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Accelerated Computer Vision 1.5 - Data Processing - Imbalanced Data
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Classroom Contents
Computer Vision
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- 1 Accelerated Computer Vision 1.1 - Intro
- 2 Accelerated Computer Vision 1.2 - Introduction to Machine Learning
- 3 Accelerated Computer Vision 1.3 - ML Applications
- 4 Accelerated Computer Vision 1.4 - Supervised and Unsupervised Learning
- 5 Accelerated Computer Vision 1.5 - Data Processing - Imbalanced Data
- 6 Accelerated Computer Vision 1.6 - Underfitting, Overfitting and Model Evaluation
- 7 Accelerated Computer Vision 1.7 - Computer Vision Applications
- 8 Accelerated Computer Vision 1.8 - Image Representation
- 9 Accelerated Computer Vision 1.9 - Neuron & Activation Functions
- 10 Accelerated Computer Vision 1.10 - Neural Networks: Components and Training
- 11 Accelerated Computer Vision 1.11 - Convolutions (Filters)
- 12 Accelerated Computer Vision 1.12 - Padding, Stride and Pooling
- 13 Using Jupyter Notebooks on Sagemaker
- 14 Accelerated Computer Vision 2.1 - Computer Vision Datasets
- 15 Accelerated Computer Vision 2.2 - LeNet
- 16 Accelerated Computer Vision 2.3 - AlexNet
- 17 Accelerated Computer Vision 2.4 - Transfer Learning
- 18 Accelerated Computer Vision 3.1 - VGG and Batch Normalization
- 19 Accelerated Computer Vision 3.2 - ResNet
- 20 Accelerated Computer Vision 3.3 - Object Detection Applications
- 21 Accelerated Computer Vision 3.4 - Bounding Box and Anchor Box
- 22 Accelerated Computer Vision 3.5 - Sliding Window Method and Non-max Suppression
- 23 Accelerated Computer Vision 3.6 - Region Based Convolutional Neural Networks (R-CNNs)
- 24 Accelerated Computer Vision 3.9 - Fully Convolutional Networks
- 25 Accelerated Computer Vision 3.7 - You Only Look Once (YOLO) model
- 26 Accelerated Computer Vision 3.8 - Semantic Segmentation
- 27 Accelerated Computer Vision 3.10 - U-Net
- 28 MLU Channel Introduction
