├── screenshot.png
├── README.md
├── .gitattributes
├── .gitignore
├── 3D_reconstruction
    └── main.cpp
└── Colaboratory'ye_Hoş_Geldiniz.ipynb


/screenshot.png:
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https://raw.githubusercontent.com/basharbme/3D_Reconstruction/master/screenshot.png


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/README.md:
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 1 | # 3D Reconstruction
 2 | 
 3 | **A Program Written in QT using VTK libraries to perform 3D-Reconstruction**
 4 | 
 5 | ## How to use
 6 | 
 7 | #### requirement
 8 | * VTK runtime libraries
 9 | * g++ compiler 
10 | 
11 | #### usage  
12 | ```bash 
13 | # Just past slices path to the excuteable
14 | ./path/to/your_executable_fle   /path/to/slices
15 | ```
16 | 
17 | ## Screenshot
18 | <p align="center"><img src="https://raw.githubusercontent.com/timlentse/3D_Reconstruction/master/screenshot.png" /> <p>
19 | 


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/.gitattributes:
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 1 | # Auto detect text files and perform LF normalization
 2 | * text=auto
 3 | 
 4 | # Custom for Visual Studio
 5 | *.cs     diff=csharp
 6 | *.sln    merge=union
 7 | *.csproj merge=union
 8 | *.vbproj merge=union
 9 | *.fsproj merge=union
10 | *.dbproj merge=union
11 | 
12 | # Standard to msysgit
13 | *.doc	 diff=astextplain
14 | *.DOC	 diff=astextplain
15 | *.docx diff=astextplain
16 | *.DOCX diff=astextplain
17 | *.dot  diff=astextplain
18 | *.DOT  diff=astextplain
19 | *.pdf  diff=astextplain
20 | *.PDF	 diff=astextplain
21 | *.rtf	 diff=astextplain
22 | *.RTF	 diff=astextplain
23 | 


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/.gitignore:
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 1 | # C++ objects and libs
 2 | 
 3 | *.slo
 4 | *.lo
 5 | *.o
 6 | *.a
 7 | *.la
 8 | *.lai
 9 | *.so
10 | *.dll
11 | *.dylib
12 | 
13 | # Qt-es
14 | 
15 | *.pro.user
16 | *.pro.user.*
17 | moc_*.cpp
18 | qrc_*.cpp
19 | ui_*.h
20 | Makefile*
21 | *-build-*
22 | 
23 | # QtCreator
24 | 
25 | *.autosave
26 | 
27 | # =========================
28 | # Operating System Files
29 | # =========================
30 | 
31 | # OSX
32 | # =========================
33 | 
34 | .DS_Store
35 | .AppleDouble
36 | .LSOverride
37 | 
38 | # Icon must ends with two \r.
39 | Icon

40 | 
41 | # Thumbnails
42 | ._*
43 | 
44 | # Files that might appear on external disk
45 | .Spotlight-V100
46 | .Trashes
47 | 
48 | # Windows
49 | # =========================
50 | 
51 | # Windows image file caches
52 | Thumbs.db
53 | ehthumbs.db
54 | 
55 | # Folder config file
56 | Desktop.ini
57 | 
58 | # Recycle Bin used on file shares
59 | $RECYCLE.BIN/
60 | 
61 | # Windows Installer files
62 | *.cab
63 | *.msi
64 | *.msm
65 | *.msp
66 | 


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/3D_reconstruction/main.cpp:
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  1 | 
  2 | 	/**
  3 | 	@ a little software of 3D reconstruction
  4 | 	@author: timlentse
  5 | 	@email: tinglenxan@gmail.com
  6 | 	*/
  7 | 
  8 | 	#include <vtkRenderer.h>
  9 | 	#include <vtkRenderWindow.h>
 10 | 	#include <vtkRenderWindowInteractor.h>
 11 | 	#include <vtkDICOMImageReader.h>
 12 | 	#include <vtkPolyDataMapper.h>
 13 | 	#include <vtkActor.h>
 14 | 	#include <vtkOutlineFilter.h>
 15 | 	#include <vtkCamera.h>
 16 | 	#include <vtkProperty.h>
 17 | 	#include <vtkPolyDataNormals.h>
 18 | 	#include <vtkContourFilter.h>
 19 | 	#include <vtkInteractorStyleTrackballCamera.h>
 20 | 
 21 | 	 int main(int argc, char *argv[])
 22 | 	{
 23 |        if (argc!=2) {
 24 | 	   std::cout << "usage: ./your_executable_fle   /path/to/slices"<<std::endl; }
 25 | 	// Create the renderer, the render window, and the interactor. The renderer
 26 | 	  // draws into the render window, the interactor enables mouse- and
 27 | 	  // keyboard-based interaction with the data within the render window.
