Udemy - Face Mask Recognition Desktop App with Deep Learning & PyQT

Files:

• Get Bonus Downloads Here.url (0.2 KB)
~Get Your Files Here ! 1. Introduction
• 1. Introduction.mp4 (23.5 MB)
• 1. Introduction.srt (3.4 KB)
2. Setting Up Project
• 1. Install Python.mp4 (16.9 MB)
• 1. Install Python.srt (2.8 KB)
• 2. Create Virtual Environment in Python.mp4 (4.7 MB)
• 2. Create Virtual Environment in Python.srt (2.7 KB)
• 3. Install Libraries like TensorFlow 2, OpenCV etc..mp4 (31.0 MB)
• 3. Install Libraries like TensorFlow 2, OpenCV etc..srt (6.0 KB)
3. Data Preparation & Preprocessing
• 1. Download Resources.html (0.3 KB)
• 10. Face Detection Load Model.mp4 (6.8 MB)
• 10. Face Detection Load Model.srt (1.9 KB)
• 11. Face Detection Blob from Image.mp4 (16.4 MB)
• 11. Face Detection Blob from Image.srt (4.0 KB)
• 12. Draw Bounding Box for Detected Face.mp4 (41.6 MB)
• 12. Draw Bounding Box for Detected Face.srt (9.0 KB)
• 13. Step - 4, Crop the Detected Face.mp4 (30.4 MB)
• 13. Step - 4, Crop the Detected Face.srt (5.0 KB)
• 14. Step - 5, Image Processing - Blob from Image (RGB mean subtraction image).mp4 (37.1 MB)
• 14. Step - 5, Image Processing - Blob from Image (RGB mean subtraction image).srt (8.5 KB)
• 15. Step - 5, Image Processing - Rotate & Flip Image.mp4 (19.4 MB)
• 15. Step - 5, Image Processing - Rotate & Flip Image.srt (3.9 KB)
• 16. Step -5, Remove Negative values and Normalize.mp4 (19.6 MB)
• 16. Step -5, Remove Negative values and Normalize.srt (4.5 KB)
• 17. Apply Data Preparation process to All images.mp4 (35.6 MB)
• 17. Apply Data Preparation process to All images.srt (9.2 KB)
• 18. Step - 6, Save Preprocessed Data in Numpy zip.mp4 (12.9 MB)
• 18. Step - 6, Save Preprocessed Data in Numpy zip.srt (3.6 KB)
• 2. Data.mp4 (36.2 MB)
• 2. Data.srt (7.3 KB)
• 2.1 1_Download_the_data.pdf (571.7 KB)
• 3. Data Preparation Process.mp4 (24.1 MB)
• 3. Data Preparation Process.srt (5.3 KB)
• 4. Data Preparation Import Required Python Libraries.mp4 (11.3 MB)
• 4. Data Preparation Import Required Python Libraries.srt (4.0 KB)
• 5. Data Preparation Get all Images Path in Folder.mp4 (24.6 MB)
• 5. Data Preparation Get all Images Path in Folder.srt (6.7 KB)
• 6. Data Preparation Labeling.mp4 (8.8 MB)
• 6. Data Preparation Labeling.srt (2.0 KB)
• 7. Data Preparation Get Images Path and Labelling Images in multiple Folders.mp4 (22.1 MB)
• 7. Data Preparation Get Images Path and Labelling Images in multiple Folders.srt (2.6 KB)
• 8. Step - 3, Face Detection.mp4 (4.9 MB)
• 8. Step - 3, Face Detection.srt (1.2 KB)
• 9. Face Detection Read Image.mp4 (10.4 MB)
• 9. Face Detection Read Image.srt (2.5 KB)
4. Face Recognition Model for Mask Identification with Deep Learning
• 1. Load Numpy Zip Data into Notebook.mp4 (14.0 MB)
