Download Udemy - Data Science - CNN and OpenCV - Breast Cancer Detection Torrent

Torrent Contents Size: 1.2 GB

Udemy - Data Science - CNN and OpenCV - Breast Cancer Detection

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1. About Convolutional Neural Network (CNN).mp4	MP4	12.5 MB
1. About Data Augmentation.mp4	MP4	18.1 MB
1. About Data Generators.mp4	MP4	15 MB
1. About Epoch and Batch Size.mp4	MP4	5.7 MB
1. About Model Checkpoint.mp4	MP4	6.2 MB
1. Creating a common method to get the number of files from a directory.mp4	MP4	7.7 MB
1. Full Project Code.html	HTML	102.4 B
1. Loading the ResNet50 model from drive.mp4	MP4	27.5 MB
1. Loading the custom CNN model from drive.mp4	MP4	16.7 MB
1. Model Building using ResNet50.mp4	MP4	38.7 MB
1. Predicting on the test data using ResNet50 and Custom CNN Model.mp4	MP4	29.2 MB
1. Project Overview.mp4	MP4	6.7 MB
1. Role of Optimizer in Deep Learning.mp4	MP4	17.5 MB
1. Understanding the dataset and the folder structure.mp4	MP4	26.8 MB
1. What you can do next to increase model’s prediction capabilities..mp4	MP4	25.2 MB
2. About Adam Optimizer.mp4	MP4	5.2 MB
2. About Classification Report.mp4	MP4	7.1 MB
2. About OpenCV.mp4	MP4	16.6 MB
2. Building a custom CNN network architecture.mp4	MP4	52.1 MB
2. Defining a method to plot training and validation accuracy and loss.mp4	MP4	17.3 MB
2. Implementing Data Augmentation techniques.mp4	MP4	30.3 MB
2. Implementing Data Generators.mp4	MP4	26.9 MB
2. Implementing Model Checkpoint.mp4	MP4	23.2 MB
2. Introduction to Google Colab.mp4	MP4	15.5 MB
2. Loading an image and predicting using the model whether the person has malignant.mp4	MP4	28.7 MB
2. Model Fitting of ResNet50, Custom CNN.mp4	MP4	40.9 MB
2. Setting up the project in Google Colab_Part 1.mp4	MP4	6.4 MB
3. About binary cross entropy loss function..mp4	MP4	11.7 MB
3. Calculating the class weights in train directory.mp4	MP4	31.9 MB
3. Classification Report in action for ResNet50 and Custom CNN Model.mp4	MP4	15.7 MB
3. Setting up the project in Google Colab_Part 2.mp4	MP4	82.9 MB
3. Understanding pre-trained models.mp4	MP4	10.8 MB
3. Understanding the project folder structure.mp4	MP4	26.9 MB
4. About Config and Create_Dataset File.mp4	MP4	82.9 MB
4. About Confusion Matrix.mp4	MP4	9.5 MB
4. About ResNet50 model.mp4	MP4	8 MB
4. Compiling the ResNet50 model.mp4	MP4	8.9 MB
5. Compiling the Custom CNN Model.mp4	MP4	4.8 MB
5. Computing the confusion matrix and using the same to derive the accuracy, sensit.mp4	MP4	19.3 MB
5. Importing the Libraries.mp4	MP4	33.5 MB
5. Understanding Conv2D, Filters, Relu activation, Batch Normalization, MaxPooling2.mp4	MP4	22.8 MB
6. About AUC-ROC.mp4	MP4	5.7 MB
6. Plotting the count of data against each class in each directory.mp4	MP4	27.7 MB
7. Computing the AUC-ROC.mp4	MP4	6.2 MB
7. Plotting some samples from both the classes.mp4	MP4	34.8 MB
8. Plot training and validation accuracy and loss.mp4	MP4	8.8 MB
9. SerializeWriting the model to disk.mp4	MP4	17 MB
Bonus Resources.txt	TXT	409.6 B
CM_TrainingHistoryPlot.png	PNG	25.8 KB
CM_weights-010-0.3063.hdf5	HDF5	42.3 MB
Detect_BreastCancer.ipynb	IPYNB	16.1 KB
Get Bonus Downloads Here.url	URL	204.8 B
Kaggle Link.txt	TXT	102.4 B
RN_TrainingHistoryPlot.png	PNG	23.8 KB
RN_weights-009-0.3958.hdf5	HDF5	96.5 MB
benign.png	PNG	5.9 KB
config.py	PY	1.1 KB
conv_bc_model.py	PY	3.4 KB
create_dataset.py	PY	1.9 KB
getPaths.py	PY	1 KB
malignant.png	PNG	6.6 KB
train_CustomModel_32_conv_20k.ipynb	IPYNB	787.6 KB
train_ResNet50_32_20k.ipynb	IPYNB	843.1 KB

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Udemy - Data Science - CNN and OpenCV - Breast Cancer Detection

Torrent Contents Size: 1.2 GB

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