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1. Bonus Lecture.html
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HTML
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1 KB
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1. Download Resources.html
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HTML
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307.2 B
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1. Install Python.mp4
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MP4
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16.9 MB
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1. Install Python.srt
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SRT
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2.8 KB
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1. Introduction.mp4
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MP4
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23.5 MB
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1. Introduction.srt
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SRT
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3.4 KB
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1. Load Numpy Zip Data into Notebook.mp4
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MP4
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14 MB
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1. Load Numpy Zip Data into Notebook.srt
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SRT
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5 KB
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1. Load TensorFlow based CNN Model in a Notebook.mp4
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MP4
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19.6 MB
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1. Load TensorFlow based CNN Model in a Notebook.srt
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SRT
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6.8 KB
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1. What you will Develop.mp4
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MP4
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13.6 MB
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1. What you will Develop.srt
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SRT
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1.5 KB
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1. What you will develop.mp4
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MP4
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10.8 MB
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1. What you will develop.srt
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SRT
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2.2 KB
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10. Face Detection Load Model.mp4
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MP4
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6.8 MB
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10. Face Detection Load Model.srt
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SRT
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1.9 KB
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10. QLabel.mp4
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MP4
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30.7 MB
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10. QLabel.srt
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SRT
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7.5 KB
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11. Face Detection Blob from Image.mp4
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MP4
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16.4 MB
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11. Face Detection Blob from Image.srt
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SRT
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4 KB
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11. QLineEdit.mp4
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MP4
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12 MB
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11. QLineEdit.srt
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SRT
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3 KB
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12. Draw Bounding Box for Detected Face.mp4
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MP4
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41.6 MB
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12. Draw Bounding Box for Detected Face.srt
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SRT
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9 KB
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12. QPushButton.mp4
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MP4
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9 MB
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12. QPushButton.srt
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SRT
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2.5 KB
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13. QComboBox.mp4
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MP4
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9.8 MB
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13. QComboBox.srt
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SRT
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2.2 KB
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13. Step - 4, Crop the Detected Face.mp4
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MP4
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30.4 MB
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13. Step - 4, Crop the Detected Face.srt
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SRT
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5 KB
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14. Placing & Arranging Widgets.mp4
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MP4
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5 MB
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14. Placing & Arranging Widgets.srt
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SRT
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2.2 KB
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14. Step - 5, Image Processing - Blob from Image (RGB mean subtraction image).mp4
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MP4
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37.1 MB
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14. Step - 5, Image Processing - Blob from Image (RGB mean subtraction image).srt
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SRT
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8.5 KB
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15. Placing Widgets using QHBoxLayout and QVBoxLayout.mp4
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MP4
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41.1 MB
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15. Placing Widgets using QHBoxLayout and QVBoxLayout.srt
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SRT
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9.3 KB
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15. Step - 5, Image Processing - Rotate & Flip Image.mp4
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MP4
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19.4 MB
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15. Step - 5, Image Processing - Rotate & Flip Image.srt
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SRT
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3.9 KB
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16. Signals and Slots.mp4
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MP4
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24.3 MB
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16. Signals and Slots.srt
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SRT
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4.5 KB
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16. Step -5, Remove Negative values and Normalize.mp4
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MP4
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19.6 MB
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16. Step -5, Remove Negative values and Normalize.srt
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SRT
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4.5 KB
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17. Apply Data Preparation process to All images.mp4
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MP4
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35.6 MB
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17. Apply Data Preparation process to All images.srt
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SRT
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9.2 KB
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17. Backend Operations in PyQt.mp4
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MP4
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48.9 MB
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17. Backend Operations in PyQt.srt
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SRT
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8.7 KB
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18. Step - 6, Save Preprocessed Data in Numpy zip.mp4
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MP4
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12.9 MB
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18. Step - 6, Save Preprocessed Data in Numpy zip.srt
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SRT
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3.6 KB
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2. Create Virtual Environment in Python.mp4
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MP4
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4.7 MB
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2. Create Virtual Environment in Python.srt
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SRT
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2.7 KB
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2. Data.mp4
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MP4
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36.2 MB
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2. Data.srt
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SRT
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7.3 KB
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2. Defining Labels and Setting Colors.mp4
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MP4
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15.4 MB
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2. Defining Labels and Setting Colors.srt
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SRT
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4.9 KB
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2. Install Visual Studio Code.mp4
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MP4
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28.5 MB
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2. Install Visual Studio Code.srt
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SRT
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8.4 KB
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2. One Hot Encoding to target or output variable (y).mp4
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MP4
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21.1 MB
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2. One Hot Encoding to target or output variable (y).srt
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SRT
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5.8 KB
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2. Setting up Visual studio code.mp4
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MP4
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7.7 MB
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2. Setting up Visual studio code.srt
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SRT
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2.5 KB
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2.1 1_Download_the_data.pdf
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PDF
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571.7 KB
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3. Create Main Window.mp4
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MP4
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7.7 MB
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3. Create Main Window.srt
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SRT
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3 KB
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3. Data Preparation Process.mp4
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MP4
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24.1 MB
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3. Data Preparation Process.srt
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SRT
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5.3 KB
