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@ -102,13 +102,13 @@
"\n", "\n",
"Here's a list of some of the thousands of tasks that deep learning (or methods heavily using deep learning) is now the best in the world at:\n", "Here's a list of some of the thousands of tasks that deep learning (or methods heavily using deep learning) is now the best in the world at:\n",
"\n", "\n",
"- NLP:: answering questions; speech recognition; summarizing documents; classifying documents; finding names, dates, etc in documents; searching for articles mentioning a concept\n", "- NLP:: answering questions; speech recognition; summarizing documents; classifying documents; finding names, dates, etc. in documents; searching for articles mentioning a concept\n",
"- Computer vision:: satellite and drone imagery interpretation (e.g. for disaster resilience); face recognition; image captioning; reading traffic signs; locating pedestrians and vehicles in autonomous vehicles\n", "- Computer vision:: satellite and drone imagery interpretation (e.g. for disaster resilience); face recognition; image captioning; reading traffic signs; locating pedestrians and vehicles in autonomous vehicles\n",
"- Medicine:: Finding anomalies in radiology images, including CT, MRI, and x-ray; counting features in pathology slides; measuring features in ultrasounds; diagnosing diabetic retinopathy\n", "- Medicine:: Finding anomalies in radiology images, including CT, MRI, and x-ray; counting features in pathology slides; measuring features in ultrasounds; diagnosing diabetic retinopathy\n",
"- Biology:: folding proteins; classifying proteins; many genomics tasks, such as tumor-normal sequencing and classifying clinically actionable genetic mutations; cell classification; analyzing protein/protein interactions\n", "- Biology:: folding proteins; classifying proteins; many genomics tasks, such as tumor-normal sequencing and classifying clinically actionable genetic mutations; cell classification; analyzing protein/protein interactions\n",
"- Image generation:: Colorizing images; increasing image resolution; removing noise from images; Converting images to art in the style of famous artists\n", "- Image generation:: Colorizing images; increasing image resolution; removing noise from images; converting images to art in the style of famous artists\n",
"- Recommendation systems:: web search; product recommendations; home page layout\n", "- Recommendation systems:: web search; product recommendations; home page layout\n",
"- Playing games (better than humans and better than any other computer algorithm): Chess, Go, Most Atari videogames, many real-time strategy games\n", "- Playing games (better than humans and better than any other computer algorithm): Chess, Go, most Atari videogames, many real-time strategy games\n",
"- Robotics:: handling objects that are challenging to locate (e.g. transparent, shiny, lack of texture) or hard to pick up\n", "- Robotics:: handling objects that are challenging to locate (e.g. transparent, shiny, lack of texture) or hard to pick up\n",
"- Other applications:: financial and logistical forecasting; text to speech; much much more..." "- Other applications:: financial and logistical forecasting; text to speech; much much more..."
] ]
@ -140,7 +140,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"The realised that a simplified model of a real neuron could be represented using simple addition and thresholdingas shown in <<neuron>>. Pitts was self-taught, and, by age 12, had received an offer to study at Cambridge with the great Bertrand Russell. He did not take up this invitation, and indeed throughout his life did not accept any offers of advanced degrees or positions of authority. Most of his famous work was done whilst he was homeless. Despite his lack of an officially recognized position, and increasing social isolation, his work with McCulloch was influential, and was picked up by a psychologist named Frank Rosenblatt." "They realised that a simplified model of a real neuron could be represented using simple addition and thresholding as shown in <<neuron>>. Pitts was self-taught, and, by age 12, had received an offer to study at Cambridge with the great Bertrand Russell. He did not take up this invitation, and indeed throughout his life did not accept any offers of advanced degrees or positions of authority. Most of his famous work was done whilst he was homeless. Despite his lack of an officially recognized position, and increasing social isolation, his work with McCulloch was influential, and was picked up by a psychologist named Frank Rosenblatt."
] ]
}, },
{ {
@ -156,7 +156,7 @@
"source": [ "source": [
"Rosenblatt further developed the artificial neuron to give it the ability to learn. Even more importantly, he worked on building the first device that actually used these principles: The Mark I Perceptron. Rosenblatt wrote about this work: \"we are about to witness the birth of such a machine – a machine capable of perceiving, recognizing and identifying its surroundings without any human training or control\". The perceptron was built, and was able to successfully recognize simple shapes.\n", "Rosenblatt further developed the artificial neuron to give it the ability to learn. Even more importantly, he worked on building the first device that actually used these principles: The Mark I Perceptron. Rosenblatt wrote about this work: \"we are about to witness the birth of such a machine – a machine capable of perceiving, recognizing and identifying its surroundings without any human training or control\". The perceptron was built, and was able to successfully recognize simple shapes.\n",
"\n", "\n",
"An MIT professor named Marvin Minsky (who was a grade behind Rosenblatt the same high school!) along with Seymour Papert wrote a book, called \"Perceptrons\", about Rosenblatt's invention. They showed that a single layer of these devices was unable to learn some simple, critical mathematical functions (such as XOR). In the same book, they also showed that using multiple layers of the devices would allow these limitations to be addressed. Unfortunately, only the first of these insights was widely recognized, as a result of which the global academic community nearly entirely gave up on neural networks for the next two decades." "An MIT professor named Marvin Minsky (who was a grade behind Rosenblatt at the same high school!) along with Seymour Papert wrote a book, called \"Perceptrons\", about Rosenblatt's invention. They showed that a single layer of these devices was unable to learn some simple, critical mathematical functions (such as XOR). In the same book, they also showed that using multiple layers of the devices would allow these limitations to be addressed. Unfortunately, only the first of these insights was widely recognized, as a result of which the global academic community nearly entirely gave up on neural networks for the next two decades."
