Fix donnemartin#49: Keras tutorial links (donnemartin#52)

Author: donnemartin

README.md

@@ -108,18 +108,18 @@ Additional TensorFlow tutorials:
 | Notebook | Description |
 |--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | keras | Keras is an open source neural network library written in Python. It is capable of running on top of either Tensorflow or Theano. |
-| [setup](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/0.%20Preamble.ipynb) | Learn about the tutorial goals and how to set up your Keras environment. |
-| [intro-deep-learning-ann](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/1.1%20Introduction%20-%20Deep%20Learning%20and%20ANN.ipynb) | Get an intro to deep learning with Keras and Artificial Neural Networks (ANN). |
-| [theano](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/1.2%20Introduction%20-%20Theano.ipynb) | Learn about Theano by working with weights matrices and gradients. |
-| [keras-otto](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/1.3%20Introduction%20-%20Keras.ipynb) | Learn about Keras by looking at the Kaggle Otto challenge. |
-| [ann-mnist](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/1.4%20(Extra)%20A%20Simple%20Implementation%20of%20ANN%20for%20MNIST.ipynb) | Review a simple implementation of ANN for MNIST using Keras. |
-| [conv-nets](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/2.1%20Supervised%20Learning%20-%20ConvNets.ipynb) | Learn about Convolutional Neural Networks (CNNs) with Keras. |
-| [conv-net-1](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/2.2.1%20Supervised%20Learning%20-%20ConvNet%20HandsOn%20Part%20I.ipynb) | Recognize handwritten digits from MNIST using Keras - Part 1. |
-| [conv-net-2](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/2.2.2%20Supervised%20Learning%20-%20ConvNet%20HandsOn%20Part%20II.ipynb) | Recognize handwritten digits from MNIST using Keras - Part 2. |
-| [keras-models](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/2.3%20Supervised%20Learning%20-%20Famous%20Models%20with%20Keras.ipynb) | Use pre-trained models such as VGG16, VGG19, ResNet50, and Inception v3 with Keras. |
-| [auto-encoders](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/3.1%20Unsupervised%20Learning%20-%20AutoEncoders%20and%20Embeddings.ipynb) | Learn about Autoencoders with Keras. |
-| [rnn-lstm](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/3.2%20RNN%20and%20LSTM.ipynb) | Learn about Recurrent Neural Networks (RNNs) with Keras. |
-| [lstm-sentence-gen](http://nbviewer.ipython.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/3.3%20(Extra)%20LSTM%20for%20Sentence%20Generation.ipynb) |  Learn about RNNs using Long Short Term Memory (LSTM) networks with Keras. |
+| [setup](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/0.%20Preamble.ipynb) | Learn about the tutorial goals and how to set up your Keras environment. |
+| [intro-deep-learning-ann](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/1.1%20Introduction%20-%20Deep%20Learning%20and%20ANN.ipynb) | Get an intro to deep learning with Keras and Artificial Neural Networks (ANN). |
+| [theano](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/1.2%20Introduction%20-%20Theano.ipynb) | Learn about Theano by working with weights matrices and gradients. |
+| [keras-otto](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/1.3%20Introduction%20-%20Keras.ipynb) | Learn about Keras by looking at the Kaggle Otto challenge. |
+| [ann-mnist](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/1.4%20%28Extra%29%20A%20Simple%20Implementation%20of%20ANN%20for%20MNIST.ipynb) | Review a simple implementation of ANN for MNIST using Keras. |
+| [conv-nets](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/2.1%20Supervised%20Learning%20-%20ConvNets.ipynb) | Learn about Convolutional Neural Networks (CNNs) with Keras. |
+| [conv-net-1](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/2.2.1%20Supervised%20Learning%20-%20ConvNet%20HandsOn%20Part%20I.ipynb) | Recognize handwritten digits from MNIST using Keras - Part 1. |
+| [conv-net-2](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/2.2.2%20Supervised%20Learning%20-%20ConvNet%20HandsOn%20Part%20II.ipynb) | Recognize handwritten digits from MNIST using Keras - Part 2. |
+| [keras-models](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/2.3%20Supervised%20Learning%20-%20Famous%20Models%20with%20Keras.ipynb) | Use pre-trained models such as VGG16, VGG19, ResNet50, and Inception v3 with Keras. |
+| [auto-encoders](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/3.1%20Unsupervised%20Learning%20-%20AutoEncoders%20and%20Embeddings.ipynb) | Learn about Autoencoders with Keras. |
+| [rnn-lstm](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/3.2%20RNN%20and%20LSTM.ipynb) | Learn about Recurrent Neural Networks (RNNs) with Keras. |
+| [lstm-sentence-gen](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/3.3%20%28Extra%29%20LSTM%20for%20Sentence%20Generation.ipynb) |  Learn about RNNs using Long Short Term Memory (LSTM) networks with Keras. |
 
 ### deep-learning-misc
 

deep-learning/keras-tutorial/1.3 Introduction - Keras.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggiohttps://github.com/donnemartin/system-design-primer"
+    "Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio"
    ]
   },
   {

deep-learning/keras-tutorial/1.4 (Extra) A Simple Implementation of ANN for MNIST.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggiohttps://github.com/donnemartin/system-design-primer"
+    "Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio"
    ]
   },
   {

deep-learning/keras-tutorial/2.1 Supervised Learning - ConvNets.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggiohttps://github.com/donnemartin/system-design-primer"
+    "Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio"
    ]
   },
   {

deep-learning/keras-tutorial/2.2.1 Supervised Learning - ConvNet HandsOn Part I.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggiohttps://github.com/donnemartin/system-design-primer"
+    "Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio"
    ]
   },
   {

deep-learning/keras-tutorial/2.2.2 Supervised Learning - ConvNet HandsOn Part II.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggiohttps://github.com/donnemartin/system-design-primer"
+    "Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio"
    ]
   },
   {

deep-learning/keras-tutorial/2.3 Supervised Learning - Famous Models with Keras.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggiohttps://github.com/donnemartin/system-design-primer"
+    "Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio"
    ]
   },
   {

deep-learning/keras-tutorial/3.1 Unsupervised Learning - AutoEncoders and Embeddings.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggiohttps://github.com/donnemartin/system-design-primer"
+    "Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio"
    ]
   },
   {

deep-learning/keras-tutorial/3.2 RNN and LSTM.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggiohttps://github.com/donnemartin/system-design-primer"
+    "Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio"
    ]
   },
   {

deep-learning/keras-tutorial/3.3 (Extra) LSTM for Sentence Generation.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggiohttps://github.com/donnemartin/system-design-primer"
+    "Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio"
    ]
   },
   {

deep-learning/keras-tutorial/4. Conclusions.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggiohttps://github.com/donnemartin/system-design-primer"
+    "Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio"
    ]
   },
   {