B1. Implement Image Recognition with a Convolutional Neural Network (Pratheerth Padman, 2022)

1. Course Overview: 1. Course Overview 00:00:00 2. Exploring and Preparing a Dataset for Image Recognition: 1. Course and Module Introduction 00:02:04 2. What Are We Trying to Solve 00:04:59 3. Demo - Setting up Your Environment 00:08:27 4. Demo - Organizing the Dataset 00:11:18 5. Demo - Exploring the Dataset 00:16:13 6. Demo - Preprocessing and Preparing the Dataset 00:19:39 7. Summary and Up Next 00:25:23 3. Training a Convolutional Neural Network to Classify Images: 01. Module Introduction 00:26:26 02. What Are Convolutional Neural Networks 00:27:25 03. CNN - Convolutions 00:29:46 04. CNN - Activation 00:34:32 05. CNN - Pooling 00:39:22 06. CNN - Classification 00:41:01 07. Demo - Creating the CNN Architecture 00:42:03 08. Demo - Training the Model 00:47:21 09. Demo - Performance Metrics - How Well Did Your Model Do 00:49:19 10. Summary and Up Next 00:52:38 4. Improving Performance of the Convolutional Neural Network: 01. Module Introduction 00:53:51 02. Better Performance – When and How 00:55:12 03. Procuring Additional Training Data - Image Augmentation 00:57:22 04. Hyperparameter Tuning 01:02:40 05. Overfitting and Underfitting 01:06:10 06. Demo - Image Augmentation and Hyperparameter Tuning 01:09:34 07. What Is Transfer Learning 01:17:30 08. Transfer Learning – When and How 01:21:51 09. Demo - Improving Performance through Transfer Learning 01:26:10 10. Summary 01:29:35