 28 | 	  vtkRenderer *Render = vtkRenderer::New();
 29 | 	  vtkRenderWindow *renWin = vtkRenderWindow::New();
 30 | 		renWin->AddRenderer(Render);
 31 | 	  vtkInteractorStyleTrackballCamera *style=vtkInteractorStyleTrackballCamera::New();
 32 | 	  vtkRenderWindowInteractor *ireninter = vtkRenderWindowInteractor::New();
 33 | 		ireninter->SetInteractorStyle(style);
 34 | 		ireninter->SetRenderWindow(renWin);
 35 | 
 36 | 
 37 | 	  // The following reader is used to read a series of 2D slices (images) that compose the volume.
 38 | 		vtkDICOMImageReader *imageseries = vtkDICOMImageReader::New();
 39 | 		imageseries->SetDirectoryName(argv[1]);
 40 | 		imageseries->SetDataSpacing (3.2, 3.2, 1.5);
 41 | 
 42 | 	  // An isosurface, or contour value of 500 is known to correspond to the
 43 | 	  // skin of the patient. Once generated, a vtkPolyDataNormals filter is
 44 | 	  // is used to create normals for smooth surface shading during rendering.
 45 | 	  vtkContourFilter *skinExtractor = vtkContourFilter::New();
 46 | 		skinExtractor->SetInputConnection(imageseries->GetOutputPort());
 47 | 		skinExtractor->SetValue(0, 300);
 48 | 	  vtkPolyDataNormals *skinNormals = vtkPolyDataNormals::New();
 49 | 		skinNormals->SetInputConnection(skinExtractor->GetOutputPort());
 50 | 		skinNormals->SetFeatureAngle(60.0);
 51 | 	  vtkPolyDataMapper *skinMapper = vtkPolyDataMapper::New();
 52 | 		skinMapper->SetInputConnection(skinNormals->GetOutputPort());
 53 | 		skinMapper->ScalarVisibilityOff();
 54 | 	  vtkActor *skin = vtkActor::New();
 55 | 		skin->SetMapper(skinMapper);
 56 | 
 57 | 	  // An outline provides context around the data.
 58 | 	  vtkOutlineFilter *outlineData = vtkOutlineFilter::New();
 59 | 		outlineData->SetInputConnection(imageseries->GetOutputPort());
 60 | 	  vtkPolyDataMapper *mapOutline = vtkPolyDataMapper::New();
 61 | 		mapOutline->SetInputConnection(outlineData->GetOutputPort());
 62 | 	  vtkActor *outline = vtkActor::New();
 63 | 		outline->SetMapper(mapOutline);
 64 | 		outline->GetProperty()->SetColor(1,0,0);
 65 | 
 66 | 	  // It is convenient to create an initial view of the data. The FocalPoint
 67 | 	  // and Position form a vector direction. Later on (ResetCamera() method)
 68 | 	  // this vector is used to position the camera to look at the data in
 69 | 	  // this direction.
 70 | 		vtkCamera *aCamera = vtkCamera::New();
 71 | 		aCamera->SetViewUp (0, 0, -1);
 72 | 		aCamera->SetPosition (0, 1, 0);
 73 | 		aCamera->SetFocalPoint (0, 0, 0);
 74 | 		aCamera->ComputeViewPlaneNormal();
 75 | 
 76 | 	  // Actors are added to the renderer. An initial camera view is created.
