Fix typos

01_intro.ipynb

@@ -1010,7 +1010,7 @@
    "source": [
     "We've changed the name of our box from *program* to *model*. This is to follow modern terminology and to reflect that the *model* is a special kind of program: it's one that can do *many different things*, depending on the *weights*. It can be implemented in many different ways. For instance, in Samuel's checkers program, different values of the weights would result in different checkers-playing strategies. \n",
     "\n",
-    "(By the way, what Samuel called \"weights\" are most generally refered to as model *parameters* these days, in case you have encountered that term. The term *weights* is reserved for a particular type of model parameter.)\n",
+    "(By the way, what Samuel called \"weights\" are most generally referred to as model *parameters* these days, in case you have encountered that term. The term *weights* is reserved for a particular type of model parameter.)\n",
     "\n",
     "Next, Samuel said we need an *automatic means of testing the effectiveness of any current weight assignment in terms of actual performance*. In the case of his checkers program, the \"actual performance\" of a model would be how well it plays. And you could automatically test the performance of two models by setting them to play against each other, and seeing which one usually wins.\n",
     "\n",

03_ethics.ipynb

@@ -135,21 +135,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Bias: Professor Lantanya Sweeney \"Arrested\""
+    "### Bias: Professor Latanya Sweeney \"Arrested\""
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Dr. Latanya Sweeney is a professor at Harvard and director of the university's data privacy lab. In the paper [\"Discrimination in Online Ad Delivery\"](https://arxiv.org/abs/1301.6822) (see <<lantanya_arrested>>) she describes her discovery that Googling her name resulted in advertisements saying \"Latanya Sweeney, arrested?\" even though she is the only known Latanya Sweeney and has never been arrested. However when she Googled other names, such as \"Kirsten Lindquist,\" she got more neutral ads, even though Kirsten Lindquist has been arrested three times."
+    "Dr. Latanya Sweeney is a professor at Harvard and director of the university's data privacy lab. In the paper [\"Discrimination in Online Ad Delivery\"](https://arxiv.org/abs/1301.6822) (see <<latanya_arrested>>) she describes her discovery that Googling her name resulted in advertisements saying \"Latanya Sweeney, arrested?\" even though she is the only known Latanya Sweeney and has never been arrested. However when she Googled other names, such as \"Kirsten Lindquist,\" she got more neutral ads, even though Kirsten Lindquist has been arrested three times."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"images/ethics/image1.png\" id=\"lantanya_arrested\" caption=\"Google search showing ads about Professor Lantanya Sweeney's arrest record\" alt=\"Screenshot of google search showing ads about Professor Lantanya Sweeney's arrest record\" width=\"400\">"
+    "<img src=\"images/ethics/image1.png\" id=\"latanya_arrested\" caption=\"Google search showing ads about Professor Latanya Sweeney's arrest record\" alt=\"Screenshot of google search showing ads about Professor Latanya Sweeney's arrest record\" width=\"400\">"
    ]
   },
   {
@@ -243,7 +243,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"images/ethics/image4.png\" id=\"congressmen\" caption=\"Congresspeople matched to criminal mugshots by Amazon software\" alt=\"Picture of the congresspeople matched to criminal mugshots by Amazon software, they are disproportionatedly people of color\" width=\"500\">"
+    "<img src=\"images/ethics/image4.png\" id=\"congressmen\" caption=\"Congresspeople matched to criminal mugshots by Amazon software\" alt=\"Picture of the congresspeople matched to criminal mugshots by Amazon software, they are disproportionately people of color\" width=\"500\">"
    ]
   },
   {
@@ -312,7 +312,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We explained in <<chapter_intro>> how an algorithm can interact with its enviromnent to create a feedback loop, making predictions that reinforce actions taken in the real world, which lead to predictions even more pronounced in the same direction. \n",
+    "We explained in <<chapter_intro>> how an algorithm can interact with its environment to create a feedback loop, making predictions that reinforce actions taken in the real world, which lead to predictions even more pronounced in the same direction. \n",
     "As an example, let's again consider YouTube's recommendation system. A couple of years ago the Google team talked about how they had introduced reinforcement learning (closely related to deep learning, but where your loss function represents a result potentially a long time after an action occurs) to improve YouTube's recommendation system. They described how they used an algorithm that made recommendations such that watch time would be optimized.\n",
     "\n",
     "However, human beings tend to be drawn to controversial content. This meant that videos about things like conspiracy theories started to get recommended more and more by the recommendation system. Furthermore, it turns out that the kinds of people that are interested in conspiracy theories are also people that watch a lot of online videos! So, they started to get drawn more and more toward YouTube. The increasing number of conspiracy theorists watching videos on YouTube resulted in the algorithm recommending more and more conspiracy theory and other extremist content, which resulted in more extremists watching videos on YouTube, and more people watching YouTube developing extremist views, which led to the algorithm recommending more extremist content... The system was spiraling out of control.\n",
@@ -826,7 +826,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The hiring process is particularly broken in tech. One study indicative of the disfunction comes from Triplebyte, a company that helps place software engineers in companies, conducting a standardized technical interview as part of this process. They have a fascinating dataset: the results of how over 300 engineers did on their exam, coupled with the results of how those engineers did during the interview process for a variety of companies. The number one finding from [Triplebyte’s research](https://triplebyte.com/blog/who-y-combinator-companies-want) is that “the types of programmers that each company looks for often have little to do with what the company needs or does. Rather, they reflect company culture and the backgrounds of the founders.”\n",
+    "The hiring process is particularly broken in tech. One study indicative of the dysfunction comes from Triplebyte, a company that helps place software engineers in companies, conducting a standardized technical interview as part of this process. They have a fascinating dataset: the results of how over 300 engineers did on their exam, coupled with the results of how those engineers did during the interview process for a variety of companies. The number one finding from [Triplebyte’s research](https://triplebyte.com/blog/who-y-combinator-companies-want) is that “the types of programmers that each company looks for often have little to do with what the company needs or does. Rather, they reflect company culture and the backgrounds of the founders.”\n",
     "\n",
     "This is a challenge for those trying to break into the world of deep learning, since most companies' deep learning groups today were founded by academics. These groups tend to look for people \"like them\"—that is, people that can solve complex math problems and understand dense jargon. They don't always know how to spot people who are actually good at solving real problems using deep learning.\n",
     "\n",