] ]
}, },
{ {
@ -165,7 +165,7 @@
"source": [ "source": [
"Perhaps the most pivotal work in neural networks in the last 50 years is the multi-volume *Parallel Distributed Processing*, released in 1986 by MIT Press. Chapter 1 lays out a similar hope to that shown by Rosenblatt:\n", "Perhaps the most pivotal work in neural networks in the last 50 years is the multi-volume *Parallel Distributed Processing*, released in 1986 by MIT Press. Chapter 1 lays out a similar hope to that shown by Rosenblatt:\n",
"\n", "\n",
"> : _…people are smarter than today s computers because the brain employs a basic computational architecture that is more suited to deal with a central aspect of the natural information processing tasks that people are so good at. …we will introduce a computational framework for modeling cognitive processes that seems… closer than other frameworks to the style of computation as it might be done by the brain._ (Parallel distributed processing, chapter 1)\n", "> : _…people are smarter than today's computers because the brain employs a basic computational architecture that is more suited to deal with a central aspect of the natural information processing tasks that people are so good at. …we will introduce a computational framework for modeling cognitive processes that seems… closer than other frameworks to the style of computation as it might be done by the brain._ (Parallel distributed processing, chapter 1)\n",
"\n", "\n",
"TK Melissa: Tell the reader what the takeaways from this are in your own words, before you dive into the list of requirements.\n", "TK Melissa: Tell the reader what the takeaways from this are in your own words, before you dive into the list of requirements.\n",
"\n", "\n",
@ -180,9 +180,9 @@
"1. A *learning rule* whereby patterns of connectivity are modified by experience \n", "1. A *learning rule* whereby patterns of connectivity are modified by experience \n",
"1. An *environment* within which the system must operate\n", "1. An *environment* within which the system must operate\n",
"\n", "\n",
"We will learn in this book about how modern neural networks handle each of these requirement. In the 1980's most models were built with a second layer of neurons, thus avoiding the problem that had been identified by Minsky (this was their \"pattern of connectivity among units\", to use the framework above). And indeed, neural networks were widely used during the 80s and 90s for real, practical projects. However, again a misunderstanding of the theoretical issues held back the field. In theory, adding just one extra layer of neurons was enough to allow any mathematical model to be approximated with these neural networks, but in practice such networks were often too big and slow to be useful.\n", "We will learn in this book about how modern neural networks handle each of these requirements. In the 1980's most models were built with a second layer of neurons, thus avoiding the problem that had been identified by Minsky (this was their \"pattern of connectivity among units\", to use the framework above). And indeed, neural networks were widely used during the 80s and 90s for real, practical projects. However, again a misunderstanding of the theoretical issues held back the field. In theory, adding just one extra layer of neurons was enough to allow any mathematical model to be approximated with these neural networks, but in practice such networks were often too big and slow to be useful.\n",
"\n", "\n",
"Although there were researchers 30 years ago showing that to get good performance in practice you need to use even more layers of neurons, it is only in the last decade that this has been more widely appreciated. Thanks to this understanding, along with the improved ability to use these in practice thanks to improvements in computer hardware, increases in data availability, and algorithmic tweaks that allow neural networks to be trained faster and more easily, neural networks are now finally living out their potential. We now have what Rosenblatt had promised: \"a machine capable of perceiving, recognizing and identifying its surroundings without any human training or control\". And you will learn how to build them in this book." "Although there were researchers 30 years ago showing that to get good performance in practice you need to use even more layers of neurons, it is only in the last decade that this has been more widely appreciated. Thanks to this understanding, along with the improved ability to use these in practice thanks to improvements in computer hardware, increases in data availability, and algorithmic tweaks that allow neural networks to be trained faster and more easily, neural networks are now finally living up to their potential. We now have what Rosenblatt had promised: \"a machine capable of perceiving, recognizing and identifying its surroundings without any human training or control\". And you will learn how to build them in this book."
] ]
}, },
{ {
@ -233,7 +233,7 @@
"source": [ "source": [
"Since we are going to be spending a lot of time together, let's get to know each other a bit… We are Sylvain and Jeremy, your guides on this journey. We hope that you will find us well suited for this position.\n", "Since we are going to be spending a lot of time together, let's get to know each other a bit… We are Sylvain and Jeremy, your guides on this journey. We hope that you will find us well suited for this position.\n",
"\n", "\n",
"Jeremy has been using and teaching machine learning for around 30 years. He started using neural networks 25 years ago. During this time he has led many companies and projects which have machine learning at their core, including founding the first company to focus on deep learning and medicine, Enlitic, and taking on the role of Pres and chief scientist of the world's largest machine learning community, Kaggle. He is the co-founder, along with Dr Rachel Thomas, of fast.ai, the organisation which built the course that this book is based on.\n", "Jeremy has been using and teaching machine learning for around 30 years. He started using neural networks 25 years ago. During this time he has led many companies and projects which have machine learning at their core, including founding the first company to focus on deep learning and medicine, Enlitic, and taking on the role of Pres and chief scientist of the world's largest machine learning community, Kaggle. He is the co-founder, along with Dr Rachel Thomas, of fast.ai, the organisation that built the course this book is based on.\n",
"\n", "\n",
"From time to time you will hear directly from us, in sidebars like this one from Jeremy:" "From time to time you will hear directly from us, in sidebars like this one from Jeremy:"
] ]
@ -310,7 +310,7 @@
"source": [ "source": [
"The hardest part of deep learning is artisanal: how do you know if you've got enough data; whether it is in the right format; if your model is training properly; and if it's not, what should you do about it? That is why we believe in learning by doing. As with basic data science skills, with deep learning you only get better through practical experience. Trying to spend too much time on the theory can be counterproductive. The key is to just code and try to solve problems: the theory can come later, when you have context and motivation.\n", "The hardest part of deep learning is artisanal: how do you know if you've got enough data; whether it is in the right format; if your model is training properly; and if it's not, what should you do about it? That is why we believe in learning by doing. As with basic data science skills, with deep learning you only get better through practical experience. Trying to spend too much time on the theory can be counterproductive. The key is to just code and try to solve problems: the theory can come later, when you have context and motivation.\n",
"\n", "\n",
"There will be times when the journey will feel hard. Times where you feel stuck. Don't give up! Rewind through the book to find the last bit where you definitely weren't stuck, and then read slowly through from there to find the first thing that isn't clear. Then try some code experiments yourself, and Google around for more tutorials on whatever the issue you're stuck with is--often you'll find some different angle on the material which might help it to click. Also, it's to not understand everything on first reading. Trying to understand the material serially before proceeding can sometimes be hard. Sometimes things click into place after you got more context from parts down the road, from having a bigger picture. So if you do get stuck on a section, trying moving on anyway and make a note to come back to it later.\n", "There will be times when the journey will feel hard. Times where you feel stuck. Don't give up! Rewind through the book to find the last bit where you definitely weren't stuck, and then read slowly through from there to find the first thing that isn't clear. Then try some code experiments yourself, and Google around for more tutorials on whatever the issue you're stuck with is--often you'll find some different angle on the material which might help it to click. Also, it's ok to not understand everything on first reading. Trying to understand the material serially before proceeding can sometimes be hard. Sometimes things click into place after you got more context from parts down the road, from having a bigger picture. So if you do get stuck on a section, try moving on anyway and make a note to come back to it later.\n",
"\n", "\n",
"Remember, you don't need any particular academic background to succeed at deep learning. Many important breakthroughs are made in research and industry by folks without a PhD, such as the paper [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434), one of the most influential papers of the last decade, with over 5000 citations, which was written by Alec Radford when he was an under-graduate. Even at Tesla, where they're trying to solve the extremely tough challenge of making a self-driving car, CEO [Elon Musk says](https://twitter.com/elonmusk/status/1224089444963311616):\n", "Remember, you don't need any particular academic background to succeed at deep learning. Many important breakthroughs are made in research and industry by folks without a PhD, such as the paper [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434), one of the most influential papers of the last decade, with over 5000 citations, which was written by Alec Radford when he was an under-graduate. Even at Tesla, where they're trying to solve the extremely tough challenge of making a self-driving car, CEO [Elon Musk says](https://twitter.com/elonmusk/status/1224089444963311616):\n",
"\n", "\n",
@ -321,7 +321,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"What you will need to succeed however is to apply what you learn in this book to a personal project and always perservere." "What you will need to succeed however is to apply what you learn in this book to a personal project and always persevere."
