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1. Download Dataset.mp4 |
5.76Мб |
1. Introduction.mp4 |
14.45Мб |
10. Check the Number of Images in the Dataset.mp4 |
18.63Мб |
11. Preparing the Dataset.mp4 |
26.41Мб |
12. Visualizing a Sample Image in the Dataset.mp4 |
6.81Мб |
13. Building Multilayer-Perceptron (MLP) Network.mp4 |
1.36Мб |
14. Building the Patch Creation Layer.mp4 |
1.66Мб |
15. Displaying Patches for an Input Image.mp4 |
4.01Мб |
16. Building the Patch Encoding Layer.mp4 |
2.89Мб |
17. Building Vision Transformer.mp4 |
5.54Мб |
18. Compiling and Displaying Model.mp4 |
30.31Мб |
19. Training the Model.mp4 |
18.30Мб |
2. Artificial Intelligence.mp4 |
8.21Мб |
2. What is inside leopards_images folder.mp4 |
2.84Мб |
20. Evaluating Intersection over Union Jaccard Index.mp4 |
41.47Мб |
3.1 Association Rule Unsupervised Machine Learning in Python.html |
136б |
3.2 Classification Models Supervised Machine Learning in Python.html |
143б |
3.3 Cluster Analysis Unsupervised Machine Learning in Python.html |
140б |
3.4 Regression Analysis Supervised Machine Learning in Python.html |
143б |
3. Machine Learning.mp4 |
3.55Мб |
3. What is inside leopards_annotation folder.mp4 |
7.20Мб |
4. Deep Learning.mp4 |
9.31Мб |
4. What is the .h5 file.mp4 |
2.00Мб |
5.1 Deep Learning for Image Classification in Python with CNN.html |
142б |
5. Object Detection.mp4 |
15.37Мб |
5. Object Detection Python Code.mp4 |
2.54Мб |
6. Enabling GPU in Google Colab.mp4 |
3.03Мб |
6. How object detection is done.mp4 |
9.86Мб |
7. Is GPU connected to Colab notebook.mp4 |
6.93Мб |
7. Vision Transformer Architecture.mp4 |
15.93Мб |
8. Connect Google Colab with Google Drive.mp4 |
4.64Мб |
9. Download TensorFlow Addons.mp4 |
2.67Мб |
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Bonus Resources.txt |
386б |
detect_leopard.ipynb |
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Get Bonus Downloads Here.url |
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image_0001.jpg |
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leopard_detector.h5 |
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