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Data Interview Question

Choosing Between ReLu and Tanh for Neural Networks

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Key Requirements:

  1. Image Classification Task:
    • The task involves categorizing images of chairs into various types such as "Office Chair" and "Dining Chair."
  2. Neural Network Design:
    • The network will likely involve multiple hidden layers, potentially using a convolutional neural network (CNN) architecture given the image classification context.
  3. Activation Function Selection:
    • The choice is between ReLu and Tanh for the hidden layers of the neural network.
  4. Performance Metrics:
    • The model should have high accuracy, fast convergence during training, and robustness to common neural network issues like vanishing gradients.
  5. Consideration of Model Efficiency:
    • The model should be computationally efficient, both in terms of training and inference time.

Clarifying Questions:

  • What is the expected size of the dataset?
  • Are there any specific computational constraints or hardware limitations?
  • Is there a preference for real-time inference or batch processing?
  • Will the network be fine-tuned or trained from scratch?
  • Are there any pre-existing models or frameworks in use that could influence activation function choice?