] ]
}, },
{ {
@ -441,7 +441,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"> jargon: (Graphic Processing Unit) GPU: Also known as a *graphics card*. A special kind of processor in your computer than can handle thousands of single tasks at the same time, especially designed for displaying 3d environments on a computer for playing games. These same basic tasks are very similar to what neural networks do, such that GPUs can run neural networks hundreds of times faster than regular CPUs. All modern computers contain a GPU, but few contain the right kind of GPU necessary for deep learning." "> jargon: (Graphic Processing Unit) GPU: Also known as a *graphics card*. A special kind of processor in your computer than can handle thousands of single tasks at the same time, especially designed for displaying 3D environments on a computer for playing games. These same basic tasks are very similar to what neural networks do, such that GPUs can run neural networks hundreds of times faster than regular CPUs. All modern computers contain a GPU, but few contain the right kind of GPU necessary for deep learning."
] ]
}, },
{ {
@ -517,7 +517,7 @@
"source": [ "source": [
"A notebook consists of _cells_. There are two main types of cell:\n", "A notebook consists of _cells_. There are two main types of cell:\n",
"\n", "\n",
"- Cells containing formatted text, images, and so forth. These use a format called _markdown_, which we will learn about soon\n", "- Cells containing formatted text, images, and so forth. These use a format called *markdown*, which we will learn about soon\n",
"- Cells containing code, which can be executed, and outputs will appear immediately underneath (which could be plain text, tables, images, animations, sounds, or even interactive applications)\n", "- Cells containing code, which can be executed, and outputs will appear immediately underneath (which could be plain text, tables, images, animations, sounds, or even interactive applications)\n",
"\n", "\n",
"Jupyter notebooks can be in one of two modes, edit mode, or command mode. In edit mode typing the keys on your keyboard types the letters into the cell in the usual way. However, in command mode, you will not see any flashing cursor, and the keys on your keyboard will each have a special function.\n", "Jupyter notebooks can be in one of two modes, edit mode, or command mode. In edit mode typing the keys on your keyboard types the letters into the cell in the usual way. However, in command mode, you will not see any flashing cursor, and the keys on your keyboard will each have a special function.\n",
@ -642,7 +642,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"You just saw how a cell that outputs a table looks inside the book. Here is an example of cell that outputs text:" "You just saw how a cell that outputs a table looks inside the book. Here is an example of a cell that outputs text:"
] ]
}, },
{ {
@ -669,7 +669,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Jupyter will always print or show the result of the last line (if there is one). For instance, here is an example of cell that outputs an image:" "Jupyter will always print or show the result of the last line (if there is one). For instance, here is an example of a cell that outputs an image:"
] ]
}, },
{ {
@ -705,7 +705,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"So, how do we know if this model is any good? You can see the error rate (proportion of images that were incorrectly identified) printed as the last column of the table. As you can see, the model is nearly perfect, even although the training time was only a few seconds (not including the one-time downloading of dataset and pretrained model). In fact, the accuracy you've achieved already is far better than anybody had ever achieved just 10 years ago!\n", "So, how do we know if this model is any good? You can see the error rate (proportion of images that were incorrectly identified) printed as the last column of the table. As you can see, the model is nearly perfect, even though the training time was only a few seconds (not including the one-time downloading of the dataset and pretrained model). In fact, the accuracy you've achieved already is far better than anybody had ever achieved just 10 years ago!\n",
"\n", "\n",
"Finally, let's check that this model actually works. Go and get a photo of a dog, or a cat; if you don't have one handy, just search Google images and download an image that you find there. Now execute the cell with `uploader` defined. It will output a button you can click, so you can select the image you want to classify." "Finally, let's check that this model actually works. Go and get a photo of a dog, or a cat; if you don't have one handy, just search Google images and download an image that you find there. Now execute the cell with `uploader` defined. It will output a button you can click, so you can select the image you want to classify."
