more ch2 typos

02_production.ipynb

@@ -139,7 +139,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Just like in computer vision, computers are very good at categorising both short and long documents based on categories such as spam, sentiment (e.g. is the review positive or negative), author, source website, and so forth. We are not aware of any rigourous work done in this area to compare to human performance, but anecdotally it seems to us that deep learning performance is similar to human performance here. Deep learning is also very good at generating context-appropriate text, such as generating replies to social media posts, and imitating a particular author's style. It is also good at making this content compelling to humans, and has been shown to be even more compelling than human-generated text. However, deep learning is currently not good at generating *correct* responses! We don't currently have a reliable way to, for instance, combine a knowledge base of medical information, along with a deep learning model for generating medically correct natural language responses. This is very dangerous, because it is so easy to create content which appears to a layman to be compelling, but actually is entirely incorrect.\n",
+    "Just like in computer vision, computers are very good at categorising both short and long documents based on categories such as spam, sentiment (e.g. is the review positive or negative), author, source website, and so forth. We are not aware of any rigorous work done in this area to compare to human performance, but anecdotally it seems to us that deep learning performance is similar to human performance here. Deep learning is also very good at generating context-appropriate text, such as generating replies to social media posts, and imitating a particular author's style. It is also good at making this content compelling to humans, and has been shown to be even more compelling than human-generated text. However, deep learning is currently not good at generating *correct* responses! We don't currently have a reliable way to, for instance, combine a knowledge base of medical information, along with a deep learning model for generating medically correct natural language responses. This is very dangerous, because it is so easy to create content which appears to a layman to be compelling, but actually is entirely incorrect.\n",
     "\n",
     "Another concern is that context-appropriate, highly compelling responses on social media can be used at massive scale — thousands of times greater than any troll farm previously seen — to spread disinformation, create unrest, and encourage conflict. As a rule of thumb, text generation will always be technologically a bit ahead of the ability of models to recognize automatically generated text. For instance, it is possible to use a model that can recognize artificially generated content to actually improve the generator that creates that content, until the classification model is no longer able to complete its task.\n",
     "\n",
@@ -274,7 +274,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> important: Services that can be used for creating datasets come and go all the time, and their features, interfaces, and pricing change regularly too. In this section, we'll show how to use one particular provider, _Bing Image Search_, using the service they have as this book as written. We'll be providing more options and more up to date information on the http://book.fast.ai[book website], so be sure to have a look there now to get the most current information on how to download images from the web to create a dataset for deep learning."
+    "> important: Services that can be used for creating datasets come and go all the time, and their features, interfaces, and pricing change regularly too. In this section, we'll show how to use one particular provider, _Bing Image Search_, using the service they have as this book was written. We'll be providing more options and more up to date information on the [book website](http://book.fast.ai), so be sure to have a look there now to get the most current information on how to download images from the web to create a dataset for deep learning."
    ]
   },
   {
@@ -804,7 +804,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "All of these approaches seem somewhat wasteful, or problematic. If we squished or stretch the images then they end up unrealistic shapes, leading to a model that learns that things look different to how they actually are, which we would expect to result in lower accuracy. If we crop the images then we remove some of the features that allow us to recognize them. For instance, if we were trying to recognise the breed of dog or cat, we may end up cropping out a key part of the body or the face necessary to distinguish between similar breeds. If we pad the images then we have a whole lot of empty space, which is just wasted computation for our model, and results in a lower effective resolution for the part of the image we actually use.\n",
+    "All of these approaches seem somewhat wasteful, or problematic. If we squished or stretched the images then they end up as unrealistic shapes, leading to a model that learns that things look different to how they actually are, which we would expect to result in lower accuracy. If we crop the images then we remove some of the features that allow us to recognize them. For instance, if we were trying to recognise the breed of dog or cat, we may end up cropping out a key part of the body or the face necessary to distinguish between similar breeds. If we pad the images then we have a whole lot of empty space, which is just wasted computation for our model, and results in a lower effective resolution for the part of the image we actually use.\n",
     "\n",
     "Instead, what we normally do in practice is to randomly select part of the image, and crop to just that part. On each epoch (which is one complete pass through all of our images in the dataset) we randomly select a different part of each image. This means that our model can learn to focus on, and recognize, different features in our images. It also reflects how images work in the real world; different photos of the same thing may be framed in slightly different ways.\n",
     "\n",
@@ -855,7 +855,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Data augmentation refers to creating random variations of our input data, such that they appear different, but are not expected to change the meaning of the data. Examples of common data augmentation for images are rotation, flipping, perspective warping, brightness changes, contrast changes, and much more. For natural photo images such as the ones we are using here, there is a standard set of augmentations which we have found work pretty well, and are provided with the `aug_transforms` function. Because the images are now all the same size, we can apply these augmentations to an entire batch of them using the GPU, which will save a lot of time. To tell fastai we want to use these transforms to a batch, we use the `batch_tfms` parameter. (Note that we're not using `RandomResizedCrop` in this example, so you can see the differences more clearly; we're also using double the amount of augmentation compared to the default, for the same reason)."
+    "Data augmentation refers to creating random variations of our input data, such that they appear different, but are not expected to change the meaning of the data. Examples of common data augmentation for images are rotation, flipping, perspective warping, brightness changes, contrast changes, and much more. For natural photo images such as the ones we are using here, there is a standard set of augmentations which we have found work pretty well, and are provided with the `aug_transforms` function. Because the images are now all the same size, we can apply these augmentations to an entire batch of them using the GPU, which will save a lot of time. To tell fastai we want to use these transforms on a batch, we use the `batch_tfms` parameter. (Note that we're not using `RandomResizedCrop` in this example, so you can see the differences more clearly; we're also using double the amount of augmentation compared to the default, for the same reason)."
    ]
   },
   {
@@ -1968,7 +1968,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,