 77 | 	  // The Dolly() method moves the camera towards the FocalPoint,
 78 | 	  // thereby enlarging the image.
 79 | 	  Render->AddActor(outline);
 80 | 	  Render->AddActor(skin);
 81 | 	  Render->SetActiveCamera(aCamera);
 82 | 	  Render->ResetCamera ();
 83 | 	  aCamera->Dolly(2);
 84 | 
 85 | 	  // Set a background color for the renderer and set the size of the
 86 | 	  // render window (expressed in pixels).
 87 | 	  Render->SetBackground(0.0,0.0,0.0);
 88 | 	  renWin->SetSize(640, 480);
 89 | 
 90 | 	  // Note that when camera movement occurs (as it does in the Dolly()
 91 | 	  // method), the clipping planes often need adjusting. Clipping planes
 92 | 	  // consist of two planes: near and far along the view direction. The
 93 | 	  // near plane clips out objects in front of the plane; the far plane
 94 | 	  // clips out objects behind the plane. This way only what is drawn
 95 | 	  // between the planes is actually rendered.
 96 | 	  Render->ResetCameraClippingRange ();
 97 | 	  // Initialize the event loop and then start it.
 98 | 	  ireninter->Initialize();
 99 | 	  ireninter->Start();
100 | 	  
101 | 	  // It is important to delete all objects created previously to prevent
102 | 	  // memory leaks. In this case, since the program is on its way to
103 | 	  // exiting, it is not so important. But in applications it is
104 | 	  // essential.
105 | 	  imageseries->Delete();
106 | 	  skinExtractor->Delete();
107 | 	  skinNormals->Delete();
108 | 	  skinMapper->Delete();
109 | 	  skin->Delete();
110 | 	  outlineData->Delete();
111 | 	  mapOutline->Delete();
112 | 	  outline->Delete();
113 | 	  aCamera->Delete();
114 | 	  ireninter->Delete();
115 | 	  renWin->Delete();
116 | 	  Render->Delete();
117 | 	  return 0;
118 | 	}
119 | 
120 | 


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/Colaboratory'ye_Hoş_Geldiniz.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "Colaboratory'ye Hoş Geldiniz",
  7 |       "provenance": [],
  8 |       "collapsed_sections": [],
  9 |       "toc_visible": true,
 10 |       "include_colab_link": true
 11 |     },
 12 |     "kernelspec": {
 13 |       "display_name": "Python 3",
 14 |       "name": "python3"
 15 |     }
 16 |   },
 17 |   "cells": [
 18 |     {
 19 |       "cell_type": "markdown",
 20 |       "metadata": {
 21 |         "id": "view-in-github",
 22 |         "colab_type": "text"
 23 |       },
 24 |       "source": [
 25 |         "<a href=\"https://colab.research.google.com/github/basharbme/3D_Reconstruction/blob/master/Colaboratory'ye_Ho%C5%9F_Geldiniz.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 26 |       ]
 27 |     },
 28 |     {
 29 |       "cell_type": "markdown",
 30 |       "metadata": {
 31 |         "id": "5fCEDCU_qrC0"
 32 |       },
 33 |       "source": [
 34 |         "<p><img alt=\"Colaboratory logo\" height=\"45px\" src=\"/img/colab_favicon.ico\" align=\"left\" hspace=\"10px\" vspace=\"0px\"></p>\n",
 35 |         "\n",
 36 |         "<h1>Colaboratory nedir?</h1>\n",
 37 |         "\n",
 38 |         "Colaboratory &#40;ya da kısaca \"Colab\"&#41;, tarayıcınızda Python'u yazmanızı ve çalıştırmanızı sağlar. Üstelik: \n",
 39 |         "- Hiç yapılandırma gerektirmez\n",
 40 |         "- GPU'lara ücretsiz erişim imkanı sunar\n",
 41 |         "- Kolay paylaşım imkanı sunar\n",
 42 |         "\n",
 43 |         "İster <strong>öğrenci</strong> ister <strong>veri bilimci</strong> ister <strong>yapay zeka araştırmacısı</strong> olun, Colab işinizi kolaylaştırabilir. Daha fazla bilgi edinmek için <a href=\"https://www.youtube.com/watch?v=inN8seMm7UI\">Colab'e Giriş</a> videosunu izleyebilir ya da aşağıdan hemen kullanmaya başlayabilirsiniz."