] ]
@ -919,7 +919,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"> : _Suppose we arrange for some automatic means of testing the effectiveness of any current weight assignment in terms of actual performance and provide a mechanism for altering the weight assignment so as to maximize the performance. We need not go into the details of such a procedure to see that it could be made entirely automatic and to see that a machine so programed would \"learn\" from its experience._" "> : _Suppose we arrange for some automatic means of testing the effectiveness of any current weight assignment in terms of actual performance and provide a mechanism for altering the weight assignment so as to maximize the performance. We need not go into the details of such a procedure to see that it could be made entirely automatic and to see that a machine so programmed would \"learn\" from its experience._"
] ]
}, },
{ {
@ -1021,7 +1021,7 @@
"\n", "\n",
"Next, he 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, that would involve having a model with one set of weights play against another with a different set, and seeing which one won.\n", "Next, he 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, that would involve having a model with one set of weights play against another with a different set, and seeing which one won.\n",
"\n", "\n",
"Finally, he says we need *a mechanism for altering the weight assignment so as to maximize the performance*. For instance, he could look at the difference in weights between the winning model and the losing model, and adjust the weights a little further in the winning *direction*. We can now see why he said that such a procedure *could be made entirely automatic and... a machine so programed would \"learn\" from its experience*.\n", "Finally, he says we need *a mechanism for altering the weight assignment so as to maximize the performance*. For instance, he could look at the difference in weights between the winning model and the losing model, and adjust the weights a little further in the winning *direction*. We can now see why he said that such a procedure *could be made entirely automatic and... a machine so programmed would \"learn\" from its experience*.\n",
"\n", "\n",
"Here is the full picture of Samuel's idea of training a machine learning model:" "Here is the full picture of Samuel's idea of training a machine learning model:"
] ]
@ -1243,7 +1243,7 @@
"source": [ "source": [
"It's not too hard to imagine what the model might look like for a checkers program. There might be a range of checkers strategies encoded, and some kind of search mechanism, and then the weights could vary how strategies are selected, what parts of the board are focused on during a search, and so forth. But it's not at all obvious what the model might look like for an image recognition program.\n", "It's not too hard to imagine what the model might look like for a checkers program. There might be a range of checkers strategies encoded, and some kind of search mechanism, and then the weights could vary how strategies are selected, what parts of the board are focused on during a search, and so forth. But it's not at all obvious what the model might look like for an image recognition program.\n",
"\n", "\n",
"What we need is some kind of function that is so flexible, that it could be used to solve any given problem, just by varying its weights. Amazingly enough, this function actually exists! It's called the *neural network*. A mathematical proof called the *universal approximation theorem* shows that this function can solve any problem to any level of accuracy. In addition, there is a completely general way to update the weights of a neural network, to make it improve at any given task. This is called *stochastic gradient descent* (SGD). We'll see how neural networks and SGD work in detail later in this book, as well as explaining the universal approximation theorem. For now, however, we will instead use Samuel's own words: *We need not go into the details of such a procedure to see that it could be made entirely automatic and to see that a machine so programed would \"learn\" from its experience.*" "What we need is some kind of function that is so flexible, that it could be used to solve any given problem, just by varying its weights. Amazingly enough, this function actually exists! It's called the *neural network*. A mathematical proof called the *universal approximation theorem* shows that this function can solve any problem to any level of accuracy. In addition, there is a completely general way to update the weights of a neural network, to make it improve at any given task. This is called *stochastic gradient descent* (SGD). We'll see how neural networks and SGD work in detail later in this book, as well as explaining the universal approximation theorem. For now, however, we will instead use Samuel's own words: *We need not go into the details of such a procedure to see that it could be made entirely automatic and to see that a machine so programmed would \"learn\" from its experience.*"
] ]
}, },
{ {
@ -1478,7 +1478,7 @@
"\n", "\n",
"The second line downloads a standard dataset from the [fast.ai datasets collection](https://course.fast.ai/datasets) (if not previously downloaded) to your server, extracts it (if not previously extracted), and returns a `Path` object with the extracted location.\n", "The second line downloads a standard dataset from the [fast.ai datasets collection](https://course.fast.ai/datasets) (if not previously downloaded) to your server, extracts it (if not previously extracted), and returns a `Path` object with the extracted location.\n",
"\n", "\n",
"> S: Throughout my time studying fast.ai, and even still today, I've learned a lot about productive coding practices. The fastai library and fast.ai notebooks is full of great little tips that have helped make me a better programmer. For instance, notice that the fastai library doesn't just return a string containing the path to the dataset, but a Path object. This is a really useful class from the Python 3 standard library that makes accessing files and directories much easier. If you haven't come across it before, be sure to check out its documentation or a tutorial and try it out. Note that the book.fast.ai website contains links to recommended tutorials for each chapter. I'll keep letting you know about little coding tips I've found useful as we come across them." "> S: Throughout my time studying fast.ai, and even still today, I've learned a lot about productive coding practices. The fastai library and fast.ai notebooks are full of great little tips that have helped make me a better programmer. For instance, notice that the fastai library doesn't just return a string containing the path to the dataset, but a Path object. This is a really useful class from the Python 3 standard library that makes accessing files and directories much easier. If you haven't come across it before, be sure to check out its documentation or a tutorial and try it out. Note that the book.fast.ai website contains links to recommended tutorials for each chapter. I'll keep letting you know about little coding tips I've found useful as we come across them."
] ]
}, },
{ {
@ -1507,7 +1507,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"> Note: _classification_ and _regression_ have very specific meanings in machine learning. These are the two main types of model that we will be investigating in this book. A classification model is one which attempts to predict a class, or category. That is, predicting from a number of discrete possibilities, such as \"dog\" or \"cat\". A regression model is one which attempts to predict one or more numeric quantities, such as temperature, or a location. Sometimes people use the word _regression_ as a shortcut to a particular kind of model called a _linear regression model_; this is a bad practice, and we won't be using that terminology in this book!" "> Note: _classification_ and _regression_ have very specific meanings in machine learning. These are the two main types of model that we will be investigating in this book. A classification model is one which attempts to predict a class, or category. That is, predicting from a number of discrete possibilities, such as \"dog\" or \"cat\". A regression model is one which attempts to predict one or more numeric quantities, such as temperature, or a location. Sometimes people use the word _regression_ as a shortcut to a particular kind of model called a *linear regression model*; this is a bad practice, and we won't be using that terminology in this book!"
