Weight Initialization Techniques for Efficient Neural Network Learning
Neural networks identify patterns from data through learning, but initially, the weights have no meaningful information.
If the weights are not set appropriately at the start, the learning process can be hindered.
Weight Initialization involves determining the initial values to assign to the weights in a neural network.
Without suitable initialization, learning might slow down, or the model may fail to find optimal solutions.
Why is weight initialization important?
Weights in neural networks should be optimized through learning, but if the initial weights are too large or too small, proper learning may not occur.
If the weights are too large, the gradient during backpropagation can become excessively large, causing instability in learning.
Conversely, if the weights are too small, the gradient might vanish, leading to minimal weight updates.
For example, if the weights are initialized to zero, every neuron will learn the same values, and the model will not function correctly.
What are some weight initialization techniques?
The main weight initialization techniques are as follows:
1. Xavier Initialization
Xavier Initialization sets the initial weights based on the number of input and output neurons to prevent the weights from being too large or too small.
This method is typically used with sigmoid and hyperbolic tangent activation functions.
2. He Initialization
He Initialization is well-suited for ReLU activation functions, setting larger weight values to prevent vanishing gradients.
It is commonly used with ReLU activation functions.
3. Normal and Uniform Distribution Initialization
Normal and Uniform Distribution Initialization methods involve random weight initialization from specific distributions.
Normal distribution initialization adjusts weights with a mean of 0 and a chosen standard deviation, while uniform distribution initialization assigns random values within a certain range.
Correct application of weight initialization can prevent vanishing or exploding gradients and enhance the convergence speed of the model with appropriate initial values.
In the next lesson, we will explore L1 & L2 Regularization Techniques.
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