไม่เน้นทฤษฏีจนทำให้ท้อหรือถอดใจไปก่อนจัดทำขึ้นโดยกลุ่มคนที่ต้องการจะเห็นความเปลี่ยนแปลงทางด้านเทคโนโลยี และ มีความหวังว่าจะมีนักพัฒนารุ่นใหม่ของไทยที่มีผลงานที่ยิ่งใหญ่ขึ้นมาได้ โดยจะใช้หลักการ “เน้นสนุก สอนเข้าใจ ทำได้จริง” สามารถเข้ามาเรียนรู้การพัฒนาซอฟต์แวร์ได้ตั้งแต่ระดับมัธยมศึกษาจนถึงนักศึกษาในมหาวิทยาลัย และ บุคคลทั่วไปที่สนใจในการพัฒนาซอฟต์แวร์และเทคโนโลยีคอมพิวเตอร์ ซึ่งในปัจจุบันนี้ถือว่ามีความสำคัญกับทุกสาขาอาชีพ และ เข้ามาเกี่ยวข้องกับทุกๆอย่างในชีวิตของเรา
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introduction to computer vision 在 Scholarship for Vietnamese students Facebook 的最讚貼文

Scholarship for Vietnamese students By Scholarship for Vietnamese students

2020-10-24 16:56:00 有 394 人按讚
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HỌC DATA SCIENCE NHƯ THẾ NÀO?
#datascience

Helu mấy em, dạo này có rất nhiều bạn sinh viên hỏi chị về định hướng theo 1 ngành siêu hot là Data Science đấy! Vậy ngồi xuống đây đọc một bài viết hay về nghề Data Science nhé. Đây là ngành nghề được dự đoán sẽ có nhu cầu cao nhất trong tương lai đó. Bài viết này sẽ đưa ra các bước và nguồn cho các bạn muốn học Data Science đó, đọc xem và share cho các bạn còn loay hoay nha!
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I. Học lập trình:
Một Data Scientist (DStist) không thể không biết lập trình, dù không cần thiết phải giỏi như một lập trình viên nhưng phải đủ khả năng viết được những chương trình cơ bản. Từ khi nhập học tới giờ, từ một đứa mà kiến thức lập trình là con số 0 tròn trình, mình đã học qua R, Java, Python, SQL (kì tới sẽ có cả NoSQL nữa). Học tới đâu là sử dụng luôn tới đấy nên thường mình phải tự học thêm rất nhiều để có thể hiểu được logic và cú pháp của ngôn ngữ lập trình đó. Ngôn ngữ quan trọng nhất, phổ biến nhất dành cho DStist là Python với thư viện khổng lồ. Xếp sau Python là R, rất mạnh về phân tích thống kê. Năm ngoái mình được Khoa Toán thuê viết một App (ShinyApp) tương tác dành cho một dự án nghiên cứu của Bang sử dụng ngôn ngữ này.

Vậy học lập trình ở đâu?
https://www.tutorialspoint.com/

Trang này thì gi gỉ gì gi cái gì cũng có, thích học gì có ngay cái đó. Còn nhớ năm ngoái mình cực kỳ đuối khi các thầy bắt học thêm Java, với lý do rằng DStist thường hay phải làm việc trực tiếp với lập trình viên, vậy thì phải học để có thể trò chuyện với nhau được. Mình đã phải đọc thêm sách, đi học thêm phụ đạo, rồi lại đọc mòn mỏi trên trang này để theo kịp các bạn trên lớp. Kết quả là cuối kì, mình tự viết được cả trò chơi và thậm chí còn lập trình được công thức toán thống kê cho thư viện Java đấy.

2. https://codingbat.com/
Đây là nơi mình luyện viết code, từ những ứng dụng đơn giản nhất chỉ vài ba dòng. Trình độ của mình đã lên rất nhanh sau khi hoàn thành phân nửa số bài tập trên này.

3. https://www.datacamp.com/
Mình chưa sử dụng trang này bao giờ, nhưng được quảng cáo khá nhiều. Trên này có các khóa học miễn phí R và Python thiết kế riêng cho DS. Thích hợp cho những ai mới bắt đầu.

