diff --git a/Book4_Ch02_Python_Codes/Bk4_Ch2_01.py b/Book4_Ch02_Python_Codes/Bk4_Ch2_01.py
deleted file mode 100644
index 1949300..0000000
--- a/Book4_Ch02_Python_Codes/Bk4_Ch2_01.py
+++ /dev/null
@@ -1,30 +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_Ch2_01.py
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-def draw_vector(vector,RBG): 
-    array = np.array([[0, 0, vector[0], vector[1]]])
-    X, Y, U, V = zip(*array)
-    plt.quiver(X, Y, U, V,angles='xy', scale_units='xy',scale=1,color = RBG)
-
-fig, ax = plt.subplots()
-
-draw_vector([4,3],np.array([0,112,192])/255)
-draw_vector([-3,4],np.array([255,0,0])/255)
-
-plt.ylabel('$x_2$')
-plt.xlabel('$x_1$')
-plt.axis('scaled')
-ax.set_xlim([-5, 5])
-ax.set_ylim([-5, 5])
-ax.grid(linestyle='--', linewidth=0.25, color=[0.5,0.5,0.5])
-plt.show()
diff --git a/Book4_Ch02_Python_Codes/Bk4_Ch2_02.py b/Book4_Ch02_Python_Codes/Bk4_Ch2_02.py
deleted file mode 100644
index c7aaa78..0000000
--- a/Book4_Ch02_Python_Codes/Bk4_Ch2_02.py
+++ /dev/null
@@ -1,19 +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_Ch2_02.py
-
-import numpy as np
-
-# define two column vectors
-a = np.array([[4], [3]])
-b = np.array([[-3], [4]])
-
-# calculate L2 norm
-a_L2_norm = np.linalg.norm(a)
-b_L2_norm = np.linalg.norm(b)
diff --git a/Book4_Ch02_Python_Codes/Bk4_Ch2_03.py b/Book4_Ch02_Python_Codes/Bk4_Ch2_03.py
deleted file mode 100644
index d16adf1..0000000
--- a/Book4_Ch02_Python_Codes/Bk4_Ch2_03.py
+++ /dev/null
@@ -1,36 +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_Ch1_03.py
-
-import matplotlib.pyplot as plt
-import numpy as np
-
-x1 = np.linspace(-10, 10, num=201);
-x2 = x1;
-
-xx1, xx2 = np.meshgrid(x1,x2)
-p = 2
-zz = ((np.abs((xx1))**p) + (np.abs((xx2))**p))**(1./p)
-
-fig, ax = plt.subplots(figsize=(12, 12))
-
-ax.contour(xx1, xx2, zz, levels = np.arange(11), cmap='RdYlBu_r')
-
-ax.axhline(y=0, color='k', linewidth = 0.25)
-ax.axvline(x=0, color='k', linewidth = 0.25)
-ax.set_xlim(-12, 12)
-ax.set_ylim(-12, 12)
-ax.spines['top'].set_visible(False)
-ax.spines['right'].set_visible(False)
-ax.spines['bottom'].set_visible(False)
-ax.spines['left'].set_visible(False)
-ax.set_xlabel('$x_1$')
-ax.set_ylabel('$x_2$')
-ax.set_aspect('equal', adjustable='box')
-plt.show()
diff --git a/Book4_Ch02_Python_Codes/Bk4_Ch2_04.py b/Book4_Ch02_Python_Codes/Bk4_Ch2_04.py
deleted file mode 100644
index a9e05db..0000000
--- a/Book4_Ch02_Python_Codes/Bk4_Ch2_04.py
+++ /dev/null
@@ -1,26 +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_Ch1_04.py
-
-import numpy as np
-
-# define two column vectors
-a = np.array([[-2], [5]])
-b = np.array([[5], [-1]])
-
-# calculate vector addition
-a_plus_b = a + b
-a_plus_b_2 = np.add(a,b)
-
-# calculate vector subtraction
-a_minus_b = a - b
-a_minus_b_2 = np.subtract(a,b)
-
-b_minus_a = b - a
-b_minus_a_2 = np.subtract(b,a)
diff --git a/Book4_Ch02_Python_Codes/Bk4_Ch2_05.py b/Book4_Ch02_Python_Codes/Bk4_Ch2_05.py
deleted file mode 100644
index 45ced0c..0000000
--- a/Book4_Ch02_Python_Codes/Bk4_Ch2_05.py
+++ /dev/null
@@ -1,17 +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_Ch2_05.py
-
-import numpy as np
-
-# define a column vector
-a = np.array([[2], [2]])
-
-b = 2*a
-c = -1.5*a
diff --git a/Book4_Ch02_Python_Codes/Bk4_Ch2_06.py b/Book4_Ch02_Python_Codes/Bk4_Ch2_06.py
deleted file mode 100644
index 63933d7..0000000
--- a/Book4_Ch02_Python_Codes/Bk4_Ch2_06.py
+++ /dev/null
