diff --git a/Exercises/regression_breakout_00.ipynb b/Exercises/regression_breakout_00.ipynb
index d37ff2f..0b42fe9 100644
--- a/Exercises/regression_breakout_00.ipynb
+++ b/Exercises/regression_breakout_00.ipynb
@@ -6,17 +6,16 @@
"source": [
"## Regression: Breakout ##\n",
"\n",
- "**ML Course (Columbia, J. Bloom, 2019)**"
+ "**ML Course (Bogotá, Colombia, J. Bloom, 2019)**"
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"![](\"https://upload.wikimedia.org/wikipedia/commons/2/20/Columbia-Aquidneck-2011.jpg\")
\n",
"\n",
- "A yacht named \"Columbia\" was used to win the [America's Cup in 1958](https://en.wikipedia.org/wiki/1958_America%27s_Cup). Technology has improved a lot since then. In this breakout, you're going making a predictive model to determine the resistance of a boat given it's geometry and speed. This is usually only measureable with advanced simulations but we can get 7-dimensional data to build a model and then determine this value for arbitrary new boat design:\n",
+ "A yacht named \"Columbia\" (not Colombia 😏) was used to win the [America's Cup in 1958](https://en.wikipedia.org/wiki/1958_America%27s_Cup). Technology has improved a lot since then. In this breakout, you're going making a predictive model to determine the resistance of a boat given it's geometry and speed. This is usually only measureable with advanced simulations but we can get 7-dimensional data to build a model and then determine this value for arbitrary new boat design:\n",
"\n",
"\n",
"- *Prediction of residuary resistance of sailing yachts at the initial design stage is of a great value for evaluating the ship's performance and for estimating the required propulsive power. Essential inputs include the basic hull dimensions and the boat velocity. The Delft data set comprises 308 full-scale experiments, which were performed at the Delft Ship Hydromechanics Laboratory for that purpose. These experiments include 22 different hull forms...*\n",
@@ -43,7 +42,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -55,7 +54,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -76,7 +75,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -103,22 +102,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
- " metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
- " weights='uniform')"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"from sklearn import neighbors\n",
"\n",
@@ -134,7 +120,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -152,20 +138,9 @@
},
{
"cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "124.31701740865698"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"mean_squared_error(test_Y,Y.mean()*np.ones(test_Y.shape))"
]
@@ -179,7 +154,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -189,7 +164,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
diff --git a/Lectures/4_NeuralNetworksIntroduction/00_neural_networks_introduction.ipynb b/Lectures/4_NeuralNetworksIntroduction/00_neural_networks_introduction.ipynb
index a6fb7c9..e27c2af 100644
--- a/Lectures/4_NeuralNetworksIntroduction/00_neural_networks_introduction.ipynb
+++ b/Lectures/4_NeuralNetworksIntroduction/00_neural_networks_introduction.ipynb
@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 1,
"metadata": {},
"outputs": [
{
diff --git a/Lectures/4_NeuralNetworksIntroduction/tensorboard.ipynb b/Lectures/4_NeuralNetworksIntroduction/tensorboard.ipynb
new file mode 100644
index 0000000..fe3ac50
--- /dev/null
+++ b/Lectures/4_NeuralNetworksIntroduction/tensorboard.ipynb
@@ -0,0 +1,131 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Using tensorboard inside of Jupyter\n",
+ "\n",
+ "see https://www.tensorflow.org/tensorboard/r2/tensorboard_in_notebooks"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Collecting tf-nightly-2.0-preview\n",
