Why ReLU Is Better Than the Other Activation Functions?

Answer: ReLU (Rectified Linear Unit) is often favored over other activation functions due to its simplicity, non-saturating nature, and effectiveness in combating the vanishing gradient problem, leading to faster training and improved performance in deep neural networks.

ReLU (Rectified Linear Unit) is widely preferred over other activation functions for several reasons:

1. Simplicity: ReLU is computationally efficient and straightforward to implement, involving only a simple thresholding operation where negative values are set to zero.
2. Non-Saturating: Unlike activation functions such as sigmoid or tanh, ReLU does not saturate in the positive region, preventing the vanishing gradient problem during backpropagation. This enables more stable and efficient training, especially in deep neural networks with many layers.
3. Sparsity: ReLU introduces sparsity by zeroing out negative activations, which can promote more efficient representations in the network by encouraging some neurons to remain inactive. This can lead to better generalization and improved capacity to learn diverse features.
4. Reduced Risk of Gradient Explosion: Unlike activation functions that can cause gradient explosion, such as tanh or sigmoid, ReLU tends to produce gradients of a moderate size, reducing the risk of unstable training dynamics.
5. Faster Convergence: Due to its non-saturating nature and absence of vanishing gradients, ReLU often leads to faster convergence during training, allowing neural networks to reach desired performance levels more quickly.
6. Empirical Success: ReLU has demonstrated remarkable success in various deep learning applications and is widely used in state-of-the-art architectures across different domains, including image classification, object detection, and natural language processing.

Conclusion:

ReLU stands out as a preferred activation function in deep learning due to its simplicity, non-saturating nature, sparsity-inducing properties, reduced risk of gradient explosion, faster convergence, and empirical success in various applications. Its effectiveness in addressing common challenges in deep neural network training makes it a go-to choice for many practitioners and researchers in the field.