| 1. About Convolutional Neural Network (CNN).mp4 | 12.5 MB | ||
| 1. About Data Augmentation.mp4 | 18.1 MB | ||
| 1. About Data Generators.mp4 | 15 MB | ||
| 1. About Epoch and Batch Size.mp4 | 5.7 MB | ||
| 1. About Model Checkpoint.mp4 | 6.2 MB | ||
| 1. Creating a common method to get the number of files from a directory.mp4 | 7.7 MB | ||
| 1. Full Project Code.html | 102.4 B | ||
| 1. Loading the ResNet50 model from drive.mp4 | 27.5 MB | ||
| 1. Loading the custom CNN model from drive.mp4 | 16.7 MB | ||
| 1. Model Building using ResNet50.mp4 | 38.7 MB | ||
| 1. Predicting on the test data using ResNet50 and Custom CNN Model.mp4 | 29.2 MB | ||
| 1. Project Overview.mp4 | 6.7 MB | ||
| 1. Role of Optimizer in Deep Learning.mp4 | 17.5 MB | ||
| 1. Understanding the dataset and the folder structure.mp4 | 26.8 MB | ||
| 1. What you can do next to increase model’s prediction capabilities..mp4 | 25.2 MB | ||
| 2. About Adam Optimizer.mp4 | 5.2 MB | ||
| 2. About Classification Report.mp4 | 7.1 MB | ||
| 2. About OpenCV.mp4 | 16.6 MB | ||
| 2. Building a custom CNN network architecture.mp4 | 52.1 MB | ||
| 2. Defining a method to plot training and validation accuracy and loss.mp4 | 17.3 MB | ||
| 2. Implementing Data Augmentation techniques.mp4 | 30.3 MB | ||
| 2. Implementing Data Generators.mp4 | 26.9 MB | ||
| 2. Implementing Model Checkpoint.mp4 | 23.2 MB | ||
| 2. Introduction to Google Colab.mp4 | 15.5 MB | ||
| 2. Loading an image and predicting using the model whether the person has malignant.mp4 | 28.7 MB | ||
| 2. Model Fitting of ResNet50, Custom CNN.mp4 | 40.9 MB | ||
| 2. Setting up the project in Google Colab_Part 1.mp4 | 6.4 MB | ||
| 3. About binary cross entropy loss function..mp4 | 11.7 MB | ||
| 3. Calculating the class weights in train directory.mp4 | 31.9 MB | ||
| 3. Classification Report in action for ResNet50 and Custom CNN Model.mp4 | 15.7 MB | ||
| 3. Setting up the project in Google Colab_Part 2.mp4 | 82.9 MB | ||
| 3. Understanding pre-trained models.mp4 | 10.8 MB | ||
| 3. Understanding the project folder structure.mp4 | 26.9 MB | ||
| 4. About Config and Create_Dataset File.mp4 | 82.9 MB | ||
| 4. About Confusion Matrix.mp4 | 9.5 MB | ||
| 4. About ResNet50 model.mp4 | 8 MB | ||
| 4. Compiling the ResNet50 model.mp4 | 8.9 MB | ||
| 5. Compiling the Custom CNN Model.mp4 | 4.8 MB | ||
| 5. Computing the confusion matrix and using the same to derive the accuracy, sensit.mp4 | 19.3 MB | ||
| 5. Importing the Libraries.mp4 | 33.5 MB | ||
| 5. Understanding Conv2D, Filters, Relu activation, Batch Normalization, MaxPooling2.mp4 | 22.8 MB | ||
| 6. About AUC-ROC.mp4 | 5.7 MB | ||
| 6. Plotting the count of data against each class in each directory.mp4 | 27.7 MB | ||
| 7. Computing the AUC-ROC.mp4 | 6.2 MB | ||
| 7. Plotting some samples from both the classes.mp4 | 34.8 MB | ||
| 8. Plot training and validation accuracy and loss.mp4 | 8.8 MB | ||
| 9. SerializeWriting the model to disk.mp4 | 17 MB | ||
| Bonus Resources.txt | 409.6 B | ||
| CM_TrainingHistoryPlot.png | 25.8 KB | ||
| CM_weights-010-0.3063.hdf5 | 42.3 MB | ||
| Detect_BreastCancer.ipynb | 16.1 KB | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| Kaggle Link.txt | 102.4 B | ||
| RN_TrainingHistoryPlot.png | 23.8 KB | ||
| RN_weights-009-0.3958.hdf5 | 96.5 MB | ||
| benign.png | 5.9 KB | ||
| config.py | 1.1 KB | ||
| conv_bc_model.py | 3.4 KB | ||
| create_dataset.py | 1.9 KB | ||
| getPaths.py | 1 KB | ||
| malignant.png | 6.6 KB | ||
| train_CustomModel_32_conv_20k.ipynb | 787.6 KB | ||
| train_ResNet50_32_20k.ipynb | 843.1 KB | ||
| ▲ 64 total files | |||
Data Science: CNN & OpenCV: Breast Cancer Detection
https://DevCourseWeb.com
Published 12/2022
Created by AutomationGig .
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 48 Lectures ( 2h 13m ) | Size: 1.13 GB
A practical hands on Deep Learning Project on building a Breast Cancer Detection model using Tensorflow, CNN and OpenCV
What you'll learn
Data Analysis and Understanding
Data Augumentation
Data Generators
Model Checkpoints
CNN and OpenCV
Pretrained Models like ResNet50
Compiling and Fitting a customized pretrained model
Model Evaluation
Model Serialization
Classification Metrics
Model Evaluation
Using trained model to detect Pneumonia using Chest XRays
Requirements
Basics knowledge of Python, Neural Networks and OpenCV is recommended
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