4. https://www.udemy.com/.../development/programming-languages/

5. https://www.codecademy.com/catalog/subject/all

Đây là hai trang do bạn bè mình giới thiệu. Có mấy bạn không đi học phụ đạo Java được đã trả tiền theo học trên này. Vì thường xuyên có giảm giá sâu nên khóa học không quá đắt đỏ. Và điểm lợi thế là sẽ có chứng nhận cuối khóa, có thể củng cố thêm cho hồ sơ xin học hoặc xin việc của bạn.

II. Học thống kê:
Đã làm việc với dữ liệu là phải hiểu lý thuyết thống kê, chí ít cũng phải biết tới những khái niệm cơ bản như lấy mẫu (sampling), trung bình (mean), trung vị (median), độ lệch chuẩn (standard deviation), hồi quy tuyến tính (linear regression),... Nếu muốn trở thành DStist thì còn phải biết tới kiến thức thống kê nâng cao, liên quan tới machine learning. Một điều tuyệt vời là những cuốn sách thống kê hay ho nhất, tổng hợp nhất lại miễn phí, nhằm đáp ứng nhu cầu học tập về dữ liệu ngày càng cao. Hai cuốn sách mà tất cả các giáo sư Khoa Toán của mình đều sử dụng là:

The Elements of Statistical Learning (Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 2001)
Cuốn này hơn 700 trang, chia làm 18 chương, sử dụng R trong phân tích thống kê. Bản thân mình thấy sách quá hay, minh họa đầy đủ, giải thích kĩ càng, đọc tới đâu có thể copy code đến đấy để tự thử nghiệm. Dĩ nhiên bạn không cần phải đọc hết sách. Đụng tới khái niệm thống kê nào thì tra cứu tương ứng trong sách cũng được.
https://web.stanford.edu/~hastie/Papers/ESLII.pdf

2. An Introduction to Statistical Learning: With Applications in R ( Trevor Hastie, Robert Tibshirani, Daniela Witten, Gareth James, 2013)
Cuốn này cũng hay, hơn 400 trang, chia làm 10 chương, cũng dùng R. Ai ngại đọc cuốn trên thì có thể bắt đầu với cuốn này.

https://www-bcf.usc.edu/.../ISL/ISLR%20First%20Printing.pdf

III. Học Data Science - Nâng cao:
Sau khi có chút kiến thức cơ bản về lập trình và thống kê rồi thì bạn có thể sử dụng các trang sau để tìm hiểu thêm về các mảng chính của DS như artificial intelligence, computer vision, machine learning, Big Data Analytics, Business Intelligence...
https://towardsdatascience.com/

Đây là trang tổng hợp cực kì nhiều bài viết chất lượng từ các giáo sư và chuyên gia trong ngành. Có rất nhiều bài hướng dẫn chi tiết từng bước cho trình độ beginner. Mình thường đọc trên trang này về machine learning và artificial intelligence (AI). Không chỉ có những phân tích rất cặn kẽ về mặt lý thuyết, nhiều bài viết còn cung cấp ví dụ minh họa và đính kèm cả code để bạn đọc tự thử nghiệm. Ví dụ bài viết sau về Deep Learning là của một giáo sư ở Barcelona, toàn bộ Code có trong Notebook trên Google Colab. Vì chạy trên Cloud nên bạn không cần cài đặt gì mà có thể lập tức chạy chương trình ngay được, cực kì phù hợp cho những ai muốn xem qua trước và không muốn mất công cài đặt này nọ.
https://towardsdatascience.com/deep-learning-for...

2. https://www.datascienceweekly.org/
Một bạn người Na Uy trên Tandem giới thiệu cho mình về trang này, bảo rằng đang tự học machine learning ở đây. Thế là mình cũng đăng ký nhận Newsletter từ mấy hôm trước. Mỗi tuần, mình nhận được một email tổng hợp các bài viết nổi bật trong ngành. Như vậy để mình luôn nắm bắt được những xu hướng mới nhất và cập nhật những tiến bộ công nghệ mới.

