Initial Neuron Weights

Requirements Clarification & Assessment

1. Understanding the Problem:

   • The question aims to assess the understanding of the initialization process in neural networks and its significance in training.
   • It specifically targets the impact of initializing weights to zero and how it affects the learning process.

2. Key Concepts Involved:

   • Neural Networks: Understanding the architecture and how layers and neurons interact.
   • Weight Initialization: The process of setting initial values for the weights in a neural network.
   • Symmetry Breaking: The concept of ensuring neurons learn different features by not starting with identical weights.
   • Gradient Descent and Backpropagation: The methods by which neural networks learn and update weights.

3. Interviewer's Expectation:

   • The candidate should demonstrate knowledge of why initializing weights to zero is detrimental.
   • The candidate should suggest alternative strategies for weight initialization.
   • The answer should reflect an understanding of how proper initialization aids in effective learning and convergence.

4. Potential Pitfalls:

   • Overlooking the nuances of how different activation functions interact with zero-initialized weights.
   • Failing to mention the role of random initialization in breaking symmetry and aiding convergence.