Merge branch 'master' into patch-9

.gitignore

@@ -1,3 +1,6 @@
+*.bak
+*.pkl
+bears/
 __pycache__/
 .last_checked
 .gitconfig

01_intro.ipynb

@@ -157,7 +157,7 @@
     "\n",
     "Although researchers showed 30 years ago that to get practical good performance you need to use even more layers of neurons, it is only in the last decade that this has been more widely appreciated. Neural networks are now finally living up to their potential, thanks to the understanding to use more layers as well as improved ability to do so thanks to improvements in computer hardware, increases in data availability, and algorithmic tweaks that allow neural networks to be trained faster and more easily. We now have what Rosenblatt had promised: \"a machine capable of perceiving, recognizing and identifying its surroundings without any human training or control\".\n",
     "\n",
-    "This is what you will learn how to build them in this book."
+    "This is what you will learn how to build in this book."
    ]
   },
   {
@@ -217,7 +217,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> J: Hi everybody, I'm Jeremy! You might be interested to know that I do not have any formal technical education. I completed a Bachelor of Arts, with a major in philosophy, and didn't do very well in my university grades. I was much more interested in doing real projects, rather than theoretical studies, so I worked full-time at a management consulting firm called McKinsey and Company throughout my degree. If you're somebody who would rather get their hands dirty building stuff rather than spend years learning abstract concepts, then you will understand where I am coming from! Look out for sidebars from me to find information most suited to people with a less mathematical or formal technical background—that is, people like me…"
+    "> J: Hi everybody, I'm Jeremy! You might be interested to know that I do not have any formal technical education. I completed a Bachelor of Arts, with a major in philosophy, and didn't do very well in my university grades. I was much more interested in doing real projects, rather than theoretical studies, so I worked full-time at a management consulting firm called McKinsey and Company throughout my degree. If you're somebody who would rather get their hands dirty building stuff than spend years learning abstract concepts, then you will understand where I am coming from! Look out for sidebars from me to find information most suited to people with a less mathematical or formal technical background—that is, people like me…"
    ]
   },
   {
@@ -263,7 +263,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Harvard professor David Perkins, who wrote Making Learning Whole, has much to say about teaching. The basic idea is to teach the *whole game*. That means that's if you're teaching baseball, you first take people to a baseball game or get them to play it. You don't teach them how to line thread into a ball, the physics of a parabola, or the coefficient of friction of a ball on a bat.\n",
+    "Harvard professor David Perkins, who wrote Making Learning Whole, has much to say about teaching. The basic idea is to teach the *whole game*. That means that if you're teaching baseball, you first take people to a baseball game or get them to play it. You don't teach them how to line thread into a ball, the physics of a parabola, or the coefficient of friction of a ball on a bat.\n",
     "\n",
     "Paul Lockhart, a Columbia math PhD, former Brown professor, and K-12 math teacher, imagines in the influential essay A Mathematician's Lament a nightmare world where music and art are taught the way math is taught. Children would not be allowed to listen to or play music until they have spent over a decade mastering music notation and theory, spending classes transposing sheet music into a different key. In art class, students study colours and applicators, but aren't allowed to actually paint until college. Sound absurd? This is how math is taught–we require students to spend years doing rote memorization, and learning dry, disconnected *fundamentals* that we claim will pay off later, long after most of them quit the subject.\n",
     "\n",
@@ -285,7 +285,7 @@
    "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",
     "\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 expected and normal to not understand everything (especially the code) 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",
+    "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 expected and normal to not understand everything (especially the code) on first reading. Trying to understand the material serially before proceeding can sometimes be hard. Sometimes things click into place after you get 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",
     "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",
@@ -388,7 +388,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As we said before, we will teach how to do things before we explain why they work. Following this top-down approach, we will begin by actually training an image classifier to recognize dogs and cats with almost 100% accuracy. To train this model and run our experiments, you will need some initial setup. Don't worry, it's not as hard as it looks like."
+    "As we said before, we will teach how to do things before we explain why they work. Following this top-down approach, we will begin by actually training an image classifier to recognize dogs and cats with almost 100% accuracy. To train this model and run our experiments, you will need some initial setup. Don't worry, it's not as hard as it looks."
    ]
   },
   {
@@ -423,7 +423,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The best choice for GPU servers for use with this book change over time, as companies come and go, and prices change. We keep a list of our recommended options on the [book website](https://book.fast.ai/). So, go there now, and follow the instructions to get connected to a GPU deep learning server. Don't worry, it only takes about two minutes to get set up on most platforms, and many don't even require any payment, or even a credit card to get started.\n",
+    "The best choice for GPU servers for use with this book changes over time, as companies come and go, and prices change. We keep a list of our recommended options on the [book website](https://book.fast.ai/). So, go there now, and follow the instructions to get connected to a GPU deep learning server. Don't worry, it only takes about two minutes to get set up on most platforms, and many don't even require any payment, or even a credit card to get started.\n",
     "\n",
     "> A: My two cents: heed this advice! If you like computers you will be tempted to setup your own box. Beware! It is feasible but surprisingly involved and distracting. There is a good reason this book is not titled, _Everything you ever wanted to know about Ubuntu system administration, NVIDIA driver installation, apt-get, conda, pip, and Jupyter notebook configuration_. That would be a book of its own. Having designed and deployed our production machine learning infrastructure at work, I can testify it has its satisfactions but it is as unrelated to modelling as maintaining an airplane is to flying one.\n",
     "\n",
@@ -508,7 +508,7 @@
    "source": [
     "When you click on a cell it will be selected. Click on the cell now which begins with the line \"# CLICK ME\". The first character in that line represents a comment in Python, so is ignored when executing the cell. The rest of the cell is, believe it or not, a complete system for creating and training a state-of-the-art model for recognizing cats versus dogs. So, let's train it now! To do so, just press shift-enter on your keyboard, or press the \"play\" button on the toolbar. Then, wait a few minutes while the following things happen:\n",
     "\n",
-    "1. A dataset containing called the [Oxford-IIIT Pet Dataset](http://www.robots.ox.ac.uk/~vgg/data/pets/) that contains 7,349 images of cats and dogs from 37 different breeds will be downloaded from the fast.ai datasets collection to the GPU server you are using, and will then be extracted\n",
+    "1. A dataset called the [Oxford-IIIT Pet Dataset](http://www.robots.ox.ac.uk/~vgg/data/pets/) that contains 7,349 images of cats and dogs from 37 different breeds will be downloaded from the fast.ai datasets collection to the GPU server you are using, and will then be extracted\n",
     "2. A *pretrained model* will be downloaded from the Internet, which has already been trained on 1.3 million images, using a competition winning model\n",
     "3. The pretrained model will be *fine-tuned* using the latest advances in transfer learning, to create a model that is specially customised for recognising dogs and cats\n",
     "\n",
@@ -2914,18 +2914,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
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-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
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02_production.ipynb

