Merge branch 'donnemartin/master'

2024-03-22 13:30:56 +08:00 · 2017-02-13 12:12:16 -08:00 · 2017-02-13 12:12:16 -08:00 · 80ae3bf64c
commit 80ae3bf64c
parent db3bb2263a a238b141f3
9 changed files with 63 additions and 57 deletions
--- a/README.md
+++ b/README.md
@ -12,8 +12,8 @@

 ## Index

-* [scikit-learn](#scikit-learn)
 * [kaggle-and-business-analyses](#kaggle-and-business-analyses)
+* [scikit-learn](#scikit-learn)
 * [deep-learning](#deep-learning)
 * [statistical-inference-scipy](#statistical-inference-scipy)
 * [pandas](#pandas)
@ -31,6 +31,20 @@
 * [contact-info](#contact-info)
 * [license](#license)

+<br/>
+<p align="center">
+  <img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/kaggle.png">
+</p>
+
+## kaggle-and-business-analyses
+
+IPython Notebook(s) used in [kaggle](https://www.kaggle.com/) competitions and business analyses.
+
+| Notebook | Description |
+|-------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------|
+| [titanic](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/kaggle/titanic.ipynb) | Predict survival on the Titanic.  Learn data cleaning, exploratory data analysis, and machine learning. |
+| [churn-analysis](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/analyses/churn.ipynb) | Predict customer churn.  Exercise logistic regression, gradient boosting classifers, support vector machines, random forests, and k-nearest-neighbors.  Includes discussions of confusion matrices, ROC plots, feature importances, prediction probabilities, and calibration/descrimination.|
+
 <br/>
 <p align="center">
  <img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/scikitlearn.png">
@ -52,20 +66,6 @@ IPython Notebook(s) demonstrating scikit-learn functionality.
 | [gmm](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-gmm.ipynb) | Implement Gaussian mixture models in scikit-learn. |
 | [validation](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-validation.ipynb) | Implement validation and model selection in scikit-learn. |

-<br/>
-<p align="center">
-  <img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/kaggle.png">
-</p>
-
-## kaggle-and-business-analyses
-
-IPython Notebook(s) used in [kaggle](https://www.kaggle.com/) competitions and business analyses.
-
-| Notebook | Description |
-|-------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------|
-| [titanic](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/kaggle/titanic.ipynb) | Predict survival on the Titanic.  Learn data cleaning, exploratory data analysis, and machine learning. |
-| [churn-analysis](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/analyses/churn.ipynb) | Predict customer churn.  Exercise logistic regression, gradient boosting classifers, support vector machines, random forests, and k-nearest-neighbors.  Includes discussions of confusion matrices, ROC plots, feature importances, prediction probabilities, and calibration/descrimination.|
-
 <br/>
 <p align="center">
  <img src="http://i.imgur.com/ZhKXrKZ.png">
@ -97,6 +97,8 @@ Additional TensorFlow tutorials:

 * [pkmital/tensorflow_tutorials](https://github.com/pkmital/tensorflow_tutorials)
 * [nlintz/TensorFlow-Tutorials](https://github.com/nlintz/TensorFlow-Tutorials)
+* [alrojo/tensorflow-tutorial](https://github.com/alrojo/tensorflow-tutorial)
+* [BinRoot/TensorFlow-Book](https://github.com/BinRoot/TensorFlow-Book)

 | Notebook | Description |
 |--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
@ -324,11 +326,13 @@ For detailed instructions, scripts, and tools to set up your development environ

 To view interactive content or to modify elements within the IPython notebooks, you must first clone or download the repository then run the ipython notebook.  More information on IPython Notebooks can be found [here.](http://ipython.org/notebook.html)

-```
-$ git clone https://github.com/donnemartin/data-science-ipython-notebooks.git
-$ cd data-science-ipython-notebooks
-$ ipython notebook
-```
+    $ git clone https://github.com/donnemartin/data-science-ipython-notebooks.git
+    $ cd data-science-ipython-notebooks
+    $ ipython notebook
+
+If you have Jupyter Notebook 4+, run the following instead of `ipython notebook`:
+
+    $ jupyter notebook

 Notebooks tested with Python 2.7.x.

