How Do You Choose the Key Elements of Your Neural Network Architecture?

General
1
  • When building your neural network architecture, how do you decide on key elements such as the type of model (e.g., CNN, RNN, Transformer), image backbones (ResNet, EfficientNet), loss functions, optimizers, and more?
  • What factors or criteria guide your decision-making for each of these components, and how do you balance experimentation with established best practices?
  1. Choosing the Model Type:
  • How do you determine whether to use CNNs, RNNs, Transformers, or other architectures? Does the task type (e.g., image classification, NLP) strongly dictate this choice?
  • Have you ever had to switch architectures mid-project due to performance or scalability issues?
  1. Selecting Image Backbones:
  • For vision tasks, how do you choose the right backbone model (e.g., ResNet, EfficientNet, MobileNet)? What trade-offs do you consider in terms of accuracy, computational cost, and model size?
  1. Loss Functions and Optimizers:
  • What considerations go into selecting the loss function (e.g., cross-entropy, MSE, hinge loss)? Are there specific losses you find work better for certain types of data or tasks?
  • How do you decide between different optimizers (e.g., SGD, Adam, RMSProp)? Do you ever adjust learning rates or experiment with more advanced optimization techniques like AdamW?
  1. Hyperparameter Tuning:
  • What strategies do you use for tuning hyperparameters like the learning rate, batch size, and number of layers? Do you rely on tools like grid search, random search, or Bayesian optimization?
  1. Additional Elements:
  • How do you incorporate regularization techniques like dropout, weight decay, or batch normalization? What signs indicate you need more regularization?
  • What metrics do you track during training to determine the success of your architecture choices?