@@ -1,20 +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_Ch2_06.py
-
-import numpy as np
-
-a = np.array([[4, 3]])
-b = np.array([[5, -2]])
-
-a_dot_b = np.inner(a, b)
-
-a_2 = np.array([[4], [3]])
-b_2 = np.array([[5], [-2]])
-a_dot_b_2 = a_2.T@b_2
diff --git a/Book4_Ch02_Python_Codes/Bk4_Ch2_07.py b/Book4_Ch02_Python_Codes/Bk4_Ch2_07.py
deleted file mode 100644
index 773c9cc..0000000
--- a/Book4_Ch02_Python_Codes/Bk4_Ch2_07.py
+++ /dev/null
@@ -1,19 +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_Ch2_07.py
-
-import numpy as np
-a = np.array([[2,3],
-              [3,4]])
-
-b = np.array([[3,4],
-              [5,6]])
-
-a_@_b = np.dot(a,b)
-# a@b
diff --git a/Book4_Ch02_Python_Codes/Bk4_Ch2_08.py b/Book4_Ch02_Python_Codes/Bk4_Ch2_08.py
deleted file mode 100644
index 2e86111..0000000
--- a/Book4_Ch02_Python_Codes/Bk4_Ch2_08.py
+++ /dev/null
@@ -1,19 +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_Ch2_08.py
-
-import numpy as np
-a = np.array([[1,2],
-              [3,4]])
-
-b = np.array([[3,4],
-              [5,6]])
-
-a_dot_b = np.vdot(a,b)
-# [1,2,3,4]*[3,4,5,6].T
diff --git a/Book4_Ch02_Python_Codes/Bk4_Ch2_09.py b/Book4_Ch02_Python_Codes/Bk4_Ch2_09.py
deleted file mode 100644
index 7d64826..0000000
--- a/Book4_Ch02_Python_Codes/Bk4_Ch2_09.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_Ch2_09.py
-
-import numpy as np
-
-a, b = np.array([[4], [3]]), np.array([[5], [-2]])
-
-# calculate cosine theta
-cos_theta = (a.T @ b) / (np.linalg.norm(a,2) * np.linalg.norm(b,2))
-
-# calculate theta in radian
-cos_radian = np.arccos(cos_theta)
-
-# convert radian to degree
-cos_degree = cos_radian * ((180)/np.pi)
diff --git a/Book4_Ch02_Python_Codes/Bk4_Ch2_10.py b/Book4_Ch02_Python_Codes/Bk4_Ch2_10.py
deleted file mode 100644
index a247aee..0000000
--- a/Book4_Ch02_Python_Codes/Bk4_Ch2_10.py
+++ /dev/null
@@ -1,37 +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_Ch2_10.py
-
-from scipy.spatial import distance
-from sklearn import datasets
-import numpy as np
-
-# import the iris data
-iris = datasets.load_iris()
-
-# Only use the first two features: sepal length, sepal width
-X = iris.data[:, :]
-
-# Extract 4 data points
-x1_data = X[0,:]
-x2_data = X[1,:]
-x51_data = X[50,:]
-x101_data = X[100,:]
-
-# calculate cosine distance
-x1_x2_cos_dist = distance.cosine(x1_data,x2_data)
-x1_norm = np.linalg.norm(x1_data) 
-x2_norm = np.linalg.norm(x2_data)
-x1_dot_x2 = x1_data.T@x2_data
-x1_x2_cos = x1_dot_x2/x1_norm/x2_norm
-
-
-x1_x51_cos_dist = distance.cosine(x1_data,x51_data)
-
-x1_x101_cos_dist = distance.cosine(x1_data,x101_data)
diff --git a/Book4_Ch02_Python_Codes/Bk4_Ch2_11.py b/Book4_Ch02_Python_Codes/Bk4_Ch2_11.py
deleted file mode 100644
index 1404f15..0000000
--- a/Book4_Ch02_Python_Codes/Bk4_Ch2_11.py
+++ /dev/null
@@ -1,24 +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_Ch2_11.py
-
-import numpy as np
-a = np.array([-2, 1, 1])
-b = np.array([1, -2, -1])
-# a = [-2, 1, 1]
-# b = [1, -2, -1]
-
-# calculate cross product of row vectors
-a_cross_b = np.cross(a, b)
-
-a_col = np.array([[-2], [1], [1]])
-b_col = np.array([[1], [-2], [-1]])
-
-# calculate cross product of column vectors
-a_cross_b_col = np.cross(a_col,b_col,axis=0)
diff --git a/Book4_Ch02_Python_Codes/Bk4_Ch2_12.py b/Book4_Ch02_Python_Codes/Bk4_Ch2_12.py
deleted file mode 100644
index d3f65d4..0000000
--- a/Book4_Ch02_Python_Codes/Bk4_Ch2_12.py
+++ /dev/null
