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01_intro.ipynb

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-    "Deep learning has power, flexibility, and simplicity. That's why we believe it should be applied across many disciplines. These include the social and physical sciences, the arts, medicine, finance, scientific research, and much more. To give a personal example, despite having no background in medicine, Jeremy started Enlitic, a company that uses deep learning algorithms to diagnose illness and disease. Within months of starting the company, it was announced that their algorithm could identify malignent tumors [more accurately than radiologists](https://www.nytimes.com/2016/02/29/technology/the-promise-of-artificial-intelligence-unfolds-in-small-steps.html).\n",
+    "Deep learning has power, flexibility, and simplicity. That's why we believe it should be applied across many disciplines. These include the social and physical sciences, the arts, medicine, finance, scientific research, and much more. To give a personal example, despite having no background in medicine, Jeremy started Enlitic, a company that uses deep learning algorithms to diagnose illness and disease. Within months of starting the company, it was announced that their algorithm could identify malignant tumors [more accurately than radiologists](https://www.nytimes.com/2016/02/29/technology/the-promise-of-artificial-intelligence-unfolds-in-small-steps.html).\n",
     "\n",
     "Here's a list of some of the thousands of tasks where deep learning, or methods heavily using deep learning, is now the best in the world:\n",
     "\n",
@@ -208,7 +208,7 @@
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     "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",
-    "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 President 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",
+    "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 President 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",
     "From time to time you will hear directly from us, in sidebars like this one from Jeremy:"
    ]
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        "<PIL.Image.Image image mode=RGB size=151x192 at 0x7F67678F7E90>"
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     "Your classifier is a deep learning model. As was already mentioned, deep learning models use neural networks, which originally date from the 1950s and have become powerful very recently thanks to recent advancements.\n",
     "\n",
-    "Another key piece of context is that deep learning is just a modern area in the more general discipline of *machine learning*. To understand the essence of what you did when you trained your own classication model, you don't need to understand deep learning. It is enough to see how your model and your training process are examples of the concepts that apply to machine learning in general.\n",
+    "Another key piece of context is that deep learning is just a modern area in the more general discipline of *machine learning*. To understand the essence of what you did when you trained your own classification model, you don't need to understand deep learning. It is enough to see how your model and your training process are examples of the concepts that apply to machine learning in general.\n",
     "\n",
     "So in this section, we will describe what machine learning is. We will introduce the key concepts, and see how they can be traced back to the original essay that introduced the concept.\n",
     "\n",
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     "\n",
     "What about the next piece, an *automatic means of testing the effectiveness of any current weight assignment in terms of actual performance*? Determining \"actual performance\" is easy enough: we can simply define our model's performance as its accuracy at predicting the correct answers.\n",
     "\n",
-    "Putting this all together, and assuming that SGD is our mechanism for updating the weight assginments, we can see how our image classifier is a machine learning model, much like Samuel envisioned."
+    "Putting this all together, and assuming that SGD is our mechanism for updating the weight assignments, we can see how our image classifier is a machine learning model, much like Samuel envisioned."
    ]
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-    "**Overfitting is the single 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 achieved."
+    "**Overfitting is the single 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 achieved."
    ]
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     "\n",
     "What is a metric? A *metric* is a function that measures quality of the model's predictions 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.\n",
     "\n",
-    "The concept of a metric may remind you of loss, but there is an important distinction. The entire purpose of loss was to define a \"measure of performance\" that the training system could use to update weights automatically. In other words, a good choice for loss is a choice that is easy for stochastic gradient descent to use. But a metric is defined for human consumption. So a good metric is one that is easy for you to understand, and that hews as closely as possible to what you want the model to do. At times, you might decide that the loss function is a suitable metirc, but that is not necessarily the case."
+    "The concept of a metric may remind you of loss, but there is an important distinction. The entire purpose of loss was to define a \"measure of performance\" that the training system could use to update weights automatically. In other words, a good choice for loss is a choice that is easy for stochastic gradient descent to use. But a metric is defined for human consumption. So a good metric is one that is easy for you to understand, and that hews as closely as possible to what you want the model to do. At times, you might decide that the loss function is a suitable metric, but that is not necessarily the case."
    ]
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-    "We just covered a lot of information so let's recap breifly. <<dljargon>> provides a handy list.\n",
+    "We just covered a lot of information so let's recap briefly. <<dljargon>> provides a handy list.\n",
     "\n",
     "```asciidoc\n",
     "[[dljargon]]\n",
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     "This model is predicting movie ratings on a scale of 0.5 to 5.0 to within around 0.6 average error. Since we're predicting a continuous number, rather than a category, we have to tell fastai what range our target has, using the `y_range` parameter.\n",
     "\n",
-    "Although we're not actually using a pretrained model (for the same reason that we didn't for the tabular model), this example shows that fastai let's us use `fine_tune` even in this case (we'll learn how and why this works later in <<chapter_pet_breeds>>). Sometimes it's best to experiment with `fine_tune` versus `fit_one_cycle` to see which works best for your dataset.\n",
+    "Although we're not actually using a pretrained model (for the same reason that we didn't for the tabular model), this example shows that fastai lets us use `fine_tune` even in this case (we'll learn how and why this works later in <<chapter_pet_breeds>>). Sometimes it's best to experiment with `fine_tune` versus `fit_one_cycle` to see which works best for your dataset.\n",
     "\n",
     "We can use the same `show_results` call we saw earlier to view a few examples of user and movie IDs, actual ratings, and predictions:"
    ]