05_pet_breeds.ipynb

@@ -255,7 +255,7 @@
    "source": [
     "We need our images to have the same dimensions, so that they can collate into tensors to be passed to the GPU. We also want to minimize the number of distinct augmentation computations we perform. The performance requirement suggests that we should, where possible, compose our augmentation transforms into fewer transforms (to reduce the number of computations and the number of lossy operations) and transform the images into uniform sizes (for more efficient processing on the GPU).\n",
     "\n",
-    "The challenge is that, if performed after resizing down to the augmented size, various common data augmentation transforms might introduce spurious empty zones, degrade data, or both. For instance, rotating an image by 45 degrees fills corner regions of the new bounds with emptyness, which will not teach the model anything. Many rotation and zooming operations will require interpolating to create pixels. These interpolated pixels are derived from the original image data but are still of lower quality.\n",
+    "The challenge is that, if performed after resizing down to the augmented size, various common data augmentation transforms might introduce spurious empty zones, degrade data, or both. For instance, rotating an image by 45 degrees fills corner regions of the new bounds with emptiness, which will not teach the model anything. Many rotation and zooming operations will require interpolating to create pixels. These interpolated pixels are derived from the original image data but are still of lower quality.\n",
     "\n",
     "To work around these challenges, presizing adopts two strategies that are shown in <<presizing>>:\n",
     "\n",

06_multicat.ipynb

@@ -40,7 +40,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the previous chapter you learned some important practical techniques for training models in practice. COnsiderations like selecting learning rates and the number of epochs are very important to getting good results.\n",
+    "In the previous chapter you learned some important practical techniques for training models in practice. Considerations like selecting learning rates and the number of epochs are very important to getting good results.\n",
     "\n",
     "In this chapter we are going to look at two other types of computer vision problems: multi-label classification and regression. The first one is when you want to predict more than one label per image (or sometimes none at all), and the second is when your labels are one or several numbers—a quantity instead of a category.\n",
     "\n",

