AI Practitioner

Feature normalization methods are  critical  when training deep learning models because they help improve model  performance, convergence speed , and  training stability . Why Feature Normalization is Used in Deep Learning 1. Accelerates Convergence Neural networks are typically trained using gradient-based optimizers like SGD, Adam, etc. If input features have different scales (e.g., one feature ranges from 0 to 1, while another ranges from 0 to 1000), the loss surface becomes  distorted or ill-conditioned . This causes gradients to  oscillate , slowing down learning or even making it unstable. Normalized inputs ensure the model sees data on a similar scale, resulting in  smoother loss surfaces  and  faster convergence . 2. Improves Numerical Stability Deep models can suffer from exploding or vanishing gradients if activations or weights grow too large or small. Normalization (especially internal ones like  Batch Normalization ) helps m...