8 Introduction to deep learning for computer vision
This chapter covers
- Understanding convolutional neural networks (convnets)
- Using data augmentation to mitigate overfitting
- Using a pretrained convnet to do feature extraction
- Fine-tuning a pretrained convnet
Computer vision is the earliest and biggest success story of deep learning. Every day, you’re interacting with deep vision models—via Google Photos, Google image search, YouTube, video filters in camera apps, OCR software, and many more. These models are also at the heart of cutting-edge research in autonomous driving, robotics, AI-assisted medical diagnosis, autonomous retail checkout systems, and even autonomous farming.
Computer vision is the problem domain that led to the initial ...
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