diff --git a/Book4_Ch14_Python_Codes/Bk4_Ch14_01.py b/Book4_Ch14_Python_Codes/Bk4_Ch14_01.py
deleted file mode 100644
index 631644c..0000000
--- a/Book4_Ch14_Python_Codes/Bk4_Ch14_01.py
+++ /dev/null
@@ -1,22 +0,0 @@
-
-###############
-# Authored by Weisheng Jiang
-# Book 4  |  From Basic Arithmetic to Machine Learning
-# Published and copyrighted by Tsinghua University Press
-# Beijing, China, 2022
-###############
-
-# Bk4_Ch14_01.py
-
-import numpy as np
-
-A = np.matrix([[1.25, -0.75],
-               [-0.75, 1.25]])
-
-LAMBDA, V = np.linalg.eig(A)
-
-B = V@np.diag(np.sqrt(LAMBDA))@np.linalg.inv(V)
-
-A_reproduced = B@B
-
-print(A_reproduced)
diff --git a/Book4_Ch14_Python_Codes/Bk4_Ch14_02.py b/Book4_Ch14_Python_Codes/Bk4_Ch14_02.py
deleted file mode 100644
index 211a052..0000000
--- a/Book4_Ch14_Python_Codes/Bk4_Ch14_02.py
+++ /dev/null
@@ -1,53 +0,0 @@
-
-###############
-# Authored by Weisheng Jiang
-# Book 4  |  From Basic Arithmetic to Machine Learning
-# Published and copyrighted by Tsinghua University Press
-# Beijing, China, 2022
-###############
-
-# Bk4_Ch14_02.py
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-# transition matrix
-T = np.matrix([[0.7, 0.2],
-               [0.3, 0.8]])
-
-# steady state
-sstate = np.linalg.eig(T)[1][:,1]
-sstate = sstate/sstate.sum()
-print(sstate)
-
-# initial states
-initial_x_array = np.array([[1, 0, 0.5, 0.4],  # Chicken
-                            [0, 1, 0.5, 0.6]]) # Rabbit
-
-num_iterations = 10;
-
-for i in np.arange(0,4):
-    
-    initial_x = initial_x_array[:,i][:, None]
-    
-    x_i = np.zeros_like(initial_x)
-    x_i = initial_x
-    X =   initial_x.T;
-    
-    # matrix power through iterations
-    
-    for x in np.arange(0,num_iterations):
-        x_i = T@x_i;
-        X = np.concatenate([X, x_i.T],axis = 0)
-    
-    fig, ax = plt.subplots()
-    
-    itr = np.arange(0,num_iterations+1);
-    plt.plot(itr,X[:,0],marker = 'x',color = (1,0,0))
-    plt.plot(itr,X[:,1],marker = 'x',color = (0,0.6,1))
-    
-    ax.grid(linestyle='--', linewidth=0.25, color=[0.5,0.5,0.5])
-    ax.set_xlim(0, num_iterations)
-    ax.set_ylim(0, 1)
-    ax.set_xlabel('Iteration, k')
-    ax.set_ylabel('State')
diff --git a/Book4_Ch14_Python_Codes/Bk4_Ch14_03.py b/Book4_Ch14_Python_Codes/Bk4_Ch14_03.py
deleted file mode 100644
index 10c577c..0000000
--- a/Book4_Ch14_Python_Codes/Bk4_Ch14_03.py
+++ /dev/null
@@ -1,106 +0,0 @@
-
-###############
-# Authored by Weisheng Jiang
-# Book 4  |  From Basic Arithmetic to Machine Learning
-# Published and copyrighted by Tsinghua University Press
-# Beijing, China, 2022
-###############
-
-# Bk4_Ch14_03.py
-
-import sympy
-import numpy as np
-import matplotlib.pyplot as plt
-from numpy import linalg as L
-
-
-def mesh_circ(c1, c2, r, num):
-    
-    theta = np.linspace(0, 2*np.pi, num)
-    r     = np.linspace(0,r, num)
-    theta,r = np.meshgrid(theta,r)
-    xx1 = np.cos(theta)*r + c1
-    xx2 = np.sin(theta)*r + c2
-    
-    return xx1, xx2
-
-
-#define symbolic vars, function
-x1,x2 = sympy.symbols('x1 x2')
-
-A = np.array([[0.5, -0.5],
-              [-0.5, 0.5]])
-