@@ -1,27 +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_Ch2_12.py
-
-import numpy as np
-a = np.array([-2, 1, 1])
-b = np.array([1, -2, -1])
-# a = [-2, 1, 1]
-# b = [1, -2, -1]
-
-
-# calculate element-wise product of row vectors
-a_times_b = np.multiply(a, b)
-a_times_b_2 = a*b
-
-a_col = np.array([[-2], [1], [1]])
-b_col = np.array([[1], [-2], [-1]])
-
-# calculate element-wise product of column vectors
-a_times_b_col = np.multiply(a_col,b_col)
-a_times_b_col_2 = a_col*b_col
diff --git a/Book4_Ch02_Python_Codes/Bk4_Ch2_13.py b/Book4_Ch02_Python_Codes/Bk4_Ch2_13.py
deleted file mode 100644
index 43a539a..0000000
--- a/Book4_Ch02_Python_Codes/Bk4_Ch2_13.py
+++ /dev/null
@@ -1,40 +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_Ch2_13.py
-
-import matplotlib.pyplot as plt 
-import numpy as np
-import seaborn as sns
-
-def plot_heatmap(x,title):
-
-    fig, ax = plt.subplots()
-    ax = sns.heatmap(x,
-                     cmap='RdYlBu_r',
-                     cbar_kws={"orientation": "horizontal"}, vmin=-1, vmax=1)
-    ax.set_aspect("equal")
-    plt.title(title)
-
-a = np.array([[0.5],[-0.7],[1],[0.25],[-0.6],[-1]])
-b = np.array([[-0.8],[0.5],[-0.6],[0.9]])
-
-a_outer_b = np.outer(a, b)
-a_outer_a = np.outer(a, a)
-b_outer_b = np.outer(b, b)
-
-# Visualizations
-plot_heatmap(a,'a')
-
-plot_heatmap(b,'b')
-
-plot_heatmap(a_outer_b,'a outer b')
-
-plot_heatmap(a_outer_a,'a outer a')
-
-plot_heatmap(b_outer_b,'b outer b')
diff --git a/Book4_Ch02_Python_Codes/Streamlit_Bk4_Ch2_13.py b/Book4_Ch02_Python_Codes/Streamlit_Bk4_Ch2_13.py
deleted file mode 100644
index ecec3ad..0000000
--- a/Book4_Ch02_Python_Codes/Streamlit_Bk4_Ch2_13.py
+++ /dev/null
@@ -1,88 +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 streamlit as st
-import numpy as np
-from plotly.subplots import make_subplots
-import plotly.graph_objects as go
-import plotly.express as px
-
-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:
-    
-    num_a = st.slider('Number of rows, a:',
-              3,6,step = 1)
-    num_b = st.slider('Number of rows, b:',
-              3,6,step = 1)
-    
-a = np.random.uniform(0,1,num_a).reshape((-1,1))
-a = np.round(a,1)
-b = np.random.uniform(0,1,num_b).reshape((-1,1))
-b = np.round(b,1)
-
-show_number = False
-with st.sidebar:
-    show_number = st.checkbox('Display values')
-
-tensor_a_b = a@b.T
-
-#%% visualization
-
-st.latex('a = ' + bmatrix(a))
-st.latex('b = ' + bmatrix(b))
-st.latex('a \otimes b = ab^{T}')
-st.latex( bmatrix(a) + '@' + bmatrix(b.T) + ' = ' + bmatrix(tensor_a_b))
-col1, col2, col3 = st.columns(3)
-
-with col1:
-    fig_a = px.imshow(a, text_auto=show_number, 
-                  color_continuous_scale='viridis',
-                  aspect = 'equal')
-    
-    fig_a.update_layout(height=400, width=300)
-    fig_a.layout.coloraxis.showscale = False
-    st.plotly_chart(fig_a)
-
-with col2:
-    fig_b = px.imshow(b, text_auto=show_number, 
-                  color_continuous_scale='viridis',
-                  aspect = 'equal')
-    
-    fig_b.update_layout(height=400, width=300)
-    fig_b.layout.coloraxis.showscale = False
-    st.plotly_chart(fig_b)
-
-with col3:
-    fig_ab = px.imshow(tensor_a_b, text_auto=show_number, 
-                  color_continuous_scale='viridis',
-                  aspect = 'equal')
-    
-    fig_ab.update_layout(height=400, width=400)
-    
-    fig_ab.layout.coloraxis.showscale = False
-    st.plotly_chart(fig_ab)
-
-
-
-
-
-