Weight initialization is a crucial step in the development of neural networks, as it sets the initial values for the weights and biases before training begins. Proper initialization can significantly impact the optimization process, particularly when using algorithms like stochastic gradient descent (SGD). If weights are poorly initialized, it can lead to issues such as slow convergence or even failure to converge altogether ¹⁵.

There are several techniques for weight initialization, including random initialization, Xavier initialization, and He initialization. Random initialization involves assigning small random values to weights, while Xavier initialization is tailored for activation functions like Sigmoid or Tanh, ensuring that the weights are set to maintain variance across layers. He initialization is specifically designed for ReLU activation functions, helping to mitigate problems like vanishing and exploding gradients ²⁵.

In summary, effective weight initialization is essential for training deep learning models, as it can enhance stability and performance, leading to more accurate predictions ³⁴.