07_sizing_and_tta.ipynb

@@ -68,7 +68,7 @@
     "\n",
     "We thought that seemed very unlikely to be true. We had never actually seen a study that showed that ImageNet happen to be exactly the right size, and that other datasets could not be developed which would provide useful insights. So we thought we would try to create a new dataset that researchers could test their algorithms on quickly and cheaply, but which would also provide insights likely to work on the full ImageNet dataset.\n",
     "\n",
-    "About three hours later we had created Imagenette. We selected 10 classes from the full ImageNet that looked very different from one another. As we had hopep, we were able to quickly and cheaply create a classifier capable of recognizing these classes. We then tried out a few algorithmic tweaks to see how they impacted Imagenette. We found some that worked pretty well, and tested them on ImageNet as well—and we were very pleased to find that our tweaks worked well on ImageNet too!\n",
+    "About three hours later we had created Imagenette. We selected 10 classes from the full ImageNet that looked very different from one another. As we had hoped, we were able to quickly and cheaply create a classifier capable of recognizing these classes. We then tried out a few algorithmic tweaks to see how they impacted Imagenette. We found some that worked pretty well, and tested them on ImageNet as well—and we were very pleased to find that our tweaks worked well on ImageNet too!\n",
     "\n",
     "There is an important message here: the dataset you get given is not necessarily the dataset you want. It's particularly unlikely to be the dataset that you want to do your development and prototyping in. You should aim to have an iteration speed of no more than a couple of minutes—that is, when you come up with a new idea you want to try out, you should be able to train a model and see how it goes within a couple of minutes. If it's taking longer to do an experiment, think about how you could cut down your dataset, or simplify your model, to improve your experimentation speed. The more experiments you can do, the better!\n",
     "\n",
@@ -289,7 +289,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's check how what effet this had on training our model:"
+    "Let's check what effect this had on training our model:"
    ]
   },
   {
@@ -914,7 +914,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's practice our paper-reading skills to try to interpret this. \"This maximum\" is refering to the previous part of the paragraph, which talked about the fact that 1 is the value of the label for the positive class. So it's not possible for any value (except infinity) to result in 1 after sigmoid or softmax. In a paper, you won't normally see \"any value\" written; instead it will get a symbol, which in this case is $z_k$. This shorthand is helpful in a paper, because it can be refered to again later and the reader will know what value is being discussed.\n",
+    "Let's practice our paper-reading skills to try to interpret this. \"This maximum\" is refering to the previous part of the paragraph, which talked about the fact that 1 is the value of the label for the positive class. So it's not possible for any value (except infinity) to result in 1 after sigmoid or softmax. In a paper, you won't normally see \"any value\" written; instead it will get a symbol, which in this case is $z_k$. This shorthand is helpful in a paper, because it can be referred to again later and the reader will know what value is being discussed.\n",
     "\n",
     "Then it says \"if $z_y\\gg z_k$ for all $k\\neq y$.\" In this case, the paper immediately follows the math with an English description, which is handy because you can just read that. In the math, the $y$ is refering to the target ($y$ is defined earlier in the paper; sometimes it's hard to find where symbols are defined, but nearly all papers will define all their symbols somewhere), and $z_y$ is the activation corresponding to the target. So to get close to 1, this activation needs to be much higher than all the others for that prediction.\n",
     "\n",

08_collab.ipynb

@@ -793,7 +793,7 @@
    "source": [
     "In computer vision, we have a very easy way to get all the information of a pixel through its RGB values: each pixel in a colored image is represented by three numbers. Those three numbers give us the redness, the greenness and the blueness, which is enough to get our model to work afterward.\n",
     "\n",
-    "For the problem at hand, we don't have the same easy way to characterize a user or a movie. There are probably relations with genres: if a given user likes romance, they are likely to give higher scores to romance movies. Other factors might be wether the movie is more action-oriented versus heavy on dialogue, or the presence of a specific actor that a user might particularly like. \n",
+    "For the problem at hand, we don't have the same easy way to characterize a user or a movie. There are probably relations with genres: if a given user likes romance, they are likely to give higher scores to romance movies. Other factors might be whether the movie is more action-oriented versus heavy on dialogue, or the presence of a specific actor that a user might particularly like. \n",
     "\n",
     "How do we determine numbers to characterize those? The answer is, we don't. We will let our model *learn* them. By analyzing the existing relations between users and movies, our model can figure out itself the features that seem important or not.\n",
     "\n",
@@ -1006,7 +1006,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The first thing we can do to make this model a little bit better is to force those predictions to be between 0 and 5. For this, we just need to use `sigmoid_range`, like in <<chapter_mulitcat>>. One thing we discovered empirically is that it's better to have the range go a little bit over 5, so we use `(0, 5.5)`:"
+    "The first thing we can do to make this model a little bit better is to force those predictions to be between 0 and 5. For this, we just need to use `sigmoid_range`, like in <<chapter_multicat>>. One thing we discovered empirically is that it's better to have the range go a little bit over 5, so we use `(0, 5.5)`:"
    ]
   },
   {
@@ -1262,7 +1262,7 @@
     "loss_with_wd = loss + wd * (parameters**2).sum()\n",
     "```\n",
     "\n",
-    "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",
+    "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 school 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",
     "parameters.grad += wd * 2 * parameters\n",