] ]
}, },
{ {
@ -1520,7 +1520,7 @@
"\n", "\n",
"fastai will *always* show you your model's accuracy using *only* the validation set, *never* the training set. This is absolutely critical, because if you train a large enough model for a long enough time, it will eventually learn to *memorize* the label of every item in your dataset! This is not actually a useful model, because what we care about is how well our model works on *previously unseen images*. That is always our goal when creating a model: to be useful on data that the model only sees in the future, after it has been trained.\n", "fastai will *always* show you your model's accuracy using *only* the validation set, *never* the training set. This is absolutely critical, because if you train a large enough model for a long enough time, it will eventually learn to *memorize* the label of every item in your dataset! This is not actually a useful model, because what we care about is how well our model works on *previously unseen images*. That is always our goal when creating a model: to be useful on data that the model only sees in the future, after it has been trained.\n",
"\n", "\n",
"Even when your model has not fully memorized all your data, earlier on in training it may have memorized certain parts of it. As a result, the longer your train for, the better your accuracy will get on the training set; and the validation set accuracy will also improve for a while, but eventually it will start getting worse, as the model starts to memorize the training set, rather than finding generalizable underlying patterns in the data. When this happens, we say that the model is *over-fitting*.\n", "Even when your model has not fully memorized all your data, earlier on in training it may have memorized certain parts of it. As a result, the longer you train for, the better your accuracy will get on the training set; and the validation set accuracy will also improve for a while, but eventually it will start getting worse, as the model starts to memorize the training set, rather than finding generalizable underlying patterns in the data. When this happens, we say that the model is *over-fitting*.\n",
"\n", "\n",
"Here's an example of what happens when you overfit, using a simplified example where we have just one parameter, and some randomly generated data based on the function `x**2`; as you see, although the predictions in the overfit model are accurate for data near the observed data, they are way off when outside of that range:" "Here's an example of what happens when you overfit, using a simplified example where we have just one parameter, and some randomly generated data based on the function `x**2`; as you see, although the predictions in the overfit model are accurate for data near the observed data, they are way off when outside of that range:"
] ]
@ -1536,7 +1536,7 @@
"cell_type": "markdown", "cell_type": "markdown",
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"**Overfitting is the most important and challenging single issue** when training for all machine learning practitioners, and all algorithms. As we will see, it is very easy to create a model that does a great job at making predictions on the exact data which it has been trained on, but it is much harder to make predictions on data that it has never seen before. And of course this is the data that will actually matter in practice. For instance, if you create a hand-written digit classifier (as we will very soon!) and use it to recognise numbers written on cheques, then you are never going to see any of the numbers that the model was trained on -- every cheque will have slightly different variations of writing to deal with. We will learn many methods to avoid overfitting in this book. However, you should only use those methods after you have confirmed that overfitting is actually occurring (i.e. you have actually observed the validation accuracy getting worse during training). We often see practitioners using over-fitting avoidance techniques even when they have enough data that they didn't need to do so, ending up with a model that could be less accurate than what they could have gotten." "**Overfitting is the singe most important and challenging issue** when training for all machine learning practitioners, and all algorithms. As we will see, it is very easy to create a model that does a great job at making predictions on the exact data which it has been trained on, but it is much harder to make predictions on data that it has never seen before. And of course this is the data that will actually matter in practice. For instance, if you create a hand-written digit classifier (as we will very soon!) and use it to recognise numbers written on cheques, then you are never going to see any of the numbers that the model was trained on -- every cheque will have slightly different variations of writing to deal with. We will learn many methods to avoid overfitting in this book. However, you should only use those methods after you have confirmed that overfitting is actually occurring (i.e. you have actually observed the validation accuracy getting worse during training). We often see practitioners using over-fitting avoidance techniques even when they have enough data that they didn't need to do so, ending up with a model that could be less accurate than what they could have gotten."
] ]
}, },
{ {
@ -1556,7 +1556,7 @@
"\n", "\n",
"The fourth line tells fastai to create a *convolutional neural network* (CNN), and selects what *architecture* to use (i.e. what kind of model to create), what data we want to train it on, and what *metric* to use. A CNN is the current state of the art approach to creating computer vision models. We'll be learning all about how they work in this book. Their structure is inspired by how the human vision system works.\n", "The fourth line tells fastai to create a *convolutional neural network* (CNN), and selects what *architecture* to use (i.e. what kind of model to create), what data we want to train it on, and what *metric* to use. A CNN is the current state of the art approach to creating computer vision models. We'll be learning all about how they work in this book. Their structure is inspired by how the human vision system works.\n",
"\n", "\n",
"There are many different architectures in fastai, which we will be learning about in this book, as well as discussing how to create your own. Most of the time, however, picking an architecture isn't a very important part of the deep learning process. It's something that academics love to talk about, but in practice it is unlikely to be something you need to spend much time on. There are some standard architectures that work most of the time, and in this case we're using one called _ResNet_ that will be learning a lot about during the book, and is both fast and accurate for many datasets and problems. The \"34\" in `resnet34` refers to the number of layers in this variant of the architecture (other options are \"18\", \"50\", \"101\", and \"152\"). Models using architectures with more layers take longer to train, and are more prone to overfitting (i.e. you can't train them for as many epochs before the accuracy on the validation set starts getting worse). On the other hand, when using more data, they can be quite a bit more accurate.\n", "There are many different architectures in fastai, which we will be learning about in this book, as well as discussing how to create your own. Most of the time, however, picking an architecture isn't a very important part of the deep learning process. It's something that academics love to talk about, but in practice it is unlikely to be something you need to spend much time on. There are some standard architectures that work most of the time, and in this case we're using one called _ResNet_ that we'll be learning a lot about during the book; it is both fast and accurate for many datasets and problems. The \"34\" in `resnet34` refers to the number of layers in this variant of the architecture (other options are \"18\", \"50\", \"101\", and \"152\"). Models using architectures with more layers take longer to train, and are more prone to overfitting (i.e. you can't train them for as many epochs before the accuracy on the validation set starts getting worse). On the other hand, when using more data, they can be quite a bit more accurate.\n",
"\n", "\n",
"A *metric* is a function that is called to measure how good the model is, using the validation set, and will be printed at the end of each *epoch*. In this case, we're using `error_rate`, which is a function provided by fastai which does just what it says: tells you what percentage of images in the validation set are being classified incorrectly. Another common metric for classification is `accuracy` (which is just `1.0 - error_rate`). fastai provides many more, which will be discussed throughout this book." "A *metric* is a function that is called to measure how good the model is, using the validation set, and will be printed at the end of each *epoch*. In this case, we're using `error_rate`, which is a function provided by fastai which does just what it says: tells you what percentage of images in the validation set are being classified incorrectly. Another common metric for classification is `accuracy` (which is just `1.0 - error_rate`). fastai provides many more, which will be discussed throughout this book."
