| 1. About Convolutional Neural Network (CNN).mp4 | 12 MB | ||
| 1. About Convolutional Neural Network (CNN).srt | 2.5 KB | ||
| 1. About Data Augmentation.mp4 | 17.1 MB | ||
| 1. About Data Augmentation.srt | 3.3 KB | ||
| 1. About Data Generators.mp4 | 14.5 MB | ||
| 1. About Data Generators.srt | 3.2 KB | ||
| 1. About Epoch and Batch Size.mp4 | 5.4 MB | ||
| 1. About Epoch and Batch Size.srt | 1.4 KB | ||
| 1. About Model Checkpoint.mp4 | 5.9 MB | ||
| 1. About Model Checkpoint.srt | 1.5 KB | ||
| 1. Creating a common method to get the number of files from a directory.mp4 | 8.5 MB | ||
| 1. Creating a common method to get the number of files from a directory.srt | 1.5 KB | ||
| 1. Full Project Code.html | 102.4 B | ||
| 1. Loading the final model from drive.mp4 | 19.8 MB | ||
| 1. Loading the final model from drive.srt | 3.6 KB | ||
| 1. Predicting on the test data using both MobileNetV2 and Custom CNN Model.mp4 | 22.3 MB | ||
| 1. Predicting on the test data using both MobileNetV2 and Custom CNN Model.srt | 4.5 KB | ||
| 1. Project Overview.mp4 | 5.7 MB | ||
| 1. Project Overview.srt | 1.6 KB | ||
| 1. Role of Optimizer in Deep Learning.mp4 | 16.5 MB | ||
| 1. Role of Optimizer in Deep Learning.srt | 3.2 KB | ||
| 1. Understanding the dataset and the folder structure.mp4 | 17 MB | ||
| 1. Understanding the dataset and the folder structure.srt | 5.6 KB | ||
| 2. About Adam Optimizer.mp4 | 5 MB | ||
| 2. About Adam Optimizer.srt | 1.5 KB | ||
| 2. About Classification Report.mp4 | 7 MB | ||
| 2. About Classification Report.srt | 1.6 KB | ||
| 2. About OpenCV.mp4 | 17.9 MB | ||
| 2. About OpenCV.srt | 2.8 KB | ||
| 2. Defining a method to plot training and validation accuracy and loss.mp4 | 25.9 MB | ||
| 2. Defining a method to plot training and validation accuracy and loss.srt | 4.7 KB | ||
| 2. Implementing Data Augmentation techniques.mp4 | 24.6 MB | ||
| 2. Implementing Data Augmentation techniques.srt | 4.3 KB | ||
| 2. Implementing Data Generators.mp4 | 23.4 MB | ||
| 2. Implementing Data Generators.srt | 4.3 KB | ||
| 2. Implementing Model Checkpoint.mp4 | 21.9 MB | ||
| 2. Implementing Model Checkpoint.srt | 4 KB | ||
| 2. Introduction to Google Colab.mp4 | 15.2 MB | ||
| 2. Introduction to Google Colab.srt | 3.5 KB | ||
| 2. Loading an image and predicting using the model whether the person has Pneumonia.mp4 | 40.1 MB | ||
| 2. Loading an image and predicting using the model whether the person has Pneumonia.srt | 6.3 KB | ||
| 2. MobileNetV2 and Custom CNN Model Fitting.mp4 | 42.6 MB | ||
| 2. MobileNetV2 and Custom CNN Model Fitting.srt | 6 KB | ||
| 2. Setting up the project in Google Colab_Part1.mp4 | 6.1 MB | ||
| 2. Setting up the project in Google Colab_Part1.srt | 1.5 KB | ||
| 3. About binary cross entropy loss function..mp4 | 11.2 MB | ||
| 3. About binary cross entropy loss function..srt | 2.4 KB | ||
| 3. Calculating the class weights in train directory.mp4 | 35.2 MB | ||
| 3. Calculating the class weights in train directory.srt | 6.3 KB | ||
| 3. Classification Report in action for both MobileNetV2 and Custom CNN Model.mp4 | 14.5 MB | ||
| 3. Classification Report in action for both MobileNetV2 and Custom CNN Model.srt | 2.7 KB | ||
| 3. Setting up the project in Google Colab_Part2.mp4 | 80.6 MB | ||
| 3. Setting up the project in Google Colab_Part2.srt | 16 KB | ||
| 3. Understanding pre-trained models.mp4 | 10 MB | ||
