韓劇電影你都看哪一部呢？論文書籍站
git version的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列線上看、影評和彩蛋懶人包
git version的問題，我們搜遍了碩博士論文和台灣出版的書籍，推薦Hartl, Michael寫的 Learn Enough Developer Tools to Be Dangerous: Git Version Control, Command Line, and Text Editors Essentials 和Liberty, Jesse的 Git for Programmers: Master Git for effective implementation of version control for your programming projects都 可以從中找到所需的評價。
另外網站Git 簡介. 版本控制(version…也說明：版本控制(version control)對於軟體開發就如同人類對於空氣一樣，已經是生存的必要 ... 本篇我將統整我所理解的Git，其中包刮Remote repository、Local repository以及 ...
這兩本書分別來自 和所出版 。
國立陽明交通大學 資訊科學與工程研究所 曾建超、黃世昆所指導 鍾明諺的 基於代碼差異之測試優先權排程方法 （2021），提出git version關鍵因素是什麼，來自於Robot Framework、自動化測試、回歸測試、優先順序、排程、微服務。
而第二篇論文長庚大學 電機工程學系 沙庫瑪所指導 Djeane Debora Onthoni的 使用深度學習方法分析ADPKD患者的非顯影和顯影電腦斷層圖像的電腦視覺任務 （2021），提出因為有 no的重點而找出了 git version的解答。
最後網站Git Tutorial Part 1: What is Version Control? - YouTube則補充：
Learn Enough Developer Tools to Be Dangerous: Git Version Control, Command Line, and Text Editors Essentials
為了解決git version 的問題，作者Hartl, Michael 這樣論述：
git version進入發燒排行的影片
เนื้อหาในคลิปนี้จะเป็นการจำลองสถานการณ์ว่าเรามีไฟล์ต่าง ๆ อยู่ใน folder ก่อนที่จะมาใช้ git และ github โดยสาธิตให้เห็นถึงขั้นตอนในการ
► 03:32 ทำ git init เพื่อสร้าง local repository ขึ้นมา
► 04:25 การสร้าง remote repository บน github
► 07:54 การกำหนด user.name และ email ของ git (สร้างไฟล์ .gitconfig)
► 09:07 การ commit change ด้วย Visual Studio Code
► 09:30 การ push (ส่ง) ไฟล์จาก local repository ไปยัง remote repository (บน github)
► 13:21 การ pull (ดึง) ไฟล์จาก remote repository (บน github) ลงมายัง local repository (local hard drive)
► 14:37 การ sync (push/pull) ไฟล์จาก local และ remote repository
เชิญสมัครเป็นสมาชิกของช่องนี้ได้ที่ ► https://www.youtube.com/subscription_center?add_user=prasertcbs
playlist สอน git เบื้องต้น ► https://www.youtube.com/playlist?list=PLoTScYm9O0GGsV1ZAyP4m_iyAbflQrKrX
playlist สอนภาษา Python ► https://www.youtube.com/playlist?list=PLoTScYm9O0GH4YQs9t4tf2RIYolHt_YwW
playlist สอนภาษาไพธอน Python OOP ► https://www.youtube.com/playlist?list=PLoTScYm9O0GEIZzlTKPUiOqkewkWmwadW
playlist สอน Python 3 GUI ► https://www.youtube.com/playlist?list=PLoTScYm9O0GFB1Y3cCmb9aPD5xRB1T11y
playlist สอนภาษา C เบื้องต้น ► https://www.youtube.com/playlist?list=PLoTScYm9O0GHHgz0S1tSyIl7vkG0y105z
playlist สอนภาษา C++ ► https://www.youtube.com/playlist?list=PLoTScYm9O0GEfZwqM2KyCBcPTVsc6cU_i
playlist สอนภาษา C# ► https://www.youtube.com/playlist?list=PLoTScYm9O0GE4trr-XPozJRwaY7V9hx8K
playlist สอนภาษา Java ► https://www.youtube.com/playlist?list=PLoTScYm9O0GF26yW0zVc2rzjkygafsILN
playlist สอนภาษา PHP เบื้องต้น ► https://www.youtube.com/playlist?list=PLoTScYm9O0GH_6LARFxozL_viEsXV2wgO
playlist สอนภาษา R เบื้องต้น ► https://www.youtube.com/playlist?list=PLoTScYm9O0GF6qjrRuZFSHdnBXD2KVICp
#prasertcbs #prasertcbs_git #prasertcbs_github
基於代碼差異之測試優先權排程方法
為了解決git version 的問題，作者鍾明諺 這樣論述：
隨著程式開發的規模日漸龐大，需要例行的測試方法來驗證每次修改後的程式完整性，而回歸測試即是屬於此種大量重複測試的類型，每次重複執行全部或是部分的相同測試，然而回歸測試的數量以及執行時間也會隨著程式規模成長，導致減緩後續修正以及持續開發的整合速度。故本篇論文以Robot Framework自動化測試工具在現有微服務架構上，提出一種基於程式差異程度以及測試案例間的優先權排程方法，從各個微服務與測試案例間建立關聯性，並產生個別的優先權值，循序從高優先權至低的順序執行，將測試執行時間最小化以及測試案例執行順序最佳化，確保在更短的時間內發現潛在的整合錯誤，以利後續開發人員修正程式碼以及加速開發與測試間
持續整合的速度。
Git for Programmers: Master Git for effective implementation of version control for your programming projects
為了解決git version 的問題，作者Liberty, Jesse 這樣論述：
使用深度學習方法分析ADPKD患者的非顯影和顯影電腦斷層圖像的電腦視覺任務
