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updates to support downloading mnist data in autoencoder section
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7 files changed

+100
-20
lines changed

7 files changed

+100
-20
lines changed

autoencoder/convolutional-autoencoder/Convolutional_Autoencoder_Exercise.ipynb

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@@ -21,6 +21,18 @@
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"Let's get started by importing our libraries and getting the dataset."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from six.moves import urllib\n",
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"opener = urllib.request.build_opener()\n",
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"opener.addheaders = [('User-agent', 'Mozilla/5.0')]\n",
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"urllib.request.install_opener(opener)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [default]",
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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"version": "3.7.1"
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}
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},
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"nbformat": 4,

autoencoder/convolutional-autoencoder/Convolutional_Autoencoder_Solution.ipynb

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@@ -21,6 +21,18 @@
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"Let's get started by importing our libraries and getting the dataset."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from six.moves import urllib\n",
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"opener = urllib.request.build_opener()\n",
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"opener.addheaders = [('User-agent', 'Mozilla/5.0')]\n",
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"urllib.request.install_opener(opener)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [default]",
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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"version": "3.7.1"
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}
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},
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"nbformat": 4,

autoencoder/convolutional-autoencoder/Upsampling_Solution.ipynb

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"Let's get started by importing our libraries and getting the dataset."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from six.moves import urllib\n",
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"opener = urllib.request.build_opener()\n",
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"opener.addheaders = [('User-agent', 'Mozilla/5.0')]\n",
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"urllib.request.install_opener(opener)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [default]",
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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"version": "3.7.1"
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}
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},
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"nbformat": 4,

autoencoder/denoising-autoencoder/Denoising_Autoencoder_Exercise.ipynb

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"Let's get started by importing our libraries and getting the dataset."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from six.moves import urllib\n",
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"opener = urllib.request.build_opener()\n",
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"opener.addheaders = [('User-agent', 'Mozilla/5.0')]\n",
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"urllib.request.install_opener(opener)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [default]",
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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"version": "3.7.1"
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}
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},
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"nbformat": 4,

autoencoder/denoising-autoencoder/Denoising_Autoencoder_Solution.ipynb

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"Let's get started by importing our libraries and getting the dataset."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from six.moves import urllib\n",
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"opener = urllib.request.build_opener()\n",
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"opener.addheaders = [('User-agent', 'Mozilla/5.0')]\n",
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"urllib.request.install_opener(opener)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [default]",
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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"version": "3.7.1"
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}
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},
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"nbformat": 4,

autoencoder/linear-autoencoder/Simple_Autoencoder_Exercise.ipynb

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"In this notebook, we'll be build a simple network architecture for the encoder and decoder. Let's get started by importing our libraries and getting the dataset."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from six.moves import urllib\n",
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"opener = urllib.request.build_opener()\n",
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"opener.addheaders = [('User-agent', 'Mozilla/5.0')]\n",
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"urllib.request.install_opener(opener)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"source": [
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"## Up Next\n",
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"\n",
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [default]",
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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"version": "3.7.1"
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}
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},
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"nbformat": 4,

autoencoder/linear-autoencoder/Simple_Autoencoder_Solution.ipynb

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"In this notebook, we'll be build a simple network architecture for the encoder and decoder. Let's get started by importing our libraries and getting the dataset."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from six.moves import urllib\n",
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"opener = urllib.request.build_opener()\n",
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"opener.addheaders = [('User-agent', 'Mozilla/5.0')]\n",
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"urllib.request.install_opener(opener)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"source": [
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"## Up Next\n",
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"\n",
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [default]",
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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"version": "3.7.1"
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}
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},
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"nbformat": 4,

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