 44 |       ]
 45 |     },
 46 |     {
 47 |       "cell_type": "markdown",
 48 |       "metadata": {
 49 |         "id": "GJBs_flRovLc"
 50 |       },
 51 |       "source": [
 52 |         "## <strong>Başlarken</strong>\n",
 53 |         "\n",
 54 |         "Okuduğunuz doküman statik bir web sayfası değil, kod yazmanıza ve yürütmenize imkan veren <strong>Colab not defteri</strong> adında etkileşimli bir ortamdır.\n",
 55 |         "\n",
 56 |         "Örneğin, buradaki <strong>kod hücresinde</strong>, bir değeri hesaplayan, bir değişken içinde saklayan ve sonucu yazdıran kısa bir Python dizesi görebilirsiniz:"
 57 |       ]
 58 |     },
 59 |     {
 60 |       "cell_type": "code",
 61 |       "metadata": {
 62 |         "colab": {
 63 |           "base_uri": "https://localhost:8080/",
 64 |           "height": 34
 65 |         },
 66 |         "id": "gJr_9dXGpJ05",
 67 |         "outputId": "9f556d03-ec67-4950-a485-cfdba9ddd14d"
 68 |       },
 69 |       "source": [
 70 |         "seconds_in_a_day = 24 * 60 * 60\n",
 71 |         "seconds_in_a_day"
 72 |       ],
 73 |       "execution_count": null,
 74 |       "outputs": [
 75 |         {
 76 |           "output_type": "execute_result",
 77 |           "data": {
 78 |             "text/plain": [
 79 |               "86400"
 80 |             ]
 81 |           },
 82 |           "metadata": {
 83 |             "tags": []
 84 |           },
 85 |           "execution_count": 0
 86 |         }
 87 |       ]
 88 |     },
 89 |     {
 90 |       "cell_type": "markdown",
 91 |       "metadata": {
 92 |         "id": "2fhs6GZ4qFMx"
 93 |       },
 94 |       "source": [
 95 |         "Yukarıdaki hücrede kodu yürütmek için tıklayarak seçin, ardından ya kodun sol tarafındaki oynat düğmesine basın ya da \"Command/Ctrl+Enter\" klavye kısayolunu kullanın. Kodu düzenlemek için hücreyi tıklamanız yeterlidir. Sonrasında düzenlemeye başlayabilirsiniz.\n",
 96 |         "\n",
 97 |         "Bir hücrede tanımladığınız değişkenler daha sonra başka hücrelerde kullanılabilir:"
 98 |       ]
 99 |     },
100 |     {
101 |       "cell_type": "code",
102 |       "metadata": {
103 |         "colab": {
104 |           "base_uri": "https://localhost:8080/",
105 |           "height": 34
106 |         },
107 |         "id": "-gE-Ez1qtyIA",
108 |         "outputId": "94cb2224-0edf-457b-90b5-0ac3488d8a97"
109 |       },
110 |       "source": [
111 |         "seconds_in_a_week = 7 * seconds_in_a_day\n",
112 |         "seconds_in_a_week"
113 |       ],
114 |       "execution_count": null,
115 |       "outputs": [
116 |         {
117 |           "output_type": "execute_result",
118 |           "data": {
119 |             "text/plain": [
120 |               "604800"
121 |             ]
122 |           },
123 |           "metadata": {
124 |             "tags": []
125 |           },
126 |           "execution_count": 0
127 |         }
128 |       ]
129 |     },
130 |     {
131 |       "cell_type": "markdown",
132 |       "metadata": {
133 |         "id": "lSrWNr3MuFUS"
134 |       },
135 |       "source": [