為了解決git version 的問題，作者Djeane Debora Onthoni 這樣論述：
ContentsABSTRACT. . . . . . . iTABLE OF CONTENTS. . . . . . . iiLIST OF FIGURES. . . . . . . viLIST OF TABLES. . . . . . . viiiLIST OF ABBREVIATIONS. . . . . . . ix1 Introduction 11.1 Medical Imaging . . . . . . . . . . . . . . . . . . 11.1.1 Ultrasound . . . . . . . . . . . . . . . . . . . 11.1.2
Magnetic Resonance Imaging . . . . . . . . . . . 21.1.3 Computed Tomography . . . . . . . . . . . . . . . 31.2 Artificial Intelligence . . . . . . . . . . . . . . 51.2.1 Machine Learning . . . . . . . . . . . . . . . . 51.2.1.1 Supervised Learning . . . . . . . . . . . . . . 51.2.1.2 Unsupervised
Learning . . . . . . . . . . . . . 61.2.2 Deep Learning . . . . . . . . . . . . . . . . . . 71.2.2.1 Classification Task . . . . . . . . . . . . . . 81.2.2.2 Localization Task . . . . . . . . . . . . . . . 91.2.2.3 Segmentation Task . . . . . . . . . . . . . . . 101.3 Kidney Disease . . . . . . . .
. . . . . . . . . . 111.4 Motivations . . . . . . . . . . . . . . . . . . . . 131.4.1 Non-contrast-enhanced Computed Tomography . . . . 141.4.2 Contrast-enhanced Computed Tomography . . . . . . 151.4.3 Localization and Segmentation for analyzing TKV . 151.5 Main Contributions . . . . . . . . . . .
. . . . . 161.6 Thesis Organization . . . . . . . . . . . . . . . . 162 Related works 182.1 Without Artificial Intelligence . . . . . . . . . . 182.2 With Artificial Intelligence . . . . . . . . . . . 192.2.1 Localization of ADPKD . . . . . . . . . . . . . . 192.2.2 Segmentation of ADPKD . . . .
. . . . . . . . . . 213 Automatic ADPKD Kidneys Localization Model on NCCT and CCT 233.1 Introduction . . . . . . . . . . . . . . . . . . . 233.2 Materials and Methods . . . . . . . . . . . . . . . 243.2.1 Data Acquisition . . . . . . . . . . . . . . . . 243.2.2 Ground Truth Annotation . . . . . .
. . . . . . . 253.2.3 Methods . . . . . . . . . . . . . . . . . . . . . 253.2.3.1 Preprocessing . . . . . . . . . . . . . . . . . 253.2.3.2 Dataset Partition . . . . . . . . . . . . . . . 273.2.3.3 Bounding Box Labeling . . . . . . . . . . . . . 283.2.3.4 Automatic ADPKD Kidneys Localization Model
. . .283.2.3.5 Training and Tuning Model . . . . . . . . . . . 303.2.3.6 Image-Wise and Subject-Wise Testing and Evaluation . . 313.2.4 Experimental Setup . . . . . . . . . . . . . . .. 313.2.5 Evaluation Metrics . . . . . . . . . . . . . . . 313.2.6 Evaluation Procedures . . . . . . . . . . . . .
. 323.3 Results on NCCT and CCT . . . . . . . . . . . . . . 333.3.1 Evaluation Results of Validation set on NCCT . . 333.3.2 Evaluation Results of Testing set on NCCT . . . . 333.3.3 Evaluation Results of Validation Set on CCT . . . 343.3.4 Evaluation Results of Testing set on CCT . . . . 353.4 Ev
aluation Results of Image-Wise Testing . . . . . 363.5 Evaluation Results of Subject-Wise Testing . . . . 383.6 Discussion . . . . . . . . . . . . . . . . . . . . 403.7 Conclusion . . . . . . . . . . . . . . . . . . . . 464 Automatic ADPKD kidneys Segmentation Model and TKV Estimation Modelon NC
CT and CCT 484.1 Introduction . . . . . . . . . . . . . . . . . . . 484.2 The Proposed Method . . . . . . . . . . . . . . . . 504.2.1 Data Preprocessing . . . . . . . . . . . . . . . 504.2.2 Automatic ADPKD Kidneys Segmentation . . . . . . 514.2.3 TKV Estimation Model . . . . . . . . . . . . . .
