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diff --git a/_sources/chapters/data-analytics/numpy-basic.ipynb b/_sources/chapters/data-analytics/numpy-basic.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# NumPy tutorial\n",
     "\n",
-    "[NumPy](http://www.numpy.org/) is the core library for scientific computing in Python. It vastly simplifies manipulating and crunching vectors and matrices. Many of python-based leading packages rely on NumPy as a infrastructure piece."
+    "[NumPy](http://www.numpy.org/) is the **core** package for scientific computing in Python. It vastly simplifies manipulating and crunching vectors and matrices. Many of other leading packages rely on NumPy as a infrastructure piece."
    ]
   },
   {
@@ -167,9 +167,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[0.69381927]\n",
-      " [0.62248209]\n",
-      " [0.41387215]]\n"
+      "[[0.67742525]\n",
+      " [0.0784592 ]\n",
+      " [0.6098676 ]]\n"
      ]
     }
    ],
@@ -178,7 +178,7 @@
     "d = np.zeros((2, 2))      # Create an array of all zeros with shape (2, 2)\n",
     "d = np.ones((1, 2))       # Create an array of all ones with shape (1, 2)\n",
     "d = np.random.random((3, 1))  # Create an array of random values with shape (3, 1)\n",
-    "# try printing them\n",
+    "# Try printing them\n",
     "print(d)"
    ]
   },
@@ -191,7 +191,7 @@
    "source": [
     "## Array Indexing\n",
     "\n",
-    "There are several ways to pull out a section of arrays. The most common ways include **slicing**, **integer array indexing** and **Boolean array indexing**. We may choose the appropriate indexing methods for different purposes."
+    "The most common ways to pull out a section of arrays include **slicing**, **integer array indexing** and **Boolean array indexing**. We may choose the appropriate indexing methods for different purposes."
    ]
   },
   {
@@ -398,17 +398,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[4 5 6] (3,)\n",
-      "[[4 5 6]] (1, 3)\n"
+      "[1 2 3] (3,)\n",
+      "[[1 2 3]] (1, 3)\n"
      ]
     }
    ],
    "source": [
     "a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])\n",
-    "row_r1 = a[1, :]    # Mix integer indexing with slice indexing\n",
-    "row_r2 = a[1:2, :]  # Slice indexing\n",
-    "print(row_r1, row_r1.shape)  # Lower rank\n",
-    "print(row_r2, row_r2.shape)"
+    "a_1row = a[0, :]     # Mix integer indexing with slice indexing\n",
+    "a_2rows = a[0:1, :]  # Slice indexing\n",
+    "print(a_1row, a_1row.shape)   # Lower rank\n",
+    "print(a_2rows, a_2rows.shape) "
    ]
   },
   {
@@ -481,15 +481,18 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 9 10 11 12]\n"
-     ]
+     "data": {
+      "text/plain": [
+       "array([ 9, 10, 11, 12])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "print(a[a > 8])"
+    "a[a > 8]"
    ]
   },
   {
@@ -689,7 +692,7 @@
     }
    ],
    "source": [
-    "x = np.array([[1, 2], [3, 4]], dtype=np.float64)\n",
+    "x = np.array([[1, 2], [3, 4]], dtype=np.float64) # Set data types of elements by dtype\n",
     "y = np.array([[5, 6], [7, 8]], dtype=np.float64)\n",
     "\n",
     "# Elementwise sum; both produce an array\n",
@@ -989,7 +992,7 @@
     "id": "89e2FXxFL9jQ"
    },
    "source": [
-    "Broadcasting typically makes your code more ***concise***, ***readable***, and more importantly, ***faster***, so you should strive to use it where possible."
+    "Broadcasting typically makes your code more ***concise***, ***readable***, and more importantly, ***faster***."
    ]
   },
   {

diff --git a/chapters/data-analytics/numpy-basic.html b/chapters/data-analytics/numpy-basic.html
@@ -424,7 +424,7 @@ <h2> Contents </h2>
 