3. https://www.kaggle.com/
Một đồng nghiệp người Ấn Độ chỉ cho mình trang này quá hay luôn. Đây là nơi bạn học hỏi bằng cách thực hành qua các dự án, các cuộc thi và thử thách quốc tế. Các công ty, tổ chức treo giải thưởng có khi lên tới cả 100,000$ cho đội nào chiến thắng. Chẳng hạn hiện giờ có 20 cuộc thi đấu song song, và đã có hàng ngàn đội đăng kí tham gia. Trên này cũng có các micro-courses hoàn toàn miễn phí từ Python cho tới Deep Learning dành cho beginner.
https://www.kaggle.com/learn/overview

4. https://www.coursera.org/browse/data-science
Và cuối cùng, dĩ nhiên là trên coursera cũng có khóa học miễn phí dành cho DS. Khi nào có thời gian, bạn thử đăng ký xem sao.

Trên đây là những hướng dẫn chung dành cho những ai muốn tìm hiểu về Data Science và học những kĩ năng cơ bản trước. Hi vọng giúp được các bạn đang quan tâm. Mình sẽ tiếp tục cập nhật thêm nhé.

Blog Mai Knows người chị thân thiết của Founder Hoa Dinh ở Đức
https://www.facebook.com/maiknowsnow/

Link tham khảo về lương của DStist:
https://www.burtchworks.com/.../2018-data-scientist.../

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introduction to computer vision 在 2how Facebook 的最佳貼文

2how By 2how

2020-02-22 12:08:24 有 95 人按讚
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คอร์สออนไลน์ Data Science เรียนฟรีโดยมหาวิทยาลัยศรีนครินทรวิโรฒ

//Admin ทราย

คอร์ส Data Science เรียนฟรีบนออนไลน์
จากมศว หมดเขต 29 ก.พ. นี้เท่านั้น!
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คอร์สวิทยาการข้อมูล (Data Science) กระบวนการของวิทยาการข้อมูล ภาษาโปรแกรมและกรอบความคิดในการวิเคราะห์ข้อมูล การวิเคราะห์เพื่อวินิจฉัยข้อมูล การทำความสะอาดข้อมูล การใช้เครื่องมือในการวิเคราะห์ข้อมูลด้วยวิธีการทางสถิติและการเรียนรู้ของเครื่อง การสร้างมโนภาพของข้อมูล กรณีศึกษาและหัวข้อการประยุกต์ใช้งานจริงในภาคอุตสาหกรรมหรือภาคธุรกิจ
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ใครที่สนใจสามารถลงทะเบียนเรียนได้เลยที่ https://thaimooc.org/…/course-v1:SWU-MOOC+swu014+2020…/about
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โดยคนที่จะเรียนจะต้อง Register หรือสมัครสมาชิกที่ Thai-Mooc ก่อนจึงจะเข้าเรียนได้ โดยเมื่อสมัครสมาชิกและ Sign in เรียบร้อยแล้วจะสามารถกด Enroll Course เพื่อเข้าเรียนต่อได้
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ผู้เรียนจะได้เรียนอะไรบ้าง?
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- Introduction to Data Science
- การพัฒนาต้นแบบ IoTเบื้องต้น
- Computer Vision
- เตรียมโปรแกรมที่เกี่ยวข้อง และศึกษาภาษา Python เบื้องต้น
- การสร้างชุดข้อมูลและการดำเนินการทางคณิตศาสตร์กับ Array
- Data Munging
- Data Visualization & Matplotlib
- วิศวกรรมคุณลักษณะ (Feature Engineering)

เป็นต้น
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เรียนทั้งหมดจำนวนชั่วโมงเรียนรู้ทั้งหมด 45 ชั่วโมงเรียนรู้ โดยจะมีกิจกรรมในบทเรียน แบบทดสอบระหว่างเรียน และแบบทดสอบหลังเรียน หากผู้เรียนมีคะแนนรวมทั้งหมดไม่ต่ำกว่า 70% ถือว่าผ่านเกณฑ์เพื่อรับประกาศนียบัตรในระบบได้
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คอร์สเรียนนี้จัดทำขึ้นโดยมหาวิทยาลัยศรีนครินทรวิโรฒ เป็นคอร์สเรียนออนไลน์ บนแพลตฟอร์ม Thai-Mooc ในรูปแบบ Self-Pace ที่ให้ผู้เรียนสามารถเข้ามาเรียนในเว็บไซต์ได้ตามเวลาที่สะดวก จะเรียนที่ไหน เมื่อไหร่ก็ได้ อีกทั้งยังไม่มีค่าใช้จ่ายในการเรียนอีกด้วย
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ใครที่สนใจสามารถลงทะเบียนเรียนได้เลยที่
https://thaimooc.org/…/course-v1:SWU-MOOC+swu014+2020…/about.
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introduction to computer vision 在 translation Youtube 的最佳貼文