] ]
@ -1569,7 +1569,7 @@
"\n", "\n",
"When using a pretrained model, `cnn_learner` will remove the last layer, since that is always specifically customized to the original training task (i.e. ImageNet dataset classification), and replace it with one or more new layers with randomized weights, of an appropriate size for the dataset you are working with. This last part of the model is known as the `head`.\n", "When using a pretrained model, `cnn_learner` will remove the last layer, since that is always specifically customized to the original training task (i.e. ImageNet dataset classification), and replace it with one or more new layers with randomized weights, of an appropriate size for the dataset you are working with. This last part of the model is known as the `head`.\n",
"\n", "\n",
"Using pretrained models is the *most* important method we have to allow us to train more accurate models, more quickly, with less data, and less time and money. You might think that would mean that using pretrained models would be the most studied area in academic deep learning... but you'd be very very wrong! The importance of pretrained models is generally not recognized or discussed in most courses, books, or software library features, and is rarely considered in academic papers. As we write this at the start of 2020, things are just starting to change, but it's likely to take a while. So be careful: most people you speak to will probably greatly underestimate what you can do in deep learning with few resources, because they probably won't deeply understand how to use pretrained models." "Using pretrained models is the *most* important method we have to allow us to train more accurate models, more quickly, with less data, and less time and money. You might think that would mean that using pretrained models would be the most studied area in academic deep learning... but you'd be very, very wrong! The importance of pretrained models is generally not recognized or discussed in most courses, books, or software library features, and is rarely considered in academic papers. As we write this at the start of 2020, things are just starting to change, but it's likely to take a while. So be careful: most people you speak to will probably greatly underestimate what you can do in deep learning with few resources, because they probably won't deeply understand how to use pretrained models."
] ]
}, },
{ {
@ -1584,10 +1584,10 @@
"\n", "\n",
"This is the key to deep learning — how to fit the parameters of a model to get it to solve your problem. In order to fit a model, we have to provide at least one piece of information: how many times to look at each image (known as number of *epochs*). The number of epochs you select will largely depend on how much time you have available, and how long you find it takes in practice to fit your model. If you select a number that is too small, you can always train for more epochs later.\n", "This is the key to deep learning — how to fit the parameters of a model to get it to solve your problem. In order to fit a model, we have to provide at least one piece of information: how many times to look at each image (known as number of *epochs*). The number of epochs you select will largely depend on how much time you have available, and how long you find it takes in practice to fit your model. If you select a number that is too small, you can always train for more epochs later.\n",
"\n", "\n",
"But why is the method called `fine_tune`, and not `fit`? fastai actually *does* have a method called `fit`, which does indeed fit a model (i.e. look at images in the training set multiple times, each time updating the *parameters* to make the predictions closer and closer to the *target labels*). But in this case, we've started with a pretrained model, and we don't want to through away all those capabilities that it already has. As we'll learn in this book, there are some important tricks to adapt a pretrained model for a new dataset -- a process called *fine-tuning*. When you use the `fine_tune` method, fastai will use these tricks for you. There are a few parameters you can set (which we'll discuss later), but in the default form shown here, it does two steps:\n", "But why is the method called `fine_tune`, and not `fit`? fastai actually *does* have a method called `fit`, which does indeed fit a model (i.e. look at images in the training set multiple times, each time updating the *parameters* to make the predictions closer and closer to the *target labels*). But in this case, we've started with a pretrained model, and we don't want to throw away all those capabilities that it already has. As we'll learn in this book, there are some important tricks to adapt a pretrained model for a new dataset -- a process called *fine-tuning*. When you use the `fine_tune` method, fastai will use these tricks for you. There are a few parameters you can set (which we'll discuss later), but in the default form shown here, it does two steps:\n",
"\n", "\n",
"1. Use one *epoch* to fit just those parts of the model necessary to get the new random *head* to work correctly with your dataset\n", "1. Use one *epoch* to fit just those parts of the model necessary to get the new random *head* to work correctly with your dataset\n",
"1. Use the number of epochs requested when calling the method to fit the entire model, updating the weights of the later layers (especially the head) faster than the earlier layers (which, as we'll see, generally don't require many changes from the pretrained weights).\n", "1. Use the number of epochs requested when calling the method to fit the entire model, updating the weights of the later layers (especially the head) faster than the earlier layers (which, as we'll see, generally don't require many changes from the pretrained weights)\n",
"\n", "\n",
"The *head* of a model is the part that is newly added to be specific to the new dataset. An *epoch* is one complete pass through the dataset. After calling `fit`, the results after each epoch are printed, showing the epoch number, the training and validation set losses (the \"measure of performance\" used for training the model), and any *metrics* you've requested (error rate, in this case)." "The *head* of a model is the part that is newly added to be specific to the new dataset. An *epoch* is one complete pass through the dataset. After calling `fit`, the results after each epoch are printed, showing the epoch number, the training and validation set losses (the \"measure of performance\" used for training the model), and any *metrics* you've requested (error rate, in this case)."
