machine-learning-in-materials-sciences · nickcafferry · Aug 6, 2020 · Aug 5, 2020 · Aug 5, 2020 · Aug 5, 2020
diff --git a/02_TensorFlow_Way/index.rst b/02_TensorFlow_Way/index.rst
@@ -6,7 +6,7 @@
    creating a simple classifier and then show an example of evaluating regression and 
    classification algorithms.
 
-
+下载本章 :download:`Jupyter Notebook <https://github.com/qmlcode/qml/blob/master/qml/wrappers.py>`
 
 
 计算图

diff --git a/04_Support_Vector_Machines/index.rst b/04_Support_Vector_Machines/index.rst
@@ -11,13 +11,10 @@
 .. toctree::
        :maxdepth: 3
 
-       /03_Linear_Regression/01_Using_the_Matrix_Inverse_Method/index
+       /04_Support_Vector_Machines/01_Introduction/index
 
 
-We introduce the concept of SVMs and how we will go about implementing them in the TensorFlow 
-framework.
-
-下载本章 :download:`Jupyter Notebook </03_Linear_Regression/01_Using_the_Matrix_Inverse_Method/01_lin_reg_inverse.ipynb>`
+We introduce the concept of SVMs and how we will go about implementing them in the TensorFlow framework.
 
 ------------
 
@@ -26,14 +23,14 @@ framework.
 .. toctree::
        :maxdepth: 3
 
-       /03_Linear_Regression/02_Implementing_a_Decomposition_Method/index
+       /04_Support_Vector_Machines/02_Working_with_Linear_SVMs/index
 
 We create a linear SVM to separate I. setosa based on sepal length and pedal width in the Iris
 data set.
 
 .. image:: 
 
-下载本章 :download:`Jupyter Notebook </03_Linear_Regression/02_Implementing_a_Decomposition_Method/02_lin_reg_decomposition.ipynb>`
+下载本章 :download:`Jupyter Notebook </04_Support_Vector_Machines/02_Working_with_Linear_SVMs/02_linear_svm.ipynb>`
 
 -----
 
@@ -42,14 +39,13 @@ data set.
 .. toctree::
        :maxdepth: 3
 
-       /03_Linear_Regression/03_TensorFlow_Way_of_Linear_Regression/index
+       /04_Support_Vector_Machines/03_Reduction_to_Linear_Regression/index
 
-The heart of SVMs is separating classes with a line.  We change tweek the algorithm slightly
-to perform SVM regression.
+The heart of SVMs is separating classes with a line.  We change tweek the algorithm slightly to perform SVM regression.
 
 .. image:: 
 
-下载本章 :download:`Jupyter Notebook </03_Linear_Regression/03_TensorFlow_Way_of_Linear_Regression/03_lin_reg_tensorflow_way.ipynb>`
+下载本章 :download:`Jupyter Notebook </04_Support_Vector_Machines/03_Reduction_to_Linear_Regression/03_support_vector_regression.ipynb>`
 
 -----------
 
@@ -58,12 +54,12 @@ TensorFlow中的核
 .. toctree::
        :maxdepth: 3
 
-       /03_Linear_Regression/04_Loss_Functions_in_Linear_Regressions/index
+       /04_Support_Vector_Machines/04_Working_with_Kernels/index
 
 In order to extend SVMs into non-linear data, we explain and show how to implement different kernels 
 in TensorFlow.
 
-下载本章 :download:`Jupyter Notebook </03_Linear_Regression/04_Loss_Functions_in_Linear_Regressions/04_lin_reg_l1_vs_l2.ipynb>`
+下载本章 :download:`Jupyter Notebook </04_Support_Vector_Machines/04_Working_with_Kernels/04_svm_kernels.ipynb>`
 
 -----------
 
@@ -72,11 +68,11 @@ in TensorFlow.
 .. toctree::
        :maxdepth: 3
 
-       /03_Linear_Regression/05_Implementing_Deming_Regression/index
+       /04_Support_Vector_Machines/05_Implementing_Nonlinear_SVMs/index
 
 We use the Gaussian kernel (RBF) to separate non-linear classes.
 
-下载本章 :download:`Jupyter Notebook </03_Linear_Regression/05_Implementing_Deming_Regression/05_deming_regression.ipynb>`
+下载本章 :download:`Jupyter Notebook </04_Support_Vector_Machines/05_Implementing_Nonlinear_SVMs/05_nonlinear_svm.ipynb>`
 
 -------------
 
@@ -86,14 +82,14 @@ We use the Gaussian kernel (RBF) to separate non-linear classes.
 .. toctree::
        :maxdepth: 3
 
-       /03_Linear_Regression/06_Implementing_Lasso_and_Ridge_Regression/index
+       /04_Support_Vector_Machines/06_Implementing_Multiclass_SVMs/index
 
 SVMs are inherently binary predictors.  We show how to extend them in a one-vs-all strategy in 
 TensorFlow.     
 