| 3. Understanding pre-trained models.srt | 2.1 KB | ||
| 3. Understanding the project folder structure.mp4 | 15.4 MB | ||
| 3. Understanding the project folder structure.srt | 4.8 KB | ||
| 4. About Config and Create_Dataset File.mp4 | 72.4 MB | ||
| 4. About Config and Create_Dataset File.srt | 14.3 KB | ||
| 4. About MobileNetV2 model.mp4 | 7.6 MB | ||
| 4. About MobileNetV2 model.srt | 1.7 KB | ||
| 4. Computing the confusion matrix and using the same to derive the accuracy, sensit.mp4 | 38.3 MB | ||
| 4. Computing the confusion matrix and using the same to derive the accuracy, sensit.srt | 7.6 KB | ||
| 4. Putting all together for MobileNetV2.mp4 | 10.9 MB | ||
| 4. Putting all together for MobileNetV2.srt | 2.1 KB | ||
| 5. Importing the Libraries.mp4 | 37.2 MB | ||
| 5. Importing the Libraries.srt | 6 KB | ||
| 5. Loading the MobileNetV2 classifier.mp4 | 16.4 MB | ||
| 5. Loading the MobileNetV2 classifier.srt | 1.7 KB | ||
| 5. Plot training and validation accuracy and loss.mp4 | 14.9 MB | ||
| 5. Plot training and validation accuracy and loss.srt | 2.9 KB | ||
| 5. Putting all together for Custom CNN Model.mp4 | 12.3 MB | ||
| 5. Putting all together for Custom CNN Model.srt | 2.3 KB | ||
| 6. Building a new fully-connected (FC) head.mp4 | 20.3 MB | ||
| 6. Building a new fully-connected (FC) head.srt | 2.9 KB | ||
| 6. Plotting the count of data against each class in each directory.mp4 | 51 MB | ||
| 6. Plotting the count of data against each class in each directory.srt | 10.3 KB | ||
| 6. SerializeWriting the model to disk.mp4 | 7.1 MB | ||
| 6. SerializeWriting the model to disk.srt | 1.5 KB | ||
| 7. Building the final MobileNetV2 model.mp4 | 8.9 MB | ||
| 7. Building the final MobileNetV2 model.srt | 1.7 KB | ||
| 7. Plotting some samples from both the classes.mp4 | 46.8 MB | ||
| 7. Plotting some samples from both the classes.srt | 7.8 KB | ||
| 8. Understanding Conv2D, Filters, Relu activation, Batch Normalization, MaxPooling2.mp4 | 26.8 MB | ||
| 8. Understanding Conv2D, Filters, Relu activation, Batch Normalization, MaxPooling2.srt | 3.7 KB | ||
| 9. Building a custom CNN network architecture.mp4 | 76.1 MB | ||
| 9. Building a custom CNN network architecture.srt | 13.3 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| CM_16_weights-018-0.1818.hdf5 | 89.4 MB | ||
| Detect_Pneumonia.ipynb | 697.6 KB | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| Kaggle Link_chest-xray-pneumonia.txt | 102.4 B | ||
| MN_16_TrainingHistoryPlot.png | 24.7 KB | ||
| MN_16_weights-016-0.2087.hdf5 | 11 MB | ||
| Normal.jpeg | 246.8 KB | ||
| Pneumonia.jpeg | 75.6 KB | ||
| config.py | 1.1 KB | ||
| conv_bc_model.py | 2.7 KB | ||
| create_dataset.py | 1.8 KB | ||
| getPaths.py | 1 KB | ||
| train_CustomModel_16_conv_modelCheckpoint_reshuffle_data.ipynb | 857.8 KB | ||
| train_MobileNet_16_modelCheckpoint_reshuffle_data (1).ipynb | 891.7 KB | ||
| ▲ 102 total files | |||
Data Science: CNN & OpenCV : Chest XRAY-Pneumonia Detection
https://DevCourseWeb.com
Last Updated 02/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 44 lectures (2h 13m) | Size: 1.08 GB
A practical hands on Deep Learning Project on building a Pneumonia 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 MobileNetV2
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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