Optimizers

SGD (Stochastic Gradient Descent)

• Description: Basic optimizer that updates weights using gradients.
• Function: float sgd(float x, float y, float lr, float *w, float *b)
• File: sgd.c

Adam

• Description: Adaptive optimizer combining momentum and RMSprop.
• Function: float adam(float x, float y, float lr, float *w, float *b, float *v_w, float *v_b, float *s_w, float *s_b, float beta1, float beta2, float epsilon)
• File: Adam.c

RMSprop

• Description: Optimizer that scales learning rates based on recent gradients.
• Function: float rms_prop(float x, float y, float lr, float *w, float *b, float *cache_w, float *cache_b, float epsilon, float beta)
• File: RMSprop.c