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3. Install Libraries like TensorFlow 2, OpenCV etc..mp4
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MP4
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31 MB
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3. Install Libraries like TensorFlow 2, OpenCV etc..srt
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SRT
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6 KB
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3. Setting Up Project.mp4
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MP4
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28.5 MB
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3. Setting Up Project.srt
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SRT
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8.4 KB
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3. Split the Data into Train and Test sets.mp4
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MP4
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10.1 MB
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3. Split the Data into Train and Test sets.srt
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SRT
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3.2 KB
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3. Step - 1, Face Detection.mp4
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MP4
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49.4 MB
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3. Step - 1, Face Detection.srt
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SRT
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13.3 KB
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4. Convolutional Neural Network Architecture.mp4
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MP4
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13.3 MB
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4. Convolutional Neural Network Architecture.srt
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SRT
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9.1 KB
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4. Data Preparation Import Required Python Libraries.mp4
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MP4
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11.3 MB
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4. Data Preparation Import Required Python Libraries.srt
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SRT
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4 KB
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4. Install PyQt and Connect VS code to Virtual Environment.mp4
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MP4
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5.4 MB
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4. Install PyQt and Connect VS code to Virtual Environment.srt
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SRT
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1.7 KB
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4. PyQT Front End Design of Desktop App.mp4
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MP4
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30.6 MB
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4. PyQT Front End Design of Desktop App.srt
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SRT
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8.4 KB
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4. Step -2, Data Preprocess.mp4
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MP4
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31.2 MB
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4. Step -2, Data Preprocess.srt
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SRT
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6.9 KB
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5. Data Preparation Get all Images Path in Folder.mp4
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MP4
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24.6 MB
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5. Data Preparation Get all Images Path in Folder.srt
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SRT
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6.7 KB
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5. Develop CNN model in TensorFlow 2.mp4
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MP4
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35.2 MB
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5. Develop CNN model in TensorFlow 2.srt
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SRT
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9 KB
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5. Face Mask Desktop App using PyQt.html
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HTML
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102.4 B
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5. PyQt Background.mp4
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MP4
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7.7 MB
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5. PyQt Background.srt
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SRT
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3.1 KB
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5. Step - 3, Get Predictions from CNN Model for Face Mask.mp4
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MP4
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33.7 MB
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5. Step - 3, Get Predictions from CNN Model for Face Mask.srt
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SRT
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6.2 KB
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6. Compile CNN model, Setting Adam Optimizer & Loss Function.mp4
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MP4
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20.8 MB
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6. Compile CNN model, Setting Adam Optimizer & Loss Function.srt
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SRT
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4.2 KB
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6. Data Preparation Labeling.mp4
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MP4
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8.8 MB
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6. Data Preparation Labeling.srt
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SRT
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2 KB
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6. Generate text for Prediction info.mp4
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MP4
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28.6 MB
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6. Generate text for Prediction info.srt
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SRT
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5 KB
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6. Your First PyQt App with QtWidgets.mp4
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MP4
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17.4 MB
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6. Your First PyQt App with QtWidgets.srt
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SRT
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6.1 KB
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7. Data Preparation Get Images Path and Labelling Images in multiple Folders.mp4
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MP4
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22.1 MB
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7. Data Preparation Get Images Path and Labelling Images in multiple Folders.srt
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SRT
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2.6 KB
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7. Get Face Mask Prediction to an Image.mp4
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MP4
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33.5 MB
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7. Get Face Mask Prediction to an Image.srt
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SRT
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5.7 KB
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7. Qt Template.mp4
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MP4
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16.7 MB
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7. Qt Template.srt
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SRT
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4.7 KB
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7. Train CNN model.mp4
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MP4
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11.4 MB
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7. Train CNN model.srt
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SRT
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2.9 KB
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8. QtWidgets.mp4
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MP4
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4.1 MB
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8. QtWidgets.srt
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SRT
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1.8 KB
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8. Real Time Face Mask Prediction.mp4
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MP4
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28.5 MB
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8. Real Time Face Mask Prediction.srt
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SRT
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5.6 KB
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8. Save Deep Learning Model in TensorFlow.mp4
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MP4
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24.8 MB
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8. Save Deep Learning Model in TensorFlow.srt
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SRT
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5.3 KB
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8. Step - 3, Face Detection.mp4
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MP4
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4.9 MB
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8. Step - 3, Face Detection.srt
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SRT
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1.2 KB
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9. Face Detection Read Image.mp4
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MP4
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10.4 MB
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9. Face Detection Read Image.srt
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SRT
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2.5 KB
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9. QWidget.mp4
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MP4
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28.1 MB
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9. QWidget.srt
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SRT
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8.6 KB
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Bonus Resources.txt
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TXT
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307.2 B
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Get Bonus Downloads Here.url
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URL
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204.8 B
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hello_world.py
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PY
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102.4 B
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lemon.jpg
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JPG
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35.8 KB
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peach.jpg
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JPG
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34.8 KB
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qt_icon.png
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PNG
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5 KB
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qt_template.py
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PY
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2.3 KB
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raddish.jpeg
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JPEG
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56.2 KB
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sample.jpg
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JPG
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748.5 KB
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strawberry.jpg
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JPG
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41.3 KB
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