] ]
@ -1651,7 +1651,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"As you can see by looking at the right-hand side of this picture, the features are now able to identify and match with higher levels semantic components, such as car wheels, text, and flower petals. Using these components layers four and five can identify even higher-level concepts:" "As you can see by looking at the right-hand side of this picture, the features are now able to identify and match with higher levels' semantic components, such as car wheels, text, and flower petals. Using these components layers four and five can identify even higher-level concepts:"
] ]
}, },
{ {
@ -1725,7 +1725,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Another examples comes from the paper [Malware Classification with Deep Convolutional Neural Networks](https://ieeexplore.ieee.org/abstract/document/8328749) which explains that \"the malware binary file isdivided into 8-bit sequences which are then converted to equivalent decimalvalues. This decimal vector is reshaped and gray-scale image is generated that represent the malware sample\", like in <<malware_proc>>" "Another example comes from the paper [Malware Classification with Deep Convolutional Neural Networks](https://ieeexplore.ieee.org/abstract/document/8328749) which explains that \"the malware binary file is divided into 8-bit sequences which are then converted to equivalent decimal values. This decimal vector is reshaped and gray-scale image is generated that represent the malware sample\", like in <<malware_proc>>"
] ]
}, },
{ {
@ -1991,7 +1991,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"One othere area where deep learning has dramatically improved in the last couple of years is natural language processing (NLP). Computers can now generate text, translate automatically from one language to another, analyze comments, label words in sentences, and much more. Here is all of the code necessary to train a model which can classify the sentiment of a movie review better than anything that existed in the world just five years ago:" "One other area where deep learning has dramatically improved in the last couple of years is natural language processing (NLP). Computers can now generate text, translate automatically from one language to another, analyze comments, label words in sentences, and much more. Here is all of the code necessary to train a model which can classify the sentiment of a movie review better than anything that existed in the world just five years ago:"
] ]
}, },
{ {
@ -2170,9 +2170,9 @@
"\n", "\n",
"The outputs themselves can be deceiving: they have the results of the last time the cell was executed, but if you change the code inside a cell without executing it, you will keep them.\n", "The outputs themselves can be deceiving: they have the results of the last time the cell was executed, but if you change the code inside a cell without executing it, you will keep them.\n",
"\n", "\n",
"Except when we mention it explicitely, the notebooks provided on the book website are meant to be run in order, from top to bottom. In general, when experimenting, you will find yourself executing cells in any order to go fast (which is a super neat feature of Jupyter Notebooks) but once you have explored and arrive at the final version of your code, make sure you can run the cells of your notebooks in order (your future self won't necessarily remember the convoluted path you took otherwise!). \n", "Except when we mention it explicitly, the notebooks provided on the book website are meant to be run in order, from top to bottom. In general, when experimenting, you will find yourself executing cells in any order to go fast (which is a super neat feature of Jupyter Notebooks) but once you have explored and arrive at the final version of your code, make sure you can run the cells of your notebooks in order (your future self won't necessarily remember the convoluted path you took otherwise!). \n",
"\n", "\n",
"In edit mode, pressing `0` twice will restart the *kernel* (which is the engine powering your notebook). This will wipe your state clean and make it as if you had just started in the notebook. Clean then on the \"Cell\" menu and then on \"Run All Above\" to run all the cells above the point you are. We have found this to be very useful when developing the fastai library." "In edit mode, pressing `0` twice will restart the *kernel* (which is the engine powering your notebook). This will wipe your state clean and make it as if you had just started in the notebook. Click on the \"Cell\" menu and then on \"Run All Above\" to run all cells above the point where you are. We have found this to be very useful when developing the fastai library."
] ]
}, },
{ {
@ -2192,7 +2192,7 @@
"doc(learn.predict)\n", "doc(learn.predict)\n",
"```\n", "```\n",
"\n", "\n",
"This will make a small window pop with a content like this:" "This will make a small window pop with content like this:"
] ]
}, },
{ {
@ -2247,9 +2247,57 @@
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-1-1e20bc7da2d6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 6\u001b[0m 'relationship', 'race'],\n\u001b[0;32m 7\u001b[0m \u001b[0mcont_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'age'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'fnlwgt'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'education-num'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m procs = [Categorify, FillMissing, Normalize])\n\u001b[0m\u001b[0;32m 9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[0mlearn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtabular_learner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdls\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccuracy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\fastai2\\tabular\\data.py\u001b[0m in \u001b[0;36mfrom_csv\u001b[1;34m(cls, csv, path, procs, cat_names, cont_names, y_names, block_y, valid_idx, **kwargs)\u001b[0m\n\u001b[0;32m 25\u001b[0m valid_idx=None, **kwargs):\n\u001b[0;32m 26\u001b[0m return cls.from_df(pd.read_csv(csv), path, procs, cat_names=cat_names, cont_names=cont_names, y_names=y_names,\n\u001b[1;32m---> 27\u001b[1;33m block_y=block_y, valid_idx=valid_idx, **kwargs)\n\u001b[0m\u001b[0;32m 28\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 29\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mdelegates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mTabDataLoader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\fastai2\\tabular\\data.py\u001b[0m in \u001b[0;36mfrom_df\u001b[1;34m(cls, df, path, procs, cat_names, cont_names, y_names, block_y, valid_idx, **kwargs)\u001b[0m\n\u001b[0;32m 17\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcont_names\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mcont_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcat_names\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_names\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[0msplits\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRandomSplitter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mvalid_idx\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mIndexSplitter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalid_idx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m \u001b[0mto\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTabularPandas\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprocs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcat_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcont_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msplits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msplits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mblock_y\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mblock_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 20\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mto\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataloaders\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\fastai2\\tabular\\core.py\u001b[0m in \u001b[0;36msetup\u001b[1;34m(self, items, train_setup)\u001b[0m\n\u001b[0;32m 178\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'train'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_setup\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 179\u001b[0m \u001b[1;31m# Procs are called as soon as data is available\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 180\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mDatasets\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 181\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 182\u001b[0m \u001b[1;31m# Cell\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\fastcore\\transform.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, x, **kwargs)\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'_name'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_get_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'encodes'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 73\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdecode\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'decodes'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 74\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;34mf'{self.name}: {self.encodes} {self.decodes}'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36maggregate\u001b[1;34m(self, func, axis, *args, **kwargs)\u001b[0m\n\u001b[0;32m 6699\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6700\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6701\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 6702\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6703\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, func, axis, broadcast, raw, reduce, result_type, args, **kwds)\u001b[0m\n\u001b[0;32m 6911\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6912\u001b[0m )\n\u001b[1;32m-> 6913\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 6914\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6915\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mapplymap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\apply.py\u001b[0m in \u001b[0;36mget_result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 184\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_raw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 