Training From Scratch
Training from scratch is a machine learning methodology where a model is built and trained entirely on a new dataset without leveraging any pre-trained weights or transfer learning. It involves initializing model parameters randomly and optimizing them through iterative training on the target data. This approach is often used when the task or data domain is significantly different from existing pre-trained models, requiring a fresh start to capture unique patterns.
Developers should use training from scratch when working with highly specialized or novel datasets where pre-trained models are unavailable or ineffective, such as in niche scientific research or custom industrial applications. It is also appropriate when computational resources are sufficient and the goal is to avoid biases or limitations from pre-trained models, ensuring the model is tailored specifically to the task at hand. This method provides full control over the training process but requires more data and time compared to transfer learning.