@@ -47,7 +47,7 @@
    "source": [
     "We've seen that deep learning can solve a lot of challenging problems quickly and with little code. As a beginner there's a sweet spot of problems that are similar enough to our example problems that you can very quickly get extremely useful results. However, deep learning isn't magic! The same 5 lines of code won't work on every problem anyone can think of today. Underestimating the constraints and overestimating the capabilities of deep learning may lead to frustratingly poor results. At least until you gain some experience to solve the problems that arise. Overestimating the constraints and underestimating the capabilities of deep learning may mean you do not attempt a solvable problem because you talk yourself out of it. \n",
     "\n",
-    "We often talk to people who overestimate both the constraints, and the capabilities of deep learning. Both of these can be problems: underestimating the capabilities means that you might not even try things which could be very beneficial; underestimating the constraints might mean that you fail to consider and react to important issues.\n",
+    "We often talk to people who underestimate both the constraints, and the capabilities of deep learning. Both of these can be problems: underestimating the capabilities means that you might not even try things which could be very beneficial; underestimating the constraints might mean that you fail to consider and react to important issues.\n",
     "\n",
     "The best thing to do is to keep an open mind. If you remain open to the possibility that deep learning might solve part of your problem with less data or complexity than you expect, then it is possible to design a process where you can find the specific capabilities and constraints related to your particular problem as you work through the process. This doesn't mean making any risky bets — we will show you how you can gradually roll out models so that they don't create significant risks, and can even backtest them prior to putting them in production.\n",
     "\n",
@@ -1957,18 +1957,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
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-    "version": 3
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03_ethics.ipynb

@@ -1041,18 +1041,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
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-  "language_info": {
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05_pet_breeds.ipynb

