Optimization in machine learning refers to the process of adjusting a model’s parameters to minimize (or maximize) a specific objective function, typically the loss function. It is a core component of training algorithms and determines how well a model learns patterns from data and generalizes to unseen inputs.
Key Characteristics:
- Objective-Driven: Optimization seeks to minimize errors (loss) between predicted and actual outputs.
- Iterative Process: Most optimization methods update parameters gradually over multiple iterations (epochs).
- Gradient-Based: Common algorithms use gradients (e.g., via gradient descent) to guide updates in the 4o
Why It Matters:
Quantum computing has the potential to outperform classical computers in specific problem domains, reshaping industries such as cybersecurity, healthcare, logistics, and AI. Though still in the experimental phase, progress in hardware, error correction, and hybrid quantum-classical algorithms signals a transformative future.
