Add train a model section

app.py

@@ -35,6 +35,15 @@ ax.scatter(X[:,1], y, c='#e76254' ,edgecolors='firebrick')
 
 st.pyplot(fig)
 
+st.subheader('Train a model')
+
+st.markdown('*\"A Gradient Based Method is a method/algorithm that finds the minima of a function, assuming that one can easily compute the gradient of that function. It assumes that the function is continuous and differentiable almost everywhere (it need not be differentiable everywhere).\"* [(Yann LeCun, Deep learning course)](https://atcold.github.io/pytorch-Deep-Learning/en/week02/02-1/#Parametrised-models)')
+
+st.markdown('Using gradient descent we find the minima of the loss adjusting the weights in each step given the following formula:')
+
+st.latex(r'''\bf{w}\leftarrow \bf{w}-\eta \frac{\partial\ell(\bf{X},\bf{y}, \bf{w})}{\partial \bf{w}}''')
+
+
 # Fitting by the respective cost_function
 if cost_function == 'RMSE-Loss':
      st.write('You selected the RMSE loss function.')