add perceptron tut

Author: irisliucy

.DS_Store

@@ -6,19 +6,19 @@
    "source": [
     "# Python Basics with Numpy\n",
     "\n",
-    "Welcome to your first assignment. This exercise gives you a brief introduction to Python. Even if you've used Python before, this will help familiarize you with functions we'll need.  \n",
+    "This exercise gives you a brief introduction to Python. Even if you've used Python before, this will help familiarize you with functions we'll need.  \n",
     "\n",
     "**Instructions:**\n",
     "- You will be using Python 3.\n",
     "- Avoid using for-loops and while-loops, unless you are explicitly told to do so.\n",
-    "- Do not modify the (# GRADED FUNCTION [function name]) comment in some cells. Your work would not be graded if you change this. Each cell containing that comment should only contain one function.\n",
-    "- After coding your function, run the cell right below it to check if your result is correct.\n",
+    "- Fill in `python_basics_with_numpy.ipynb`. After coding your function, run the cell right below it to check if your result is correct.\n",
+    "- You can follow `python_basics_with_numpy_guide.html` or `python_basics_with_numpy_answer.ipynb` for correct answer\n",
     "\n",
     "**After this assignment you will:**\n",
     "- Be able to use iPython Notebooks\n",
     "- Be able to use numpy functions and numpy matrix/vector operations\n",
-    "- Understand the concept of \"broadcasting\"\n",
     "- Be able to vectorize code\n",
+    "- Be able to read and write image and csv file\n",
     "\n",
     "Let's get started!"
    ]
@@ -377,7 +377,7 @@
     "collapsed": true
    },
    "source": [
-    "## 2) Vectorization"
+    "## 2 - Vectorization"
    ]
   },
   {
@@ -482,7 +482,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 2.1 Implement the L1 and L2 loss functions\n",
+    "### 2.1 - Implement the L1 and L2 loss functions\n",
     "\n",
     "**Exercise**: Implement the numpy vectorized version of the L1 loss. You may find the function `abs(x)` (absolute value of x) and `np.sum(x)` (summation of array elements) useful.\n",
     "\n",
@@ -611,7 +611,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 3) I/O Reading"
+    "## 3 - I/O Reading"
    ]
   },
   {
@@ -625,7 +625,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3.1 Reading Images by spliting color channels\n",
+    "### 3.1 - Reading Images by spliting color channels\n",
     "**Exercise**: Read a colored image and output it to a grayscale image. \n",
     "Method 1: We can manipulate [this formula](https://en.wikipedia.org/wiki/Grayscale#Converting_color_to_grayscale) to change an image from RGB channel to a single greyscale channel.\n",
     "\n",
@@ -668,7 +668,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3.2 Reading Images with library\n",
+    "### 3.2 - Reading Images with library\n",
     "**Exercise**: Read a colored image and output it to a grayscale image. \n",
     "Method 2: We can make use of the built-in function in \"Pillow\" library to directly convert a colored image to greyscale using `Image.convert('L')`\n",
     "\n",
@@ -710,7 +710,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3.3 Reading CSV \n",
+    "### 3.3 - Reading CSV \n",
     "**Exercise**: Read an array from CSV using the \"csv\" library.\n",
     "\n",
     "**Note**:\n",
@@ -739,7 +739,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3.3 Writing CSV \n",
+    "### 3.4 - Writing CSV \n",
     "**Exercise**: Write a matrix from CSV using the \"csv\" library.\n",
     "\n",
     "**Note**:\n",