Update README

easy/Log_Softmax/README.md

@@ -34,7 +34,9 @@ $$\text{softmax}(x_i) = \frac{e^{x_i}}{\sum_{j=1}^n e^{x_j}}$$
 
 However, directly applying the logarithm to the softmax function can lead to numerical instability, especially when dealing with large numbers. To prevent this, we use the log-softmax function, which incorporates a shift by subtracting the maximum value from the input vector:
 
-`\text{log_softmax}(x_i) = x_i - \max(x) - \log\left(\sum_{j=1}^n e^{x_j - \max(x)}\right)`
+$$
+\text{log\_softmax}(x_i) = x_i - \max(x) - \log\left(\sum_{j=1}^n e^{x_j - \max(x)}\right)
+$$
 
 This formulation helps to avoid overflow issues that can occur when exponentiating large numbers. The log-softmax function is particularly useful in machine learning for calculating probabilities in a stable manner, especially when used with cross-entropy loss functions.