 .. image::
 
-下载本章 :download:`Jupyter Notebook </03_Linear_Regression/06_Implementing_Lasso_and_Ridge_Regression/06_lasso_and_ridge_regression.ipynb>`
+下载本章 :download:`Jupyter Notebook </04_Support_Vector_Machines/06_Implementing_Multiclass_SVMs/06_multiclass_svm.ipynb>`
 
 -----------
 

diff --git a/05_Nearest_Neighbor_Methods/index.rst b/05_Nearest_Neighbor_Methods/index.rst
@@ -1,39 +1,124 @@
-Nearest Neighbor methods are a very popular ML algorithm.  We show how to implement k-Nearest 
-Neighbors, weighted k-Nearest Neighbors, and k-Nearest Neighbors with mixed distance functions. 
-In this chapter we also show how to use the Levenshtein distance (edit distance) in TensorFlow, 
-and use it to calculate the distance between strings. We end this chapter with showing how to 
-use k-Nearest Neighbors for categorical prediction with the MNIST handwritten digit recognition.
+.. note::
 
-引言
-=====
+   Nearest Neighbor methods are a very popular ML algorithm.  We show how to implement k-Nearest 
+   Neighbors, weighted k-Nearest Neighbors, and k-Nearest Neighbors with mixed distance functions. 
+   In this chapter we also show how to use the Levenshtein distance (edit distance) in TensorFlow, 
+   and use it to calculate the distance between strings. We end this chapter with showing how to 
+   use k-Nearest Neighbors for categorical prediction with the MNIST handwritten digit recognition.
 
+引言
+----------------
+.. toctree::
+       :maxdepth: 3
+
+       /05_Nearest_Neighbor_Methods/01_Introduction/index
+
 We introduce the concepts and methods needed for performing k-Nearest Neighbors in TensorFlow.
 
+------------
+
 最近邻法的使用
-=============
+---------------
+.. toctree::
+       :maxdepth: 3
+
+       /05_Nearest_Neighbor_Methods/02_Working_with_Nearest_Neighbors/index
 
 We create a nearest neighbor algorithm that tries to predict housing worth (regression).
 
+
+.. image:: 
+
+下载本章 :download:`Jupyter Notebook </05_Nearest_Neighbor_Methods/02_Working_with_Nearest_Neighbors/02_nearest_neighbor.ipynb>`
+
+-----
+
 文本距离函数
-============
+--------------
+.. toctree::
+       :maxdepth: 3
+
+       /05_Nearest_Neighbor_Methods/03_Working_with_Text_Distances/index
 
 In order to use a distance function on text, we show how to use edit distances in TensorFlow.
 
+.. image:: 
+
+下载本章 :download:`Jupyter Notebook </05_Nearest_Neighbor_Methods/03_Working_with_Text_Distances/03_text_distances.ipynb>`
+
+-----------
+
 计算混合距离函数
-===============
+----------
+.. toctree::
+       :maxdepth: 3
+
+       /05_Nearest_Neighbor_Methods/04_Computing_with_Mixed_Distance_Functions/index
 
 Here we implement scaling of the distance function by the standard deviation of the input 
 feature for k-Nearest Neighbors.
 
+
+下载本章 :download:`Jupyter Notebook </05_Nearest_Neighbor_Methods/04_Computing_with_Mixed_Distance_Functions/04_mixed_distance_functions_knn.ipynb>`
+
+-----------
+
 地址匹配
-========
+-------------
+.. toctree::
+       :maxdepth: 3
+
+       /05_Nearest_Neighbor_Methods/05_An_Address_Matching_Example/index
+
 
 We use a mixed distance function to match addresses. We use numerical distance for zip codes,
 and string edit distance for street names. The street names are allowed to have typos.
 
+
+下载本章 :download:`Jupyter Notebook </05_Nearest_Neighbor_Methods/05_An_Address_Matching_Example/05_address_matching.ipynb>`
+
+-------------
+
 图像处理的近邻法
-==============
-
+-----------
+
+.. toctree::
+       :maxdepth: 3
+
+       /05_Nearest_Neighbor_Methods/06_Nearest_Neighbors_for_Image_Recognition/index
+
+
 The MNIST digit image collection is a great data set for illustration of how to perform 
 k-Nearest Neighbors for an image classification task.
 
+.. image::
+
+下载本章 :download:`Jupyter Notebook </05_Nearest_Neighbor_Methods/06_Nearest_Neighbors_for_Image_Recognition/06_image_recognition.ipynb>`
+
+-----------
+
+本章学习模块
+-----------
+
+.. Submodules
+.. ----------
+
+*tensorflow\.zeros* 
+^^^^^^^^^^^^^^^^^^^
+
+.. automodule:: tensorflow.zeros
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+------
+
+*tensorflow\.ones*
+^^^^^^^^^^^^^^^^^^
+
+.. automodule:: tensorflow.ones
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+-------------