• 1. Load Numpy Zip Data into Notebook.srt (5.0 KB)
• 2. One Hot Encoding to target or output variable (y).mp4 (21.1 MB)
• 2. One Hot Encoding to target or output variable (y).srt (5.8 KB)
• 3. Split the Data into Train and Test sets.mp4 (10.1 MB)
• 3. Split the Data into Train and Test sets.srt (3.2 KB)
• 4. Convolutional Neural Network Architecture.mp4 (13.3 MB)
• 4. Convolutional Neural Network Architecture.srt (9.1 KB)
• 5. Develop CNN model in TensorFlow 2.mp4 (35.2 MB)
• 5. Develop CNN model in TensorFlow 2.srt (9.0 KB)
• 6. Compile CNN model, Setting Adam Optimizer & Loss Function.mp4 (20.8 MB)
• 6. Compile CNN model, Setting Adam Optimizer & Loss Function.srt (4.2 KB)
• 7. Train CNN model.mp4 (11.4 MB)
• 7. Train CNN model.srt (2.9 KB)
• 8. Save Deep Learning Model in TensorFlow.mp4 (24.8 MB)
• 8. Save Deep Learning Model in TensorFlow.srt (5.3 KB)
5. Predictions with Face Recognition model for Face Mask
• 1. Load TensorFlow based CNN Model in a Notebook.mp4 (19.6 MB)
• 1. Load TensorFlow based CNN Model in a Notebook.srt (6.8 KB)
• 2. Defining Labels and Setting Colors.mp4 (15.4 MB)
• 2. Defining Labels and Setting Colors.srt (4.9 KB)
• 3. Step - 1, Face Detection.mp4 (49.4 MB)
• 3. Step - 1, Face Detection.srt (13.3 KB)
• 4. Step -2, Data Preprocess.mp4 (31.2 MB)
• 4. Step -2, Data Preprocess.srt (6.9 KB)
• 5. Step - 3, Get Predictions from CNN Model for Face Mask.mp4 (33.7 MB)
• 5. Step - 3, Get Predictions from CNN Model for Face Mask.srt (6.2 KB)
• 6. Generate text for Prediction info.mp4 (28.6 MB)
• 6. Generate text for Prediction info.srt (5.0 KB)
• 7. Get Face Mask Prediction to an Image.mp4 (33.5 MB)
• 7. Get Face Mask Prediction to an Image.srt (5.7 KB)
• 8. Real Time Face Mask Prediction.mp4 (28.5 MB)
• 8. Real Time Face Mask Prediction.srt (5.6 KB)
6. PyQt Basics
• 1. What you will Develop.mp4 (13.6 MB)
• 1. What you will Develop.srt (1.5 KB)
• 10. QLabel.mp4 (30.7 MB)
• 10. QLabel.srt (7.5 KB)
• 11. QLineEdit.mp4 (12.0 MB)
• 11. QLineEdit.srt (3.0 KB)
• 12. QPushButton.mp4 (9.0 MB)
• 12. QPushButton.srt (2.5 KB)
• 13. QComboBox.mp4 (9.8 MB)
• 13. QComboBox.srt (2.2 KB)
• 14. Placing & Arranging Widgets.mp4 (5.0 MB)
• 14. Placing & Arranging Widgets.srt (2.2 KB)
• 15. Placing Widgets using QHBoxLayout and QVBoxLayout.mp4 (41.1 MB)
• 15. Placing Widgets using QHBoxLayout and QVBoxLayout.srt (9.3 KB)
• 16. Signals and Slots.mp4 (24.3 MB)
• 16. Signals and Slots.srt (4.5 KB)
• 17. Backend Operations in PyQt.mp4 (48.9 MB)
• 17. Backend Operations in PyQt.srt (8.7 KB)
• 2. Install Visual Studio Code.mp4 (28.5 MB)
• 2. Install Visual Studio Code.srt (8.4 KB)
• 3. Setting Up Project.mp4 (28.5 MB)
• 3. Setting Up Project.srt (8.4 KB)
• 4. Install PyQt and Connect VS code to Virtual Environment.mp4 (5.4 MB)