Layer Depth on Backpropagation

Requirements Clarification & Assessment

1. Understanding the Objective:

   • The goal is to transition from a basic neural network to a deep neural network and assess how backpropagation is affected by increased layers and nodes.
   • Key aspects to explore include computational complexity, gradient behavior, training time, and overfitting.

2. Identify Key Concepts:

   • Backpropagation: The process of updating weights in a network by propagating errors backward from output to input layers.
   • Deep Neural Network (DNN): A neural network with multiple hidden layers.
   • Layers and Nodes: Increasing these adds complexity to the network.

3. Potential Challenges:

   • Computational Complexity: More layers/nodes mean more computations during training.
   • Gradient Issues: Vanishing/exploding gradients can hinder effective training.
   • Overfitting: A deeper network might memorize training data rather than generalizing.

4. Technical Constraints:

   • Hardware limitations (e.g., GPU availability) can affect training time and feasibility.
   • Choice of activation functions and optimization algorithms can influence performance.