First batch of edits

DataScienceandAIVE · May 14, 2020 · 7abc2c3 · 7abc2c3
1 parent 5b70a64
commit 7abc2c3
Show file tree

Hide file tree

Showing 42 changed files with 2,010 additions and 2,415 deletions.
diff --git a/01_intro.ipynb b/01_intro.ipynb
diff --git a/02_production.ipynb b/02_production.ipynb
diff --git a/03_ethics.ipynb b/03_ethics.ipynb
diff --git a/04_mnist_basics.ipynb b/04_mnist_basics.ipynb
diff --git a/05_pet_breeds.ipynb b/05_pet_breeds.ipynb
diff --git a/06_multicat.ipynb b/06_multicat.ipynb
diff --git a/07_sizing_and_tta.ipynb b/07_sizing_and_tta.ipynb
diff --git a/08_collab.ipynb b/08_collab.ipynb
@@ -21,7 +21,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Collaborative filtering deep dive"
+    "# Collaborative Filtering Deep Dive"
    ]
   },
   {
@@ -48,7 +48,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## A first look at the data"
+    "## A First Look at the Data"
    ]
   },
   {
@@ -318,7 +318,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Learning the latent factors"
+    "## Learning the Latent Factors"
    ]
   },
   {
@@ -816,7 +816,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Collaborative filtering from scratch"
+    "## Collaborative Filtering from Scratch"
    ]
   },
   {
@@ -1214,7 +1214,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Weight decay"
+    "### Weight Decay"
    ]
   },
   {
@@ -1272,7 +1272,7 @@
     "In practice though, it would be very inefficient (and maybe numerically unstable) to compute that big sum and add it to the loss. If you remember a little bit of high schoool math, you might recall that the derivative of `p**2` with respect to `p` is `2*p`, so adding that big sum to our loss is exactly the same as doing:\n",
     "\n",
     "``` python\n",
-    "weight.grad += wd * 2 * weight\n",
+    "parameters.grad += wd * 2 * parameters\n",
     "```\n",
     "\n",
     "In practice, since `wd` is a parameter that we choose, we can just make it twice as big, so we don't even need the `*2` in the above equation. To use weight decay in fastai, just pass `wd` in your call to fit:"
@@ -1354,7 +1354,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Creating our own Embedding module"
+    "### Creating Our Own Embedding Module"
    ]
   },
   {
@@ -1601,7 +1601,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Interpreting embeddings and biases"
+    "## Interpreting Embeddings and Biases"
    ]
   },
   {
@@ -1903,7 +1903,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Embedding distance"
+    "### Embedding Distance"
    ]
   },
   {
@@ -1950,7 +1950,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Boot strapping a collaborative filtering model"
+    "## Boot Strapping a Collaborative Filtering Model"
    ]
   },
   {
@@ -1986,7 +1986,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Deep learning for collaborative filtering"
+    "## Deep Learning for Collaborative Filtering"
    ]
   },
   {
@@ -2238,7 +2238,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Sidebar: kwargs and delegates"
+    "### Sidebar: Kwargs and Delegates"
    ]
   },
   {
@@ -2330,7 +2330,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Further research\n",
+    "### Further Research\n",
     "\n",
     "1. Take a look at all the differences between the `Embedding` version of `DotProductBias` and the `create_params` version, and try to understand why each of those changes is required. If you're not sure, try reverting each change, to see what happens. (NB: even the type of brackets used in `forward` has changed!)\n",
     "1. Find three other areas where collaborative filtering is being used, and find out what pros and cons of this approach in those areas.\n",

diff --git a/09_tabular.ipynb b/09_tabular.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "hide_input": true
+    "hide_input": false
    },
    "outputs": [
     {
@@ -41,7 +41,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Tabular modelling deep dive"
+    "# Tabular Modeling Deep Dive"
    ]
   },
   {
@@ -57,7 +57,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Categorical embeddings"
+    "## Categorical Embeddings"
    ]
   },
   {
@@ -187,7 +187,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Beyond deep learning"
+    "## Beyond Deep Learning"
    ]
   },
   {
@@ -239,7 +239,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## The dataset"
+    "## The Dataset"
    ]
   },
   {
@@ -390,7 +390,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Look at the data"
+    "### Look at the Data"
    ]
   },
   {
@@ -542,7 +542,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Decision trees"
+    "## Decision Trees"
    ]
   },
   {
@@ -591,7 +591,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Handling dates"
+    "### Handling Dates"
    ]
   },
   {
@@ -1405,7 +1405,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Creating the decision tree"
+    "### Creating the Decision Tree"
    ]
   },
   {
@@ -7418,7 +7418,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Categorical variables"
+    "### Categorical Variables"
    ]
   },
   {
@@ -7454,7 +7454,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Random forests"
+    "## Random Forests"
    ]
   },
   {
@@ -7493,7 +7493,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Creating a random forest"
+    "### Creating a Random Forest"
    ]
   },
   {
@@ -7662,7 +7662,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Out-of-bag error"
+    "### Out-of-Bag Error"
    ]
   },
   {
@@ -7721,7 +7721,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Model interpretation"
+    "## Model Interpretation"
    ]
   },
   {
@@ -7743,7 +7743,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Tree variance for prediction confidence"
+    "### Tree Variance for Prediction Confidence"
    ]
   },
   {
@@ -7840,7 +7840,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Feature importance"
+    "### Feature Importance"
    ]
   },
   {
@@ -8020,7 +8020,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Removing low-importance variables"
+    "### Removing Low-Importance Variables"
    ]
   },
   {
@@ -8173,7 +8173,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Removing redundant features"
+    "### Removing Redundant Features"
    ]
   },
   {
@@ -8396,14 +8396,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Tk add transition"
+    "Now that we know which variable influence the most our predictions, we can have a look at how they affect the results using partial dependence plots."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Partial dependence"
+    "### Partial Dependence"
    ]
   },
   {
@@ -8526,7 +8526,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Data leakage"
+    "### Data Leakage"
    ]
   },
   {
@@ -8570,7 +8570,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Tree interpreter"
+    "### Tree Interpreter"
    ]
   },
   {
@@ -8716,7 +8716,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Extrapolation and neural networks"
+    "## Extrapolation and Neural Networks"
    ]
   },
   {
@@ -8730,7 +8730,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### The extrapolation problem"
+    "### The Extrapolation Problem"
    ]
   },
   {
@@ -8890,7 +8890,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Finding out of domain data"
+    "### Finding out of Domain Data"
    ]
   },
   {
@@ -9139,7 +9139,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Using a neural network"
+    "### Using a Neural Network"
    ]
   },
   {
@@ -9560,7 +9560,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Sidebar: fastai's Tabular classes"
+    "### Sidebar: fastai's Tabular Classes"
    ]
   },
   {
@@ -9700,7 +9700,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Combining embeddings with other methods"
+    "### Combining Embeddings with Other Methods"
    ]
   },
   {
@@ -9732,7 +9732,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Conclusion: our advice for tabular modeling"
+    "## Conclusion: Our Advice for Tabular Modeling"
    ]
   },
   {
@@ -9802,7 +9802,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Further research"
+    "### Further Research"
    ]
   },
   {