   <section class="tex2jax_ignore mathjax_ignore" id="numpy-tutorial">
 <h1><span class="section-number">2.1. </span>NumPy tutorial<a class="headerlink" href="#numpy-tutorial" title="Permalink to this heading">#</a></h1>
-<p><a class="reference external" href="http://www.numpy.org/">NumPy</a> is the core library for scientific computing in Python. It vastly simplifies manipulating and crunching vectors and matrices. Many of python-based leading packages rely on NumPy as a infrastructure piece.</p>
+<p><a class="reference external" href="http://www.numpy.org/">NumPy</a> is the <strong>core</strong> package for scientific computing in Python. It vastly simplifies manipulating and crunching vectors and matrices. Many of other leading packages rely on NumPy as a infrastructure piece.</p>
 <p>In this tutorial, we will cover:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">numpy</span></code>: Array, Array Indexing, Array Manipulation, Array Math &amp; Broadcasting.</p></li>
@@ -498,23 +498,23 @@ <h2><span class="section-number">2.1.1. </span>Array and its Creation<a class="h
 <span class="n">d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>      <span class="c1"># Create an array of all zeros with shape (2, 2)</span>
 <span class="n">d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>       <span class="c1"># Create an array of all ones with shape (1, 2)</span>
 <span class="n">d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>  <span class="c1"># Create an array of random values with shape (3, 1)</span>
-<span class="c1"># try printing them</span>
+<span class="c1"># Try printing them</span>
 <span class="nb">print</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[0.69381927]
- [0.62248209]
- [0.41387215]]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[0.67742525]
+ [0.0784592 ]
+ [0.6098676 ]]
 </pre></div>
 </div>
 </div>
 </div>
 </section>
 <section id="array-indexing">
 <h2><span class="section-number">2.1.2. </span>Array Indexing<a class="headerlink" href="#array-indexing" title="Permalink to this heading">#</a></h2>
-<p>There are several ways to pull out a section of arrays. The most common ways include <strong>slicing</strong>, <strong>integer array indexing</strong> and <strong>Boolean array indexing</strong>. We may choose the appropriate indexing methods for different purposes.</p>
+<p>The most common ways to pull out a section of arrays include <strong>slicing</strong>, <strong>integer array indexing</strong> and <strong>Boolean array indexing</strong>. We may choose the appropriate indexing methods for different purposes.</p>
 <ul class="simple">
 <li><p><em><strong>Slicing</strong></em></p></li>
 </ul>
@@ -604,16 +604,16 @@ <h2><span class="section-number">2.1.2. </span>Array Indexing<a class="headerlin
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="mi">12</span><span class="p">]])</span>
-<span class="n">row_r1</span> <span class="o">=</span> <span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="p">:]</span>    <span class="c1"># Mix integer indexing with slice indexing</span>
-<span class="n">row_r2</span> <span class="o">=</span> <span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span> <span class="p">:]</span>  <span class="c1"># Slice indexing</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">row_r1</span><span class="p">,</span> <span class="n">row_r1</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>  <span class="c1"># Lower rank</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">row_r2</span><span class="p">,</span> <span class="n">row_r2</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="n">a_1row</span> <span class="o">=</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:]</span>     <span class="c1"># Mix integer indexing with slice indexing</span>
+<span class="n">a_2rows</span> <span class="o">=</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span> <span class="p">:]</span>  <span class="c1"># Slice indexing</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">a_1row</span><span class="p">,</span> <span class="n">a_1row</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>   <span class="c1"># Lower rank</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">a_2rows</span><span class="p">,</span> <span class="n">a_2rows</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> 
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[4 5 6] (3,)
-[[4 5 6]] (1, 3)
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[1 2 3] (3,)
+[[1 2 3]] (1, 3)
 </pre></div>
 </div>
 </div>
@@ -648,12 +648,12 @@ <h2><span class="section-number">2.1.2. </span>Array Indexing<a class="headerlin
 <p>We can do all of the above in a single concise statement, which is more readable.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">a</span><span class="p">[</span><span class="n">a</span> <span class="o">&gt;</span> <span class="mi">8</span><span class="p">])</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">a</span><span class="p">[</span><span class="n">a</span> <span class="o">&gt;</span> <span class="mi">8</span><span class="p">]</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 9 10 11 12]
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([ 9, 10, 11, 12])
 </pre></div>
 </div>
 </div>
@@ -744,7 +744,7 @@ <h2><span class="section-number">2.1.4. </span>Array Math<a class="headerlink" h
 <h3><span class="section-number">2.1.4.1. </span>Basic Arithmetic<a class="headerlink" href="#basic-arithmetic" title="Permalink to this heading">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span> <span class="c1"># Set data types of elements by dtype</span>
 <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
 
 <span class="c1"># Elementwise sum; both produce an array</span>
@@ -929,7 +929,7 @@ <h2><span class="section-number">2.1.5. </span>One more thing: Broadcasting<a cl
 </div>
 </div>
 <a class="reference internal image-reference" href="../../_images/numpy_broadcast2.png"><img alt="../../_images/numpy_broadcast2.png" class="align-center" src="../../_images/numpy_broadcast2.png" style="width: 600px;" /></a>
-<p>Broadcasting typically makes your code more <em><strong>concise</strong></em>, <em><strong>readable</strong></em>, and more importantly, <em><strong>faster</strong></em>, so you should strive to use it where possible.</p>
+<p>Broadcasting typically makes your code more <em><strong>concise</strong></em>, <em><strong>readable</strong></em>, and more importantly, <em><strong>faster</strong></em>.</p>
 </section>
 <section id="references">
 <h2><span class="section-number">2.1.6. </span>References<a class="headerlink" href="#references" title="Permalink to this heading">#</a></h2>

diff --git a/searchindex.js b/searchindex.js