136 |         "Colab not defterleri; <strong>yürütülebilir kod</strong>, <strong>zengin metin</strong>, <strong>resimler</strong>, <strong>HTML</strong>, <strong>LaTeX</strong> ve diğer öğeleri tek bir dokümanda birleştirmenizi sağlar. Oluşturduğunuz Colab not defterleri Google Drive hesabınızda saklanır. Colab not defterlerinizi arkadaşlarınızla veya iş arkadaşlarınızla kolayca paylaşabilir, not defterlerinize yorum yapmalarını, hatta düzenlemelerini sağlayabilirsiniz. Daha fazla bilgiyi <a href=\"/notebooks/basic_features_overview.ipynb\">Colab'e Genel Bakış</a> bölümünde bulabilirsiniz. Yeni bir Colab not defteri oluşturmak için yukarıdaki Dosya menüsünü ya da <a href=\"http://colab.research.google.com#create=true\">yeni bir Colab not defteri oluşturma</a> bağlantısını kullanabilirsiniz.\n",
137 |         "\n",
138 |         "Colab not defterleri, Colab tarafından barındırılan Jupyter not defterleridir. Jupyter projesi hakkında daha fazla bilgiyi <a href=\"https://www.jupyter.org\">jupyter.org</a> adresinde bulabilirsiniz."
139 |       ]
140 |     },
141 |     {
142 |       "cell_type": "markdown",
143 |       "metadata": {
144 |         "id": "UdRyKR44dcNI"
145 |       },
146 |       "source": [
147 |         "## Veri bilimi\n",
148 |         "\n",
149 |         "Colab ile popüler Python kitaplıklarının tüm avantajlarından yararlanarak veri analiz edip görselleştirebilirsiniz. Aşağıdaki kod hücresi rastgele veri oluşturmak için <strong>numpy</strong>'yi, bu veriyi görselleştirmek için de <strong>matplotlib</strong>'i kullanır. Kodu düzenlemek için hücreyi tıklamanız yeterlidir. Sonrasında düzenlemeye başlayabilirsiniz."
150 |       ]
151 |     },
152 |     {
153 |       "cell_type": "code",
154 |       "metadata": {
155 |         "colab": {
156 |           "base_uri": "https://localhost:8080/",
157 |           "height": 281
158 |         },
159 |         "id": "C4HZx7Gndbrh",
160 |         "outputId": "46abc637-6abd-41b2-9bba-80a7ae992e06"
161 |       },
162 |       "source": [
163 |         "import numpy as np\n",
164 |         "from matplotlib import pyplot as plt\n",
165 |         "\n",
166 |         "ys = 200 + np.random.randn(100)\n",
167 |         "x = [x for x in range(len(ys))]\n",
168 |         "\n",
169 |         "plt.plot(x, ys, '-')\n",
170 |         "plt.fill_between(x, ys, 195, where=(ys > 195), facecolor='g', alpha=0.6)\n",
171 |         "\n",
172 |         "plt.title(\"Sample Visualization\")\n",
173 |         "plt.show()"
174 |       ],
175 |       "execution_count": null,
176 |       "outputs": [
177 |         {
178 |           "output_type": "display_data",
179 |           "data": {
180 |             "image/png": 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xQMRAxJA3IPxMtYNv/9N78dGHjo/2oIdk0xt64bmnjavQ51TFxaGZ/Bz7pukk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\nqBkOrrz0BF79igNdDqI4jvVu6IvMjP04Ec0T0fO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DuZTgNklnJPVUtC2UtFfSYUmPSbok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\n/wEY0siNlckV2gAAAABJRU5ErkJggg==\n",
181 |             "text/plain": [
182 |               "<Figure size 432x288 with 1 Axes>"
183 |             ]
184 |           },
185 |           "metadata": {
186 |             "tags": []
187 |           }
188 |         }
189 |       ]
190 |     },
191 |     {
192 |       "cell_type": "markdown",
193 |       "metadata": {
194 |         "id": "4_kCnsPUqS6o"
195 |       },
196 |       "source": [
197 |         "Kendi verilerinizi Google Drive hesabınızdan &#40;e-tablolar dahil&#41;, GitHub'dan ve diğer pek çok kaynaktan Colab not defterlerine aktarabilirsiniz. Veri içe aktarma ve Colab'in veri bilimi için nasıl kullanılabileceği hakkında daha fazla bilgi edinmek için <a href=\"#working-with-data\">Verilerle Çalışma</a> bölümünün altındaki bağlantılara bakabilirsiniz."
