Summary
This chapter scratched the surface of the vast and fascinating world of ANNs. We learned about the structure of ANNs, and how to design a network topology based on application requirements. Then, we focused on OpenCV's implementation of MLP ANNs, as well as on OpenCV's support for diverse DNNs that have been trained in other frameworks.
We applied neural networks to real-world problems: notably, handwritten digit recognition; object detection and classification; and a combination of face detection, age classification, and gender classification in real time. We saw that even in these introductory demos, neural networks show a lot of promise in terms of versatility, accuracy, and speed. Hopefully, this encourages you to try out pre-trained models from various authors, and to learn to train advanced models of your own in various frameworks. Finally, we have learned how to use MediaPipe for hand detection and trained a classifier with TensorFlow, and utilized...
