Computer Vision and Speech Processing From Theory to Practice
Computer Vision and Speech Processing From Theory to Practice Computer vision and speech processing share a long history of theory and practice. In this article, we connect core ideas from math and learning to real projects you can build and maintain. You will find a simple workflow, practical tips, and concrete examples that work with common tools, data, and hardware. A practical workflow Data: collect diverse images and sounds. Clean labels, balanced sets, and clear privacy rules matter more than fancy models. Models: start with proven architectures. Leverage pre-trained weights and simple fine-tuning to adapt to your task. Training: define loss functions that match your goal, monitor with validation metrics, and use regularization to avoid overfitting. Evaluation: report accuracy, precision/recall, and task-specific metrics such as mean average precision or word error rate. Test on real-world scenarios, not only on a clean test set. Deployment: consider latency and memory. Use quantization or smaller backbones for edge devices, and set up monitoring to catch drift after release. A concrete example ...