VTYPOS: various

notebooks/02.02-The-Basics-Of-NumPy-Arrays.ipynb

@@ -56,7 +56,7 @@
    "metadata": {},
    "source": [
     "First let's discuss some useful array attributes.\n",
-    "We'll start by defining three random arrays, a one-dimensional, tw-dimensional, and three-dimensional array.\n",
+    "We'll start by defining three random arrays, a one-dimensional, two-dimensional, and three-dimensional array.\n",
     "We'll use NumPy's random number generator, which we will *seed* with a set value in order to ensure that the same random arrays are generated each time this code is run:"
    ]
   },

notebooks/02.03-Computation-on-arrays-ufuncs.ipynb

@@ -534,7 +534,7 @@
    "source": [
     "### Trigonometric functions\n",
     "\n",
-    "NumPy provides a large number of useful ufuncs, and some of the most useful for the data scientist are the trigonometric functions\n",
+    "NumPy provides a large number of useful ufuncs, and some of the most useful for the data scientist are the trigonometric functions.\n",
     "We'll start by defining an array of angles:"
    ]
   },

notebooks/02.04-Computation-on-arrays-aggregates.ipynb

@@ -494,7 +494,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "(Note that the heights are measured in centimeters).\n",
     "Now that we have this data array, we can compute a variety of summary statistics:"
    ]
   },

notebooks/05.10-Manifold-Learning.ipynb

@@ -650,7 +650,7 @@
     "## Example: Isomap on Faces\n",
     "\n",
     "One place manifold learning is often used is in understanding the relationship between high-dimensional data points.\n",
-    "A common case of high-dimensional data is images: for example, a set of images with 1,000 pixels each can be thought of as collection of points in 1,000 dimensions – the brightness of each pixel in each image defines the coordinate in that dimension.\n",
+    "A common case of high-dimensional data is images: for example, a set of images with 1,000 pixels each can be thought of as a collection of points in 1,000 dimensions – the brightness of each pixel in each image defines the coordinate in that dimension.\n",
     "\n",
     "Here let's apply Isomap on some faces data.\n",
     "We will use the Labeled Faces in the Wild dataset, which we previously saw in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) and [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb).\n",

notebooks/05.11-K-Means.ipynb

@@ -361,7 +361,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### k Means is limited to linear cluster boundaries\n",
+    "#### k-means is limited to linear cluster boundaries\n",
     "The fundamental model assumptions of *k*-means (points will be closer to their own cluster center than to others) means that the algorithm will often be ineffective if the clusters have complicated geometries.\n",
     "\n",
     "In particular, the boundaries between *k*-means clusters will always be linear, which means that it will fail for more complicated boundaries.\n",
@@ -453,7 +453,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### k means can be slow for large numbers of samples\n",
+    "#### k-means can be slow for large numbers of samples\n",
     "Because each iteration of *k*-means must access every point in the dataset, the algorithm can be relatively slow as the number of samples grows.\n",
     "You might wonder if this requirement to use all data at each iteration can be relaxed; for example, you might just use a subset of the data to update the cluster centers at each step.\n",
     "This is the idea behind batch-based *k*-means algorithms, one form of which is implemented in ``sklearn.cluster.MiniBatchKMeans``.\n",

notebooks/05.13-Kernel-Density-Estimation.ipynb

@@ -625,7 +625,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Compared to the simple scatter plot we initially used, this visualization paints a much clearer of the geographical distribution of observations of these two species."
+    "Compared to the simple scatter plot we initially used, this visualization paints a much clearer picture of the geographical distribution of observations of these two species."
    ]
   },
   {

notebooks/05.14-Image-Features.ipynb

@@ -116,7 +116,7 @@
    "source": [
     "## HOG in Action: A Simple Face Detector\n",
     "\n",
-    "Using these HOG features, we can build up a simple facial detection algorithm with any Scikit-Learn estimator; here we will use a linear support vector machine (refer back to [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) if you need a refresher on this)\n",
+    "Using these HOG features, we can build up a simple facial detection algorithm with any Scikit-Learn estimator; here we will use a linear support vector machine (refer back to [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) if you need a refresher on this).\n",
     "The steps are as follows:\n",
     "\n",
     "1. Obtain a set of image thumbnails of faces to constitute \"positive\" training samples.\n",
@@ -652,7 +652,7 @@
     "\n",
     "Finally, I should add that HOG and other procedural feature extraction methods for images are no longer state-of-the-art techniques.\n",
     "Instead, many modern object detection pipelines use variants of deep neural networks: one way to think of neural networks is that they are an estimator which determines optimal feature extraction strategies from the data, rather than relying on the intuition of the user.\n",
-    "An intro to these deep neural net methods is conceptually (and computationlly!) beyond the scope of this section, although open tools like Google's [TensorFlow](https://www.tensorflow.org/) have recently made deep learning approaches much more accessible than they once were.\n",
+    "An intro to these deep neural net methods is conceptually (and computationally!) beyond the scope of this section, although open tools like Google's [TensorFlow](https://www.tensorflow.org/) have recently made deep learning approaches much more accessible than they once were.\n",
     "As of the writing of this book, deep learning in Python is still relatively young, and so I can't yet point to any definitive resource.\n",
     "That said, the list of references in the following section should provide a useful place to start!"
    ]