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2019-07-04 20:11:01 有 4,020 人看過 有 40 人喜歡
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#'94年にコナミが発売したアクション作品であり、悪魔城ドラキュラシリーズ唯一のMD作品でもある。1917年の第一次大戦中ヨーロッパを舞台とし、外伝作の位置付けだったが、後に本作の主人公やその血縁が出るシリーズ作も発売されるなど、現在では正統なドラキュラシリーズの一作に数えられる。
制作時、ゲーム誌メガドライブFANにて敵キャラ、ステージトラップのアイデアを公募した「コナミ & メガドライブFAN特別共同企画」というコンテストも行われ、賞品で金、銀、銅色のオリジナルロムカセットが贈られた。
BGMは山根氏が作曲、山根氏が初めて作曲を担当した悪魔ドラキュラシリーズの作品であり、最終決戦前で「Theme of Simon」が流れるあたりは、68版やSFC版をオーバーラップさせる、ファン向けの粋な演出。

作曲：山根ミチル氏

Year: 1994.03.17(U),1994.03.18(J),1994.03.20(EU)
Manufacturer: konami
Computer: Mega drive
Sound: YM2612,SN76489
composer: Michiru yamane
--------------------------------------------------------------------------------------
00:00 01.Konami logo (コナミロゴ)
00:04 02.THE BEATING IN THE DRAKNESS (タイトル画面)
00:18 03.A VISION OF DARK SECRETS (オープニングデモ)
01:16 04.BONDS OF BRAVE MEN (プレイヤーセレクト)
01:54 05.ARDUOUS JOURNEY (全体マップ)
02:07 06.Reincarnated Soul, Part 1 (Introduction) (スタートデモ：旧ドラキュラ城)
02:21 07.Reincarnated Soul, Part 2 (ステージ 1) (ステージ1：旧ドラキュラ城)
04:10 08.The Sinking Old Sanctuary (ステージ2：アトランティス神殿)
06:07 09.The Discolored Wall (ステージ3：ピサの斜塔)
08:09 10.Beginning (武器フルパワーBGM1 - エキスパートモード限定)
09:53 11.Bloody Tears (武器フルパワーBGM2 - エキスパートモード限定)
11:01 12.Vampire Killer (武器フルパワーBGM3 - エキスパートモード限定)
12:09 13.After the Good Fight (ゲームオーバー)
12:16 14.Mysterious Curse (パスワード)
13:19 15.Iron-Blue Intention (ステージ4：ドイツ兵器工場)
15:12 16.The Prayer of a Tragic Queen (ステージ5：ベルサイユ宮殿)
16:51 17.Calling From Heaven (ステージ6：プロセルピナ城)
19:18 18.Pressure (Invincibility)
19:46 19.Messenger From Devil (中ボス戦)
20:20 20.Nothing to Lose (ステージ1：ボス直前)
21:30 21.THE 6 SERVANTS OF THE DEVIL (ステージボス戦)
22:41 22.Theme of Simon (ステージ6：ラストボス直前)
24:57 23.The Vampire's Stomach (ラストボス：ドラキュラ戦)
26:10 24.STAGE CLEAR WITH THE RED CRYSTAL (ステージクリア)
26:21 25.Orb (サントラ未収録曲)
26:24 26.Energy Orb (サントラ未収録曲)
26:31 27.Dracula Orb (サントラ未収録曲)
26:34 28.All Clear (オールクリア)
26:44 29.Together Forever (エンディング)
28:44 30.Requiem for the Nameless Victims (スタッフロール)
31:15 31.Death (ミス)
--------------------------------------------------------------------------------------

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It is a central game music channel in the generation for 'from 80s to '90 such as an arcade, household use, the PC As for the music here, the basics become the recording from a reproduction sound source
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