198 |       ]
199 |     },
200 |     {
201 |       "cell_type": "markdown",
202 |       "metadata": {
203 |         "id": "OwuxHmxllTwN"
204 |       },
205 |       "source": [
206 |         "## Makine öğrenimi\n",
207 |         "\n",
208 |         "Colab ile bir resim veri kümesini içe aktarabilir, üzerinde bir resim sınıflandırıcıyı eğitebilir ve modeli değerlendirebilirsiniz. Hem de sadece <a href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/quickstart/beginner.ipynb\">birkaç satır kodla</a>. Colab not defterleri Google'ın bulut sunucularında kod yürütür. Yani makinenizin gücünden bağımsız olarak, <a href=\"#using-accelerated-hardware\">GPU'lar ve TPU'lar</a> dahil Google donanımının gücünden yararlanabilirsiniz. Tek ihtiyacınız olan şey bir tarayıcıdır."
209 |       ]
210 |     },
211 |     {
212 |       "cell_type": "markdown",
213 |       "metadata": {
214 |         "id": "ufxBm1yRnruN"
215 |       },
216 |       "source": [
217 |         "Colab, makine öğrenimi topluluğunda yaygın olarak şu uygulamalarla kullanılır:\n",
218 |         "- TensorFlow'u kullanmaya başlama\n",
219 |         "- Nöral ağ geliştirme ve eğitme\n",
220 |         "- TPU'lar ile deneme yapma\n",
221 |         "- Yapay zeka araştırmalarını yayma\n",
222 |         "- Eğitici oluşturma\n",
223 |         "\n",
224 |         "Makine öğrenimi uygulamalarını açıklayarak tanıtan örnek Colab not defterlerini görmek için aşağıdaki <a href=\"#machine-learning-examples\">makine öğrenimi örneklerine</a> bakabilirsiniz."
225 |       ]
226 |     },
227 |     {
228 |       "cell_type": "markdown",
229 |       "metadata": {
230 |         "id": "-Rh3-Vt9Nev9"
231 |       },
232 |       "source": [
233 |         "## Diğer Kaynaklar\n",
234 |         "\n",
235 |         "### Colab'de Not Defterleriyle Çalışma\n",
236 |         "- [Colaboratory'ye Genel Bakış](/notebooks/basic_features_overview.ipynb)\n",
237 |         "- [Markdown rehberi](/notebooks/markdown_guide.ipynb)\n",
238 |         "- [Kitaplıkları içe aktarma ve bağımlıları yükleme](/notebooks/snippets/importing_libraries.ipynb)\n",
239 |         "- [GitHub'da not defteri kaydetme ve yükleme](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)\n",
240 |         "- [Etkileşimli formlar](/notebooks/forms.ipynb)\n",
241 |         "- [Etkileşimli widget'lar](/notebooks/widgets.ipynb)\n",
242 |         "- <img src=\"/img/new.png\" height=\"20px\" align=\"left\" hspace=\"4px\" alt=\"New\"></img>\n",
243 |         " [Colab'de TensorFlow 2](/notebooks/tensorflow_version.ipynb)\n",
244 |         "\n",
245 |         "<a name=\"working-with-data\"></a>\n",
246 |         "### Verilerle Çalışma\n",
247 |         "- [Veri yükleme: Drive, E-Tablolar ve Google Cloud Storage](/notebooks/io.ipynb) \n",
248 |         "- [Grafikler: Veri görselleştirme](/notebooks/charts.ipynb)\n",
249 |         "- [BigQuery'yi kullanmaya başlama](/notebooks/bigquery.ipynb)\n",