@ -341,6 +345,8 @@ Notebooks tested with Python 2.7.x.
 * [TensorFlow Examples](https://github.com/aymericdamien/TensorFlow-Examples) by Aymeric Damien
 * [TensorFlow Tutorials](https://github.com/pkmital/tensorflow_tutorials) by Parag K Mital
 * [TensorFlow Tutorials](https://github.com/nlintz/TensorFlow-Tutorials) by Nathan Lintz
+* [TensorFlow Tutorials](https://github.com/alrojo/tensorflow-tutorial) by Alexander R Johansen
+* [TensorFlow Book](https://github.com/BinRoot/TensorFlow-Book) by Nishant Shukla
 * [Summer School 2015](https://github.com/mila-udem/summerschool2015) by mila-udem
 * [Kaggle](https://www.kaggle.com/)
 * [Yhat Blog](http://blog.yhat.com/)
--- a/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb
+++ b/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb
@ -230,7 +230,7 @@
   "outputs": [],
   "source": [
    "# Initializing the variables\n",
-    "init = tf.initialize_all_variables()"
+    "init = tf.global_variables_initializer()"
   ]
  },
  {
@ -337,7 +337,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
-   "version": "2.7.5+"
+   "version": "2.7.12"
  }
 },
 "nbformat": 4,
--- a/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/convolutional_network.ipynb
+++ b/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/convolutional_network.ipynb
@ -213,7 +213,7 @@
   "outputs": [],
   "source": [
    "# Initializing the variables\n",
-    "init = tf.initialize_all_variables()"
+    "init = tf.global_variables_initializer()"
   ]
  },
  {
@ -313,7 +313,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
-   "version": "2.7.5+"
+   "version": "2.7.12"
  }
 },
 "nbformat": 4,
--- a/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/multilayer_perceptron.ipynb
+++ b/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/multilayer_perceptron.ipynb
@ -165,7 +165,7 @@
   "outputs": [],
   "source": [
    "# Initializing the variables\n",
-    "init = tf.initialize_all_variables()"
+    "init = tf.global_variables_initializer()"
   ]
  },
  {
@ -231,21 +231,21 @@
 ],
 "metadata": {
  "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
   "language": "python",
-   "name": "python3"
+   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
-    "version": 3
+    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
  }
 },
 "nbformat": 4,
--- a/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/recurrent_network.ipynb
+++ b/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/recurrent_network.ipynb
@ -236,7 +236,7 @@
   ],
   "source": [
    "# Initializing the variables\n",
-    "init = tf.initialize_all_variables()\n",
+    "init = tf.global_variables_initializer()\n",
    "\n",
    "# Launch the graph\n",
    "with tf.Session() as sess:\n",
@ -286,7 +286,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
-   "version": "2.7.5+"
+   "version": "2.7.12"
  }
 },
 "nbformat": 4,
--- a/deep-learning/tensor-flow-exercises/2_fullyconnected.ipynb
+++ b/deep-learning/tensor-flow-exercises/2_fullyconnected.ipynb
@ -381,7 +381,7 @@
    "  # This is a one-time operation which ensures the parameters get initialized as\n",
    "  # we described in the graph: random weights for the matrix, zeros for the\n",
    "  # biases. \n",
-    "  tf.initialize_all_variables().run()\n",
+    " tf.global_variables_initializer().run()\n",
    "  print 'Initialized'\n",
    "  for step in xrange(num_steps):\n",
    "    # Run the computations. We tell .run() that we want to run the optimizer,\n",
@ -544,7 +544,7 @@
    "num_steps = 3001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
-    "  tf.initialize_all_variables().run()\n",
+    "  tf.global_variables_initializer().run()\n",
    "  print \"Initialized\"\n",
    "  for step in xrange(num_steps):\n",
    "    # Pick an offset within the training data, which has been randomized.\n",
@ -589,21 +589,21 @@
  "colab_default_view": {},
  "colab_views": {},
  "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
   "language": "python",
-   "name": "python3"
+   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
-    "version": 3
+    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
  }
 },
 "nbformat": 4,
--- a/deep-learning/tensor-flow-exercises/4_convolutions.ipynb
+++ b/deep-learning/tensor-flow-exercises/4_convolutions.ipynb
@ -412,7 +412,7 @@
    "num_steps = 1001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
-    "  tf.initialize_all_variables().run()\n",
+    "  tf.global_variables_initializer().run()\n",
    "  print \"Initialized\"\n",
    "  for step in xrange(num_steps):\n",
    "    offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
@ -467,21 +467,21 @@
  "colab_default_view": {},
  "colab_views": {},
  "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
   "language": "python",
-   "name": "python3"
+   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
-    "version": 3
+    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
  }
 },
 "nbformat": 4,
--- a/deep-learning/tensor-flow-exercises/5_word2vec.ipynb
+++ b/deep-learning/tensor-flow-exercises/5_word2vec.ipynb
@ -744,7 +744,7 @@
    "num_steps = 100001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
-    "  tf.initialize_all_variables().run()\n",
+    "  tf.global_variables_initializer().run()\n",
    "  print \"Initialized\"\n",
    "  average_loss = 0\n",
    "  for step in xrange(num_steps):\n",
@ -867,21 +867,21 @@
  "colab_default_view": {},
  "colab_views": {},
  "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
   "language": "python",
-   "name": "python3"
+   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
-    "version": 3
+    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
  }
 },
 "nbformat": 4,
--- a/deep-learning/tensor-flow-exercises/6_lstm.ipynb
+++ b/deep-learning/tensor-flow-exercises/6_lstm.ipynb
@ -928,7 +928,7 @@
    "summary_frequency = 100\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
-    "  tf.initialize_all_variables().run()\n",
+    "  tf.global_variables_initializer().run()\n",
    "  print 'Initialized'\n",
    "  mean_loss = 0\n",
    "  for step in xrange(num_steps):\n",
@ -1042,21 +1042,21 @@
  "colab_default_view": {},
  "colab_views": {},
  "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
   "language": "python",
-   "name": "python3"
+   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
-    "version": 3
+    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
  }
 },
 "nbformat": 4,