534.3 Experiment and Results . . . . . . . . . . . . . . 544.3.1 Dataset . . . . . . . . . . . . . . . . . . . . . 544.3.2 Experimental Setup . . . . . . . . . . . . . . . 554.3.3 Evaluation Metrics . . . . . . . . . . . . . . . 564.3.4 ADPKD Kidney Segmentation Results . . . . . . . . 564.3.4.1
Validation set results on NCCT . . . . . . . . 574.3.4.2 Testing set results on NCCT . . . . . . . . . . 57vii4.3.4.3 Validation set results on CCT . . . . . . . 584.3.4.4 Testing set results on CCT . . . . . . . . . . 594.3.5 TKV Estimation Results . . . . . . . . . . . . . 604.4 Discussion .
. . . . . . . . . . . . . . . . . . . 604.5 Conclusion . . . . . . . . . . . . . . . . . . . . 645 Conclusions and Future works 655.1 Conclusions . . . . . . . . . . . . . . . . . . . . 655.2 Future Works . . . . . . . . . . . . . . . . . . . 66Bibliography 67List of Figures1.1 Types of medical i
maging. . . . . . . . . . . . . . 21.2 Supervised and Unsupervised Learning algorithms based on tasks. . . . . . 61.3 Various architectures based on computer vision tasks. . . . . . . . . . . . . 81.4 Healthy kidneys. . . . . . . . . . . . . . . . . . 121.5 ADPKD kidneys on NCCT and CCT, and renal
cyst: (a) ADPKD kidneyand liver cyst on CCT; (b) ADPKD kidney and liver cyst on NCCT; (c)ADPKD kidney, liver, and spleen; (d) Renal cyst in non-ADPKD. . . . . . 143.1 Raw image and respective ground truth images: (a) Raw image; (b) Groundtruth for right kidney (Green); (c) Ground truth for left kidn
ey (Yellow); (d)Ground truth for both right (Green) and left (Yellow) kidneys. . . . . . . . 253.2 Proposed automatic ADPKD kidneys localization model framework. . . . . 263.3 Preprocessing procedures. .. . . . . . . . . . . . 263.4 The architecture of automatic ADPKD kidneys localization model, whe
rejfj, C(x, y), w, h, and V2 refer to as total number of feature maps, centerbounding box, width, height, and version 2, respectively. . . . . . . . . . . 293.5 Precision and recall curve on NCCT: (a) Right kidney; (b) Left kidney. . . . 363.6 Automatic ADPKD kidneys localization results on NCCT. .
. . . . . . . . 363.7 Precision and recall curve on CCT: (a) Right kidney; (b) Left kidney. . . . . 393.8 Automatic ADPKD kidneys localization results on CCT. . . . . . . . . . . 393.9 Precision and recall curve of our model on image-wise testing set: (a) Rightkidney; (b) Left kidney. . . . . . . .
. . . . . . . 41xiv3.10 Comparison of classification and localization loss on image-wise testing set. 423.11 Detection results: (a) ADPKD kidneys associated with liver cysts; (b)ADPKD kidneys with adjacent organs. . . . . . . . . . 423.12 Precision and recall curve of our model on subject-wise test
ing: (a) Rightkidney; (b) Left kidney. . . . . . . . . . . . . . .. 433.13 Comparison of classification and localization loss on image-wise testing set. 443.14 Detection results: (a) Small size of ADPKD kidneys; (b) Big size of ADPKDkidneys. . . . . . . . . . . . . . . . . . . . . . .. 453.15 Miscla
ssification and mislocalization example: (a) Misclassification; (b)Mislocalization. . . . . . . . . . . . . . . . . . .. 464.1 The overview of proposed method work flow. . . .. 504.2 The overview of data preprocessing. . . . . . . . 514.3 The overview of automatic ADPKD kidneys segmentation model. .
. . . . 534.4 Automatic ADPKD kidneys segmentation ROC curve on NCCT. . . . . . . 604.5 Automatic ADPKD kidneys segmentation results on NCCT. . . . . . . . . 614.6 Automatic ADPKD kidneys segmentation ROC curve on CCT. . . . . . . . 614.7 Automatic ADPKD kidneys segmentation results on CCT. . . . .
. . . . . 624.8 Validation curve for DTR: (a) Validation score on NCCT; (b) Validationscore on CCT. . . . . . . . . . . . . . . . . . . . 63List of Tables3.1 210 CT data acquisitions from 97 ADPKD patients. . . 243.2 Localization results using validation set with k-fold on NCCT. . . . . . . . 343.3
Localization results using testing set on NCCT. . . .353.4 Localization results using validation set with k-fold on CCT. . . . . . . . . 373.5 Localization results using testing set on CCT. . . . 383.6 Evaluation metrics results on image-wise testing. . .403.7 Comparison of AP and mAP with other ar
chitectures on image-wise testing. 413.8 Evaluation metrics results of subject-wise testing. .433.9 Comparison of AP and mAP with other architectures on subject-wise testing. 444.1 Segmentation results using validation set with k-fold on NCCT. . . . . . . . 584.2 Segmentation results using testing s
et on NCCT. . . .594.3 Segmentation results using validation set with k-fold on CCT. . . . . . . . . 624.4 Segmentation results using testing set on CCT. . . . 634.5 R2 score using validation set with k-fold on NCCT and CCT. . . . . . . . . 64