-Lambda, V = L.eig(A)
-
-x = np.array([[x1,x2]]).T
-
-f_x = x.T@A@x
-f_x = f_x[0][0]
-
-f_x_fcn = sympy.lambdify([x1,x2],f_x)
-
-xx1, xx2 = mesh_circ(0, 0, 1, 50)
-
-ff_x = f_x_fcn(xx1,xx2)
-
-if Lambda[1] > 0:
-    levels = np.linspace(0,Lambda[0],21)
-else:
-    levels = np.linspace(Lambda[1],Lambda[0],21)
-
-t = np.linspace(0,np.pi*2,100)
-
-# 2D visualization
-fig, ax = plt.subplots()
-
-ax.plot(np.cos(t), np.sin(t), color = 'k')
-
-cs = plt.contourf(xx1, xx2, ff_x,
-                  levels=levels, cmap = 'RdYlBu_r')
-plt.show()
-ax.set_aspect('equal')
-ax.xaxis.set_ticks([])
-ax.yaxis.set_ticks([])
-ax.set_xlabel('$x_1$')
-ax.set_ylabel('$x_2$')
-ax.set_xlim(-1,1)
-ax.set_ylim(-1,1)
-clb = fig.colorbar(cs, ax=ax)
-clb.set_ticks(levels)
-
-#%% 3D surface of f(x1,x2)
-
-x1_ = np.linspace(-1.2,1.2,31)
-x2_ = np.linspace(-1.2,1.2,31)
-
-xx1_fine, xx2_fine = np.meshgrid(x1_,x2_)
-
-ff_x_fine = f_x_fcn(xx1_fine,xx2_fine)
-
-f_circle = f_x_fcn(np.cos(t), np.sin(t))
-
-# 3D visualization
-
-fig, ax = plt.subplots()
-ax = plt.axes(projection='3d')
-
-ax.plot(np.cos(t), np.sin(t), f_circle, color = 'k')
-# circle projected to f(x1,x2)
-
-ax.plot_wireframe(xx1_fine,xx2_fine,ff_x_fine,
-                  color = [0.8,0.8,0.8],
-                  linewidth = 0.25)
-
-ax.contour3D(xx1_fine,xx2_fine,ff_x_fine,15,
-             cmap = 'RdYlBu_r')
-
-ax.view_init(elev=30, azim=60)
-ax.xaxis.set_ticks([])
-ax.yaxis.set_ticks([])
-ax.zaxis.set_ticks([])
-ax.set_xlim(xx1_fine.min(),xx1_fine.max())
-ax.set_ylim(xx2_fine.min(),xx2_fine.max())
-plt.tight_layout()
-ax.set_proj_type('ortho')
-plt.show()
diff --git a/Book4_Ch14_Python_Codes/Streamlit_Bk4_Ch14_02.py b/Book4_Ch14_Python_Codes/Streamlit_Bk4_Ch14_02.py
deleted file mode 100644
index b84c888..0000000
--- a/Book4_Ch14_Python_Codes/Streamlit_Bk4_Ch14_02.py
+++ /dev/null
@@ -1,54 +0,0 @@
-
-###############
-# Authored by Weisheng Jiang
-# Book 4  |  From Basic Arithmetic to Machine Learning
-# Published and copyrighted by Tsinghua University Press
-# Beijing, China, 2022
-###############
-
-
-import numpy as np
-import streamlit as st
-import time
-
-# transition matrix
-A = np.matrix([[0.7, 0.2],
-               [0.3, 0.8]])
-
-
-with st.sidebar:
-    pi_0_chicken = st.slider('Ratio of chicken:',
-              0.0, 1.0, step = 0.1)
-    pi_0_rabbit  = 1 - pi_0_chicken
-    st.write('Ratio of rabbit: ' + str(round(pi_0_rabbit,1)))
-    
-    num_iterations = st.slider('Number of nights:',
-                               20,100,step = 5)
-
-
-progress_bar = st.sidebar.progress(0)
-status_text = st.sidebar.empty()
-last_rows = np.array([[pi_0_chicken, pi_0_rabbit]])
-# st.write(last_rows) # row vector
-
-chart = st.line_chart(last_rows)
-
-for i in range(1, num_iterations):
-    
-    last_status = last_rows[-1,:]
-    
-    # st.write(last_status)
-    
-    new_rows = last_status@A.T
-    
-    percent = (i + 1)*100/num_iterations
-    
-    status_text.text("%i%% Complete" % percent)
-    
-    chart.add_rows(new_rows)
-    progress_bar.progress(i)
-    last_rows = new_rows
-    time.sleep(0.1)
-
-progress_bar.empty()
-
