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[ DevCourseWeb com ] Udemy - Data Science - CNN and OpenCV - Chest XRAY-Pneumonia Detection
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1. Introduction and Getting Started
1. Project Overview.mp4
1. Project Overview.srt
2. Introduction to Google Colab.mp4
2. Introduction to Google Colab.srt
3. Understanding the project folder structure.mp4
3. Understanding the project folder structure.srt
10. Model Evaluation
1. Predicting on the test data using both MobileNetV2 and Custom CNN Model.mp4
1. Predicting on the test data using both MobileNetV2 and Custom CNN Model.srt
2. About Classification Report.mp4
2. About Classification Report.srt
3. Classification Report in action for both MobileNetV2 and Custom CNN Model.mp4
3. Classification Report in action for both MobileNetV2 and Custom CNN Model.srt
4. Computing the confusion matrix and using the same to derive the accuracy, sensit.mp4
4. Computing the confusion matrix and using the same to derive the accuracy, sensit.srt
5. Plot training and validation accuracy and loss.mp4
5. Plot training and validation accuracy and loss.srt
6. SerializeWriting the model to disk.mp4
6. SerializeWriting the model to disk.srt
11. Using trained model to predict whether a person has Pneumonia
1. Loading the final model from drive.mp4
1. Loading the final model from drive.srt
2. Loading an image and predicting using the model whether the person has Pneumonia.mp4
2. Loading an image and predicting using the model whether the person has Pneumonia.srt
12. Project Files and Code
1. Full Project Code.html
code
Detect_Pneumonia.ipynb
Kaggle Link_chest-xray-pneumonia.txt
output
CM_16_weights-018-0.1818.hdf5
MN_16_TrainingHistoryPlot.png
MN_16_weights-016-0.2087.hdf5
sampleTest_Pictures
Normal.jpeg
Pneumonia.jpeg
train_CustomModel_16_conv_modelCheckpoint_reshuffle_data.ipynb
train_MobileNet_16_modelCheckpoint_reshuffle_data (1).ipynb
utils
config.py
conv_bc_model.py
create_dataset.py
getPaths.py
2. Data Understanding & Importing Libraries
1. Understanding the dataset and the folder structure.mp4
1. Understanding the dataset and the folder structure.srt
2. Setting up the project in Google Colab_Part1.mp4
2. Setting up the project in Google Colab_Part1.srt
3. Setting up the project in Google Colab_Part2.mp4
3. Setting up the project in Google Colab_Part2.srt
4. About Config and Create_Dataset File.mp4
4. About Config and Create_Dataset File.srt
5. Importing the Libraries.mp4
5. Importing the Libraries.srt
6. Plotting the count of data against each class in each directory.mp4
6. Plotting the count of data against each class in each directory.srt
7. Plotting some samples from both the classes.mp4
7. Plotting some samples from both the classes.srt
3. Common Methods for plotting and class weight calculation
1. Creating a common method to get the number of files from a directory.mp4
1. Creating a common method to get the number of files from a directory.srt
2. Defining a method to plot training and validation accuracy and loss.mp4
2. Defining a method to plot training and validation accuracy and loss.srt
3. Calculating the class weights in train directory.mp4
3. Calculating the class weights in train directory.srt
4. Data Augmentation
1. About Data Augmentation.mp4
1. About Data Augmentation.srt
2. Implementing Data Augmentation techniques.mp4
2. Implementing Data Augmentation techniques.srt
5. Data Generators
1. About Data Generators.mp4
1. About Data Generators.srt
2. Implementing Data Generators.mp4
2. Implementing Data Generators.srt
6. Model Building
1. About Convolutional Neural Network (CNN).mp4
1. About Convolutional Neural Network (CNN).srt
2. About OpenCV.mp4
2. About OpenCV.srt
3. Understanding pre-trained models.mp4
3. Understanding pre-trained models.srt
4. About MobileNetV2 model.mp4
4. About MobileNetV2 model.srt
5. Loading the MobileNetV2 classifier.mp4
5. Loading the MobileNetV2 classifier.srt
6. Building a new fully-connected (FC) head.mp4
6. Building a new fully-connected (FC) head.srt
7. Building the final MobileNetV2 model.mp4
7. Building the final MobileNetV2 model.srt
8. Understanding Conv2D, Filters, Relu activation, Batch Normalization, MaxPooling2.mp4
8. Understanding Conv2D, Filters, Relu activation, Batch Normalization, MaxPooling2.srt
9. Building a custom CNN network architecture.mp4
9. Building a custom CNN network architecture.srt
7. Compiling the Model
1. Role of Optimizer in Deep Learning.mp4
1. Role of Optimizer in Deep Learning.srt
2. About Adam Optimizer.mp4
2. About Adam Optimizer.srt
3. About binary cross entropy loss function..mp4
3. About binary cross entropy loss function..srt
4. Putting all together for MobileNetV2.mp4
4. Putting all together for MobileNetV2.srt
5. Putting all together for Custom CNN Model.mp4
5. Putting all together for Custom CNN Model.srt
8. ModelCheckpoint
1. About Model Checkpoint.mp4
1. About Model Checkpoint.srt
2. Implementing Model Checkpoint.mp4
2. Implementing Model Checkpoint.srt
9. Fitting the Model
1. About Epoch and Batch Size.mp4
1. About Epoch and Batch Size.srt
2. MobileNetV2 and Custom CNN Model Fitting.mp4
2. MobileNetV2 and Custom CNN Model Fitting.srt
Bonus Resources.txt
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