Optimization algorithm minimizing loss by iteratively adjusting model parameters. This algorithm plays a crucial role in modern machine learning and neural network training.
Core Principles
Gradient Descent operates by systematically processing input data and applying mathematical transformations to achieve desired outputs. The algorithm's efficiency and effectiveness depend on proper parameter tuning and implementation details.
Implementation in OCR
OCR systems leverage gradient descent to handle various challenges including image preprocessing, feature extraction, sequence modeling, and decision making. DeepSeek-OCR integrates this algorithm as part of its comprehensive processing pipeline.
Advantages and Limitations
Key advantages include computational efficiency, scalability to large datasets, and proven effectiveness across diverse applications. Limitations may include sensitivity to parameter settings, computational requirements, and performance on edge cases.
Best Practices
Successful implementation requires careful consideration of hyperparameters, validation strategies, and integration with other system components. Practitioners should conduct thorough testing and validation before production deployment.