diff --git a/Book4_Ch14_Python_Codes/Streamlit_Bk4_Ch14_04.py b/Book4_Ch14_Python_Codes/Streamlit_Bk4_Ch14_04.py
deleted file mode 100644
index 9ca0d78..0000000
--- a/Book4_Ch14_Python_Codes/Streamlit_Bk4_Ch14_04.py
+++ /dev/null
@@ -1,119 +0,0 @@
-
-###############
-# Authored by Weisheng Jiang
-# Book 4  |  From Basic Arithmetic to Machine Learning
-# Published and copyrighted by Tsinghua University Press
-# Beijing, China, 2022
-###############
-
-import plotly.graph_objects as go
-import streamlit as st
-import numpy as np
-import plotly.express as px
-import pandas as pd
-import sympy
-
-def bmatrix(a):
-    """Returns a LaTeX bmatrix
-
-    :a: numpy array
-    :returns: LaTeX bmatrix as a string
-    """
-    if len(a.shape) > 2:
-        raise ValueError('bmatrix can at most display two dimensions')
-    lines = str(a).replace('[', '').replace(']', '').splitlines()
-    rv = [r'\begin{bmatrix}']
-    rv += ['  ' + ' & '.join(l.split()) + r'\\' for l in lines]
-    rv +=  [r'\end{bmatrix}']
-    return '\n'.join(rv)
-
-with st.sidebar:
-    
-    st.latex(r'''
-             A = \begin{bmatrix}
-    a & b\\
-    b & c
-    \end{bmatrix}''')
-    
-    a = st.slider('a',-2.0, 2.0, step = 0.05, value = 1.0)
-    b = st.slider('b',-2.0, 2.0, step = 0.05, value = 0.0)  
-    c = st.slider('c',-2.0, 2.0, step = 0.05, value = 1.0)
-    
-#%%
-
-theta_array = np.linspace(0, 2*np.pi, 36)
-
-X = np.column_stack((np.cos(theta_array), 
-                     np.sin(theta_array)))
-
-# st.write(X)
-A = np.array([[a, b],
-              [b, c]])
-
-st.latex(r'''z^Tz = 1''')
-st.latex(r'''x = Az''')
-
-st.latex('A =' + bmatrix(A))
-
-X_ = X@A
-
-#define symbolic vars, function
-x1,x2 = sympy.symbols('x1 x2')
-y1,y2 = sympy.symbols('y1 y2')
-x = np.array([[x1,x2]]).T
-y = np.array([[y1,y2]]).T
-
-Q = np.linalg.inv(A@A.T)
-D,V = np.linalg.eig(Q)
-D = np.diag(D)
-
-st.latex(r'Q = \left( AA^T\right)^{-1} = ' + bmatrix(np.round(Q, 3)))
-
-st.latex(r'''Q = V \Lambda V^{T}''')
-st.latex(bmatrix(np.around(Q, decimals=3)) + '=' + 
-         bmatrix(np.around(V, decimals=3)) + '@' + 
-         bmatrix(np.around(D, decimals=3)) + '@' + 
-         bmatrix(np.around(V.T, decimals=3)))
-
-f_x = x.T@np.round(Q, 3)@x
-f_y = y.T@np.round(D, 3)@y
-
-from sympy import *
-st.write('The formula of the ellipse:')
-st.latex(latex(simplify(f_x[0][0])) + ' = 1')
-
-st.write('The formula of the transformed ellipse:')
-st.latex(latex(simplify(f_y[0][0])) + ' = 1')
-
-#%% 
-color_array = np.linspace(0,1,len(X))
-# st.write(color_array)
-X_c = np.column_stack((X_, color_array))
-df = pd.DataFrame(X_c, columns=['x1','x2', 'color'])
-
-#%% Scatter
-
-fig = px.scatter(df, 
-                 x="x1", 
-                 y="x2", 
-                 color='color',
-                 color_continuous_scale=px.colors.sequential.Rainbow)
-
-
-
-fig.update_layout(
-    autosize=False,
-    width=500,
-    height=500)
-
-
-fig.add_hline(y=0, line_color = 'black')
-fig.add_vline(x=0, line_color = 'black')
-fig.update_layout(coloraxis_showscale=False)
-fig.update_xaxes(range=[-3, 3])
-fig.update_yaxes(range=[-3, 3])
-
-st.plotly_chart(fig)
-
-
-