@@ -971,7 +971,7 @@
    "source": [
     "What does this function do in practice? Taking the exponential ensures all our numbers are positive, and then dividing by the sum ensures we are going to have a bunch of numbers that add up to one. The exponential also has a nice property: if one of the numbers in our activations `x` is slightly bigger than the others, the exponential will amplify this (since it grows, well... exponentially) which means that in the softmax, that number will be closer to 1. \n",
     "\n",
-    "Intuitively, the Softmax function *really* wants to pick one class among the others, so it's ideal for training a classifier when we know each picture has a definite label. (Note that it may be less ideal during inference, as you might want your model to sometimes tell you it doesn't recognize any of the classes is has seen during training, and not pick a class because it has a slightly bigger activation score. In this case, it might be better to train a model using multiple binary output columns, each using a sigmoid activation.)\n",
+    "Intuitively, the Softmax function *really* wants to pick one class among the others, so it's ideal for training a classifier when we know each picture has a definite label. (Note that it may be less ideal during inference, as you might want your model to sometimes tell you it doesn't recognize any of the classes that it has seen during training, and not pick a class because it has a slightly bigger activation score. In this case, it might be better to train a model using multiple binary output columns, each using a sigmoid activation.)\n",
     "\n",
     "Softmax is the first part of the cross entropy loss, the second part is log likeklihood. "
    ]
@@ -2532,18 +2532,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
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06_multicat.ipynb

@@ -1928,18 +1928,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
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-   "file_extension": ".py",
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07_sizing_and_tta.ipynb

@@ -1012,18 +1012,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
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08_collab.ipynb

@@ -2296,18 +2296,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
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-   "file_extension": ".py",
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09_tabular.ipynb

@@ -9737,18 +9737,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
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-    "version": 3
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-   "file_extension": ".py",
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10_nlp.ipynb

@@ -2251,18 +2251,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
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11_midlevel_data.ipynb

@@ -1264,18 +1264,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
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12_nlp_dive.ipynb

@@ -2347,18 +2347,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
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15_arch_details.ipynb

@@ -247,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
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    "source": [
@@ -377,7 +377,7 @@
   },
   {
    "cell_type": "code",
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+   "execution_count": null,
    "metadata": {},
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@@ -396,7 +396,7 @@
   },
   {
    "cell_type": "code",
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+   "execution_count": null,
    "metadata": {},
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@@ -413,7 +413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
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@@ -431,10 +431,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
-   "metadata": {
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-    "scrolled": true
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    "outputs": [
     {
      "data": {
@@ -502,7 +500,7 @@
   },
   {
    "cell_type": "code",
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+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -810,18 +808,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
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16_accel_sgd.ipynb

@@ -1307,18 +1307,6 @@
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    "language": "python",
    "name": "python3"
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-  "language_info": {
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17_foundations.ipynb

@@ -2448,18 +2448,6 @@
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    "name": "python3"
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18_CAM.ipynb

@@ -653,18 +653,6 @@
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    "name": "python3"
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app_blog.ipynb

@@ -292,18 +292,6 @@
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    "name": "python3"
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clean/01_intro.ipynb

@@ -1567,18 +1567,6 @@
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    "name": "python3"
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-  "language_info": {
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clean/02_production.ipynb

@@ -1080,18 +1080,6 @@
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    "name": "python3"
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clean/03_ethics.ipynb

@@ -311,18 +311,6 @@
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clean/05_pet_breeds.ipynb

@@ -1750,18 +1750,6 @@
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-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
   }
  },
  "nbformat": 4,

clean/06_multicat.ipynb

@@ -1344,18 +1344,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
   }
  },
  "nbformat": 4,

clean/07_sizing_and_tta.ipynb

@@ -655,18 +655,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
   }
  },
  "nbformat": 4,

clean/08_collab.ipynb

@@ -1703,18 +1703,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
   }
  },
  "nbformat": 4,

clean/09_tabular.ipynb

@@ -8340,18 +8340,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
   }
  },
  "nbformat": 4,

clean/10_nlp.ipynb

@@ -1548,18 +1548,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
   }
  },
  "nbformat": 4,

clean/11_midlevel_data.ipynb

@@ -843,18 +843,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
   }
  },
  "nbformat": 4,

clean/12_nlp_dive.ipynb

@@ -1626,18 +1626,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
   }
  },
  "nbformat": 4,

clean/15_arch_details.ipynb

@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -195,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -205,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -215,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -226,10 +226,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
-   "metadata": {
+   "metadata": {},
-    "scrolled": true
-   },
    "outputs": [
     {
      "data": {
@@ -290,7 +288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -437,18 +435,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
   }
  },
  "nbformat": 4,

clean/16_accel_sgd.ipynb

@@ -754,18 +754,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
   }
  },
  "nbformat": 4,

clean/17_foundations.ipynb

@@ -1602,18 +1602,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
   }
  },
  "nbformat": 4,

clean/18_CAM.ipynb

@@ -470,18 +470,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
   }
  },
  "nbformat": 4,

clean/app_blog.ipynb

@@ -76,18 +76,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
   }
  },
  "nbformat": 4,

settings.ini

@@ -21,4 +21,5 @@ lib_path = fastbook
 title = fastbook
 doc_host = https://fastai.github.io
 doc_baseurl = /fastbook/
+host = github