+ " Using cached https://files.pythonhosted.org/packages/b2/21/f31e83814a6f37c96a2d11f63cd63bbc2a1099cde12e703775ce92cc4572/tf_nightly_2.0_preview-2.0.0.dev20190520-cp36-cp36m-macosx_10_9_x86_64.whl\n",
+ "Requirement already satisfied: numpy<2.0,>=1.14.5 in /Users/jbloom/anaconda3/lib/python3.6/site-packages (from tf-nightly-2.0-preview) (1.16.2)\n",
+ "Collecting wrapt>=1.11.1 (from tf-nightly-2.0-preview)\n",
+ "Requirement already satisfied: google-pasta>=0.1.6 in /Users/jbloom/anaconda3/lib/python3.6/site-packages (from tf-nightly-2.0-preview) (0.1.6)\n",
+ "Requirement already satisfied: gast>=0.2.0 in /Users/jbloom/anaconda3/lib/python3.6/site-packages (from tf-nightly-2.0-preview) (0.2.0)\n",
+ "Requirement already satisfied: keras-preprocessing>=1.0.5 in /Users/jbloom/anaconda3/lib/python3.6/site-packages (from tf-nightly-2.0-preview) (1.0.5)\n",
+ "Collecting tb-nightly<1.15.0a0,>=1.14.0a0 (from tf-nightly-2.0-preview)\n",
+ " Using cached https://files.pythonhosted.org/packages/6f/99/4220b50dc87814988e969cc859c07d070423bea820bc24d16c2023057eb6/tb_nightly-1.14.0a20190520-py3-none-any.whl\n",
+ "Requirement already satisfied: absl-py>=0.7.0 in /Users/jbloom/anaconda3/lib/python3.6/site-packages (from tf-nightly-2.0-preview) (0.7.1)\n",
+ "Requirement already satisfied: keras-applications>=1.0.6 in /Users/jbloom/anaconda3/lib/python3.6/site-packages (from tf-nightly-2.0-preview) (1.0.6)\n",
+ "Requirement already satisfied: grpcio>=1.8.6 in /Users/jbloom/anaconda3/lib/python3.6/site-packages (from tf-nightly-2.0-preview) (1.12.1)\n",
+ "Requirement already satisfied: protobuf>=3.6.1 in /Users/jbloom/anaconda3/lib/python3.6/site-packages (from tf-nightly-2.0-preview) (3.7.1)\n",
+ "Requirement already satisfied: wheel>=0.26 in /Users/jbloom/anaconda3/lib/python3.6/site-packages (from tf-nightly-2.0-preview) (0.30.0)\n",
+ "Requirement already satisfied: six>=1.10.0 in /Users/jbloom/anaconda3/lib/python3.6/site-packages (from tf-nightly-2.0-preview) (1.11.0)\n",
+ "Requirement already satisfied: termcolor>=1.1.0 in /Users/jbloom/anaconda3/lib/python3.6/site-packages (from tf-nightly-2.0-preview) (1.1.0)\n",
+ "Requirement already satisfied: tensorflow-estimator-2.0-preview in /Users/jbloom/anaconda3/lib/python3.6/site-packages (from tf-nightly-2.0-preview) (1.14.0.dev2019052000)\n",
+ "Requirement already satisfied: astor>=0.6.0 in /Users/jbloom/anaconda3/lib/python3.6/site-packages (from tf-nightly-2.0-preview) (0.7.1)\n",
+ "Requirement already satisfied: markdown>=2.6.8 in /Users/jbloom/anaconda3/lib/python3.6/site-packages (from tb-nightly<1.15.0a0,>=1.14.0a0->tf-nightly-2.0-preview) (2.6.11)\n",
+ "Requirement already satisfied: werkzeug>=0.11.15 in /Users/jbloom/anaconda3/lib/python3.6/site-packages (from tb-nightly<1.15.0a0,>=1.14.0a0->tf-nightly-2.0-preview) (0.14.1)\n",
+ "Requirement already satisfied: h5py in /Users/jbloom/anaconda3/lib/python3.6/site-packages (from keras-applications>=1.0.6->tf-nightly-2.0-preview) (2.7.1)\n",
+ "Requirement already satisfied: setuptools in /Users/jbloom/anaconda3/lib/python3.6/site-packages (from protobuf>=3.6.1->tf-nightly-2.0-preview) (39.1.0)\n",
+ "Installing collected packages: wrapt, tb-nightly, tf-nightly-2.0-preview\n",
+ "Successfully installed tb-nightly-1.14.0a20190520 tf-nightly-2.0-preview-2.0.0.dev20190520 wrapt-1.11.1\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install tf-nightly-2.0-preview"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load the TensorBoard notebook extension ... you may need to restart your kernel\n",
+ "%load_ext tensorboard"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import tensorflow as tf\n",
+ "import datetime, os"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%tensorboard --logdir nn_results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}