[ DevCourseWeb.com ] Udemy - Data Science - CNN and OpenCV - Breast Cancer Detection

~Get Your Files Here !/15. Project Files and Code/output/RN_weights-009-0.3958.hdf5

~Get Your Files Here !/2. Data Understanding & Importing Libraries/3. Setting up the project in Google Colab_Part 2.mp4

~Get Your Files Here !/2. Data Understanding & Importing Libraries/4. About Config and Create_Dataset File.mp4

~Get Your Files Here !/7. Model Building/2. Building a custom CNN network architecture.mp4

~Get Your Files Here !/12. Using ResNet50 model to detect presence of malignant cells in images/2. Loading an image and predicting using the model whether the person has malignant.mp4

~Get Your Files Here !/15. Project Files and Code/output/CM_weights-010-0.3063.hdf5

~Get Your Files Here !/10. Fitting the Model/2. Model Fitting of ResNet50, Custom CNN.mp4

~Get Your Files Here !/7. Model Building/1. Model Building using ResNet50.mp4

~Get Your Files Here !/2. Data Understanding & Importing Libraries/7. Plotting some samples from both the classes.mp4

~Get Your Files Here !/2. Data Understanding & Importing Libraries/5. Importing the Libraries.mp4

~Get Your Files Here !/3. Common Methods for plotting and class weight calculation/3. Calculating the class weights in train directory.mp4

~Get Your Files Here !/4. Data Augmentation/2. Implementing Data Augmentation techniques.mp4

~Get Your Files Here !/11. Model Evaluation/1. Predicting on the test data using ResNet50 and Custom CNN Model.mp4

~Get Your Files Here !/13. Using custom CNN model to detect presence of malignant cells in images/2. Loading an image and predicting using the model whether the person has malignant.mp4

~Get Your Files Here !/2. Data Understanding & Importing Libraries/6. Plotting the count of data against each class in each directory.mp4

~Get Your Files Here !/12. Using ResNet50 model to detect presence of malignant cells in images/1. Loading the ResNet50 model from drive.mp4

~Get Your Files Here !/5. Data Generators/2. Implementing Data Generators.mp4

~Get Your Files Here !/1. Introduction and Getting Started/3. Understanding the project folder structure.mp4

~Get Your Files Here !/2. Data Understanding & Importing Libraries/1. Understanding the dataset and the folder structure.mp4

~Get Your Files Here !/14. Future scope of work/1. What you can do next to increase model’s prediction capabilities..mp4

~Get Your Files Here !/9. ModelCheckpoint/2. Implementing Model Checkpoint.mp4

~Get Your Files Here !/6. About CNN and Pre-trained Models/5. Understanding Conv2D, Filters, Relu activation, Batch Normalization, MaxPooling2.mp4

~Get Your Files Here !/11. Model Evaluation/5. Computing the confusion matrix and using the same to derive the accuracy, sensit.mp4

~Get Your Files Here !/4. Data Augmentation/1. About Data Augmentation.mp4

~Get Your Files Here !/8. Compiling the Model/1. Role of Optimizer in Deep Learning.mp4

~Get Your Files Here !/3. Common Methods for plotting and class weight calculation/2. Defining a method to plot training and validation accuracy and loss.mp4

~Get Your Files Here !/11. Model Evaluation/9. SerializeWriting the model to disk.mp4

~Get Your Files Here !/13. Using custom CNN model to detect presence of malignant cells in images/1. Loading the custom CNN model from drive.mp4

~Get Your Files Here !/6. About CNN and Pre-trained Models/2. About OpenCV.mp4

~Get Your Files Here !/11. Model Evaluation/3. Classification Report in action for ResNet50 and Custom CNN Model.mp4

~Get Your Files Here !/1. Introduction and Getting Started/2. Introduction to Google Colab.mp4

~Get Your Files Here !/5. Data Generators/1. About Data Generators.mp4

~Get Your Files Here !/6. About CNN and Pre-trained Models/1. About Convolutional Neural Network (CNN).mp4

~Get Your Files Here !/8. Compiling the Model/3. About binary cross entropy loss function..mp4

~Get Your Files Here !/6. About CNN and Pre-trained Models/3. Understanding pre-trained models.mp4

~Get Your Files Here !/11. Model Evaluation/4. About Confusion Matrix.mp4

~Get Your Files Here !/8. Compiling the Model/4. Compiling the ResNet50 model.mp4

~Get Your Files Here !/11. Model Evaluation/8. Plot training and validation accuracy and loss.mp4

~Get Your Files Here !/6. About CNN and Pre-trained Models/4. About ResNet50 model.mp4

~Get Your Files Here !/3. Common Methods for plotting and class weight calculation/1. Creating a common method to get the number of files from a directory.mp4

~Get Your Files Here !/11. Model Evaluation/2. About Classification Report.mp4

~Get Your Files Here !/1. Introduction and Getting Started/1. Project Overview.mp4

~Get Your Files Here !/2. Data Understanding & Importing Libraries/2. Setting up the project in Google Colab_Part 1.mp4

~Get Your Files Here !/9. ModelCheckpoint/1. About Model Checkpoint.mp4

~Get Your Files Here !/11. Model Evaluation/7. Computing the AUC-ROC.mp4

~Get Your Files Here !/11. Model Evaluation/6. About AUC-ROC.mp4

~Get Your Files Here !/10. Fitting the Model/1. About Epoch and Batch Size.mp4

~Get Your Files Here !/8. Compiling the Model/2. About Adam Optimizer.mp4

~Get Your Files Here !/8. Compiling the Model/5. Compiling the Custom CNN Model.mp4

~Get Your Files Here !/15. Project Files and Code/train_ResNet50_32_20k.ipynb