remove requirement file, added conda environment file

Author: dibgerge

.gitignore

@@ -108,3 +108,4 @@ venv.bak/
 *.pkl
 *-solved.ipynb
 
+.idea/

Exercise1/exercise1.ipynb

@@ -1299,7 +1299,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.6"
   }
  },
  "nbformat": 4,

Exercise3/exercise3.ipynb

@@ -915,7 +915,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.6"
   }
  },
  "nbformat": 4,

Exercise4/exercise4.ipynb

@@ -710,15 +710,27 @@
     "</div>\n",
     "\n",
     "<div class=\"alert alert-box alert-success\">\n",
-    "**Practical Tip:** Gradient checking works for any function where you are computing the cost and the gradient. Concretely, you can use the same `computeNumericalGradient` function to check if your gradient implementations for the other exercises are correct too (e.g., logistic regression’s cost function).\n",
+    "    <b>Practical Tip:</b> Gradient checking works for any function where you are computing the cost and the gradient. Concretely, you can use the same `computeNumericalGradient` function to check if your gradient implementations for the other exercises are correct too (e.g., logistic regression’s cost function).\n",
     "</div>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'utils' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-1-d4995b4088e4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheckNNGradients\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnnCostFunction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'utils' is not defined"
+     ]
+    }
+   ],
    "source": [
     "utils.checkNNGradients(nnCostFunction)"
    ]
@@ -916,7 +928,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.6"
   }
  },
  "nbformat": 4,

Exercise5/exercise5.ipynb

@@ -19,9 +19,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# used for manipulating directory paths\n",
@@ -141,9 +139,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def linearRegCostFunction(X, y, theta, lambda_=0.0):\n",
@@ -359,9 +355,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def learningCurve(X, y, Xval, yval, lambda_=0):\n",
@@ -528,9 +522,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def polyFeatures(X, p):\n",
@@ -732,9 +724,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def validationCurve(X, y, Xval, yval):\n",
@@ -896,9 +886,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }

README.md

@@ -57,7 +57,7 @@ If you are on a windows machine:
 
 Once you have installed python, create a new python environment will all the requirements using the following command: 
 
-    conda create -n machine_learning python=3.6 scipy=1 numpy=1.13 matplotlib=2.1 jupyter
+    conda env create -f environment.yml
 
 After the new environment is setup, activate it using (windows)
 

environment.yml

@@ -0,0 +1,9 @@
+name: machine_learning
+channels:
+  - defaults
+dependencies:
+  - jupyter=1.0.0
+  - matplotlib=2.1.2
+  - numpy=1.13.3
+  - python=3.6.4
+  - scipy=1.0.0