improve_training_results.md

Improving Deep Learning Training Results

Deep learning training outcomes can significantly improve by focusing on three main components: data, model, and training. This framework can be expanded to "data, model, loss, optimizer," as detailed in Andrej Karpathy's insightful blog post. He suggests dividing the model into model and loss, with training encapsulated by the optimizer. Here are strategies to enhance each component:

Data

• Data Augmentation: Apply data augmentation techniques to combat overfitting.
• Expand Dataset: Increasing the dataset size can lead to more robust training outcomes.

Model

• Dropout: Integrate dropout layers to prevent overfitting.
• Activation Functions: Utilize ReLU/GLUE for non-linear transformations.
• Loss Functions: Proper selection of loss functions is crucial.

Training (Optimizer/Scheduler)

• Epochs: Increasing the number of iterations or epochs, especially when using dropout, is often necessary for optimal performance.
• Learning Rate: Adjusting the learning rate is a primary method for enhancing performance.
• Optimizer Choice: Switching optimizers, e.g., from Adam to AdamW, can improve results due to AdamW's weight decay feature, which acts as a regularizer.
• Scheduling: Implementing schedulers, with or without options for warm-up phases, can fine-tune the learning rate adjustment process over time. Example schedulers include StepLR, CosineAnnealingLR, and ExponentialLR, with warm-up variations like CosLR and LinearLR.

Note: Weight decay, Dropout and Label Smoothing are the basic regularizers.

Note: The strategies outlined above are often detailed in academic papers.