Download Udemy - Data Science - CNN and OpenCV - Chest XRAY-Pneumonia Detection torrent

Udemy - Data Science - CNN and OpenCV - Chest XRAY-Pneumonia Detection

Torrent:	Udemy - Data Science - CNN and OpenCV - Chest XRAY-Pneumonia Detection
Description:	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
Downloads:	45
Category:	Other /Tutorials
Size:	1.1 GB
Show Files »	[ DevCourseWeb.com ] Udemy - Data Science - CNN and OpenCV - Chest XRAY-Pneumonia Detection Get Bonus Downloads Here.url (0.2 KB) ~Get Your Files Here ! 1. Introduction and Getting Started 1. Project Overview.mp4 (5.7 MB) 1. Project Overview.srt (1.6 KB) 2. Introduction to Google Colab.mp4 (15.2 MB) 2. Introduction to Google Colab.srt (3.5 KB) 3. Understanding the project folder structure.mp4 (15.4 MB) 3. Understanding the project folder structure.srt (4.8 KB) 10. Model Evaluation 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) 2. About Classification Report.mp4 (7.0 MB) 2. About Classification Report.srt (1.6 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) 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) 5. Plot training and validation accuracy and loss.mp4 (14.9 MB) 5. Plot training and validation accuracy and loss.srt (2.9 KB) 6. SerializeWriting the model to disk.mp4 (7.1 MB) 6. SerializeWriting the model to disk.srt (1.5 KB) 11. Using trained model to predict whether a person has Pneumonia 1. Loading the final model from drive.mp4 (19.8 MB) 1. Loading the final model from drive.srt (3.6 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) 12. Project Files and Code 1. Full Project Code.html (0.1 KB) code Detect_Pneumonia.ipynb (697.6 KB) Kaggle Link_chest-xray-pneumonia.txt (0.1 KB) output CM_16_weights-018-0.1818.hdf5 (89.4 MB) MN_16_TrainingHistoryPlot.png (24.7 KB) MN_16_weights-016-0.2087.hdf5 (11.0 MB) sampleTest_Pictures Normal.jpeg (246.8 KB) Pneumonia.jpeg (75.6 KB) train_CustomModel_16_conv_modelCheckpoint_reshuffle_data.ipynb (857.8 KB) train_MobileNet_16_modelCheckpoint_reshuffle_data (1).ipynb (891.7 KB) utils config.py (1.1 KB) conv_bc_model.py (2.7 KB) create_dataset.py (1.8 KB) getPaths.py (1.0 KB) 2. Data Understanding & Importing Libraries 1. Understanding the dataset and the folder structure.mp4 (17.0 MB) 1. Understanding the dataset and the folder structure.srt (5.6 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. Setting up the project in Google Colab_Part2.mp4 (80.6 MB) 3. Setting up the project in Google Colab_Part2.srt (16.0 KB) 4. About Config and Create_Dataset File.mp4 (72.4 MB) 4. About Config and Create_Dataset File.srt (14.3 KB) 5. Importing the Libraries.mp4 (37.2 MB) 5. Importing the Libraries.srt (6.0 KB) 6. Plotting the count of data against each class in each directory.mp4 (51.0 MB) 6. Plotting the count of data against each class in each directory.srt (10.3 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) 3. Common Methods for plotting and class weight calculation 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) 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) 3. Calculating the class weights in train directory.mp4 (35.2 MB) 3. Calculating the class weights in train directory.srt (6.3 KB) 4. Data Augmentation 1. About Data Augmentation.mp4 (17.1 MB) 1. About Data Augmentation.srt (3.3 KB) 2. Implementing Data Augmentation techniques.mp4 (24.6 MB) 2. Implementing Data Augmentation techniques.srt (4.3 KB) 5. Data Generators 1. About Data Generators.mp4 (14.5 MB) 1. About Data Generators.srt (3.2 KB) 2. Implementing Data Generators.mp4 (23.4 MB) 2. Implementing Data Generators.srt (4.3 KB) 6. Model Building 1. About Convolutional Neural Network (CNN).mp4 (12.0 MB) 1. About Convolutional Neural Network (CNN).srt (2.5 KB) 2. About OpenCV.mp4 (17.9 MB) 2. About OpenCV.srt (2.8 KB) 3. Understanding pre-trained models.mp4 (10.0 MB) 3. Understanding pre-trained models.srt (2.1 KB) 4. About MobileNetV2 model.mp4 (7.6 MB) 4. About MobileNetV2 model.srt (1.7 KB) 5. Loading the MobileNetV2 classifier.mp4 (16.4 MB) 5. Loading the MobileNetV2 classifier.srt (1.7 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) 7. Building the final MobileNetV2 model.mp4 (8.9 MB) 7. Building the final MobileNetV2 model.srt (1.7 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) 7. Compiling the Model 1. Role of Optimizer in Deep Learning.mp4 (16.5 MB) 1. Role of Optimizer in Deep Learning.srt (3.2 KB) 2. About Adam Optimizer.mp4 (5.0 MB) 2. About Adam Optimizer files
Added:	2022-05-06 10:03:08
Language:	English
Peers:	Seeders : 7 , Leechers : 4
Tags:	Development Data Science Deep Learning Udemy
Release name:	Udemy - Data Science - CNN and OpenCV - Chest XRAY-Pneumonia Detection
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