250 |         "\n",
251 |         "### Makine Öğrenimi Hızlandırılmış Kursu\n",
252 |         "Google'ın online Makine Öğrenimi kursundan birkaç not defterini burada bulabilirsiniz. Daha fazlası için <a href=\"https://developers.google.com/machine-learning/crash-course/\">tam kurs web sitesine</a> bakın.\n",
253 |         "- [Pandas'a giriş](/notebooks/mlcc/intro_to_pandas.ipynb)\n",
254 |         "- [TensorFlow kavramları](/notebooks/mlcc/tensorflow_programming_concepts.ipynb)\n",
255 |         "- [TensorFlow ile ilk adımlar](/notebooks/mlcc/first_steps_with_tensor_flow.ipynb)\n",
256 |         "- [Nöral ağlara giriş](/notebooks/mlcc/intro_to_neural_nets.ipynb)\n",
257 |         "- [Seyrek veriler ve yerleştirilmiş öğelere giriş](/notebooks/mlcc/intro_to_sparse_data_and_embeddings.ipynb)\n",
258 |         "\n",
259 |         "<a name=\"using-accelerated-hardware\"></a>\n",
260 |         "### Hızlandırılmış Donanım Kullanma\n",
261 |         "- [GPU'lar ile TensorFlow](/notebooks/gpu.ipynb)\n",
262 |         "- [TPU'lar ile TensorFlow](/notebooks/tpu.ipynb)"
263 |       ]
264 |     },
265 |     {
266 |       "cell_type": "markdown",
267 |       "metadata": {
268 |         "id": "P-H6Lw1vyNNd"
269 |       },
270 |       "source": [
271 |         "<a name=\"machine-learning-examples\"></a>\n",
272 |         "\n",
273 |         "## Makine Öğrenimi Örnekleri\n",
274 |         "\n",
275 |         "Colaboratory'nin mümkün kıldığı etkileşimli makine öğrenimi analizlerinin uçtan uca örneklerini görmek için, <a href=\"https://tfhub.dev\">TensorFlow Hub</a>'daki modelleri kullanan bu eğiticilere bakın.\n",
276 |         "\n",
277 |         "Öne çıkan birkaç örnek:\n",
278 |         "\n",
279 |         "- <a href=\"https://tensorflow.org/hub/tutorials/tf2_image_retraining\">Bir Resim Sınıflandırıcıyı Yeniden Eğitme</a>: Çiçekleri ayırt etmek için önceden eğitilmiş bir resim sınıflandırıcının üzerine bir Keras modeli inşa eder.\n",
280 |         "- <a href=\"https://tensorflow.org/hub/tutorials/tf2_text_classification\">Metin Sınıflandırma</a>: IMDB'deki film yorumlarını <em>olumlu</em> veya <em>olumsuz</em> olarak sınıflandırır.\n",
281 |         "- <a href=\"https://tensorflow.org/hub/tutorials/tf2_arbitrary_image_stylization\">Stil Aktarımı</a>: Resimler arasında stil aktarımı yapmak için derin öğrenmeyi kullanır.\n",
282 |         "- <a href=\"https://tensorflow.org/hub/tutorials/retrieval_with_tf_hub_universal_encoder_qa\">Çok Dilli Evrensel Cümle Kodlayıcı Soru-Cevap</a>: SQuAD veri kümesinden soruları cevaplamak için bir makine öğrenimi modeli kullanır.\n",
283 |         "- <a href=\"https://tensorflow.org/hub/tutorials/tweening_conv3d\">Video İnterpolasyonu</a>: Bir videonun ilk ve son karesi arasında ne olduğunu tahmin eder.\n"
284 |       ]
285 |     }
286 |   ]
287 | }


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