185\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 186\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 187\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 188\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mapply_empty_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 290\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 291\u001b[0m \u001b[1;31m# compute the result using the series generator\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 292\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_series_generator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 293\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 294\u001b[0m \u001b[1;31m# wrap results\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\apply.py\u001b[0m in \u001b[0;36mapply_series_generator\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 319\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 320\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries_gen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 321\u001b[1;33m \u001b[0mresults\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 322\u001b[0m \u001b[0mkeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 323\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\fastai2\\tabular\\core.py\u001b[0m in \u001b[0;36m_apply_cats\u001b[1;34m(voc, add, c)\u001b[0m\n\u001b[0;32m 183\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_apply_cats\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mvoc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0madd\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 184\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_categorical_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 185\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCategorical\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcategories\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvoc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcodes\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 186\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcodes\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0madd\u001b[0m \u001b[1;31m#if is_categorical_dtype(c) else c.map(voc[c.name].o2i)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 187\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_decode_cats\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvoc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvoc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\arrays\\categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, values, categories, ordered, dtype, fastpath)\u001b[0m\n\u001b[0;32m 412\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 413\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 414\u001b[1;33m \u001b[0mcodes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_get_codes_for_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcategories\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 415\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 416\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mnull_mask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\arrays\\categorical.py\u001b[0m in \u001b[0;36m_get_codes_for_values\u001b[1;34m(values, categories)\u001b[0m\n\u001b[0;32m 2699\u001b[0m \u001b[0mt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhash_klass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcats\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2700\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_locations\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcats\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2701\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mcoerce_indexer_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlookup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcats\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2702\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2703\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.lookup\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\core\\numeric.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m 536\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 537\u001b[0m \"\"\"\n\u001b[1;32m--> 538\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 539\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 540\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [ "source": [
"from fastai2.tabular.all import *\n", "from fastai2.tabular.all import *\n",
"path = untar_data(URLs.ADULT_SAMPLE)\n", "path = untar_data(URLs.ADULT_SAMPLE)\n",
@ -2604,7 +2652,7 @@
"\n", "\n",
"As you look at datasets throughout this book, think about where they might have come from, and how they might have been curated. Then, think about what kinds of interesting dataset you could create for your own projects. (We’ll even take you step by step through the process of creating your own image dataset soon.)\n", "As you look at datasets throughout this book, think about where they might have come from, and how they might have been curated. Then, think about what kinds of interesting dataset you could create for your own projects. (We’ll even take you step by step through the process of creating your own image dataset soon.)\n",
"\n", "\n",
"fast.ai has spent a lot of time creating cutdown versions of popular datasets that are specially designed to support rapid prototyping and experimentation, and to be easier to learn with. In this book we will often start by using one of the cutdown versions, and were later on scale up to the full-size version (just as we're doing in this chapter!) In fact, this is how the world’s top practitioners do their modelling projects in practice; they do most of their experimentation and prototyping with subsets of their data, and only use the full dataset when they have a good understanding of what they have to do." "fast.ai has spent a lot of time creating cutdown versions of popular datasets that are specially designed to support rapid prototyping and experimentation, and to be easier to learn with. In this book we will often start by using one of the cutdown versions, and we later on scale up to the full-size version (just as we're doing in this chapter!) In fact, this is how the world’s top practitioners do their modelling projects in practice; they do most of their experimentation and prototyping with subsets of their data, and only use the full dataset when they have a good understanding of what they have to do."
] ]
}, },
{ {
@ -2618,7 +2666,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Each of the models we trained showed a training and validation loss. A good validation set is one of the most important piece of your training, let's see why and learn how to create one." "Each of the models we trained showed a training and validation loss. A good validation set is one of the most important pieces of your training, let's see why and learn how to create one."
] ]
}, },
{ {
@ -2772,7 +2820,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"If you would like to learn more about how to use deep learning models in practice, including identifying and fixing errors, and creating a real working web application, and how to avoid your model causing unexpected harm to your organization or society more generally, then keep reading the next chapters, _From model to production_, and _Data ethics_. If you would like to start learning the foundations of how deep learning works _under the hood_, skip to <<chapter_mnist_basics>>, _Under the hood: training a digit classifier_. (Did you ever read _Choose Your Own Adventure_ books as a kid? Well, this is kind of like that… except with more deep learning than that book series contained.)\n", "If you would like to learn more about how to use deep learning models in practice, including identifying and fixing errors, and creating a real working web application, and how to avoid your model causing unexpected harm to your organization or society more generally, then keep reading the next chapters, _From model to production_, and _Data ethics_. If you would like to start learning the foundations of how deep learning works *under the hood*, skip to <<chapter_mnist_basics>>, _Under the hood: training a digit classifier_. (Did you ever read _Choose Your Own Adventure_ books as a kid? Well, this is kind of like that… except with more deep learning than that book series contained.)\n",
"\n", "\n",
"Either way, you will need to read all these chapters in order to progress further in the book; but it is totally up to you which order you read them in. They don't depend on each other. If you skip ahead to <<chapter_mnist_basics>>, then we will remind you at the end of that section to come back and read the chapters you skipped over before you go any further." "Either way, you will need to read all these chapters in order to progress further in the book; but it is totally up to you which order you read them in. They don't depend on each other. If you skip ahead to <<chapter_mnist_basics>>, then we will remind you at the end of that section to come back and read the chapters you skipped over before you go any further."
] ]
@ -2800,7 +2848,7 @@
" - Lots of data T / F\n", " - Lots of data T / F\n",
" - Lots of expensive computers T / F\n", " - Lots of expensive computers T / F\n",
" - A PhD T / F\n", " - A PhD T / F\n",
"1. Name five areas where deep learning is now the best in the world\n", "1. Name five areas where deep learning is now the best in the world.\n",
"1. What was the name of the first device that was based on the principle of the artificial neuron?\n", "1. What was the name of the first device that was based on the principle of the artificial neuron?\n",
"1. Based on the book of the same name, what are the requirements for \"Parallel Distributed Processing\"?\n", "1. Based on the book of the same name, what are the requirements for \"Parallel Distributed Processing\"?\n",
"1. What were the two theoretical misunderstandings that held back the field of neural networks?\n", "1. What were the two theoretical misunderstandings that held back the field of neural networks?\n",
@ -2876,7 +2924,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.7.5" "version": "3.7.4"
}, },
"toc": { "toc": {
"base_numbering": 1, "base_numbering": 1,