"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# CLICK ME\n",
"from fastai2.vision.all import *\n",
"path = untar_data(URLs.PETS)/'images'\n",
"\n",
"def is_cat(x): return x[0].isupper()\n",
"dls = ImageDataLoaders.from_name_func(\n",
" path, get_image_files(path), valid_pct=0.2, seed=42,\n",
" label_func=is_cat, item_tfms=Resize(224))\n",
"\n",
"learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
"learn.fine_tune(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sidebar: This book was written in Jupyter Notebooks"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1+1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img = PILImage.create('images/chapter1_cat_example.jpg')\n",
"img.to_thumb(192)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### End sidebar"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f78619047d7544908daa7fadd3c6f0c4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"FileUpload(value={}, description='Upload')"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"uploader = widgets.FileUpload()\n",
"uploader"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [],
"source": [
"#hide\n",
"# For the book, we can't actually click an upload button, so we fake it\n",
"uploader = SimpleNamespace(data = ['images/chapter1_cat_example.jpg'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Is this a cat?: True.\n",
"Probability it's a cat: 0.999986\n"
]
}
],
"source": [
"img = PILImage.create(uploader.data[0])\n",
"is_cat,_,probs = learn.predict(img)\n",
"print(f\"Is this a cat?: {is_cat}.\")\n",
"print(f\"Probability it's a cat: {probs[1].item():.6f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### What is machine learning?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": false
},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gv('''program[shape=box3d width=1 height=0.7]\n",
"inputs->program->results''')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gv('''model[shape=box3d width=1 height=0.7]\n",
"inputs->model->results; weights->model''')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gv('''ordering=in\n",
"model[shape=box3d width=1 height=0.7]\n",
"inputs->model->results; weights->model; results->performance\n",
"performance->weights[constraint=false label=update]''')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gv('''model[shape=box3d width=1 height=0.7]\n",
"inputs->model->results''')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### What is a neural network?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### A bit of deep learning jargon"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gv('''ordering=in\n",
"model[shape=box3d width=1 height=0.7 label=architecture]\n",
"inputs->model->predictions; parameters->model; labels->loss; predictions->loss\n",
"loss->parameters[constraint=false label=update]''')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Limitations inherent to machine learning\n",
"\n",
"From this picture we can now see some fundamental things about training a deep learning model:\n",
"\n",
"- A model cannot be created without data ;\n",
"- A model can only learn to operate on the patterns seen in the input data used to train it ;\n",
"- This learning approach only creates *predictions*, not recommended *actions* ;\n",
"- It's not enough to just have examples of input data; we need *labels* for that data too (e.g. pictures of dogs and cats aren't enough to train a model; we need a label for each one, saying which ones are dogs, and which are cats).\n",
"\n",
"Generally speaking, we've seen that most organizations that think they don't have enough data, actually mean they don't have enough *labeled* data. If any organization is interested in doing something in practice with a model, then presumably they have some inputs they plan to run their model against. And presumably they've been doing that some other way for a while (e.g. manually, or with some heuristic program), so they have data from those processes! For instance, a radiology practice will almost certainly have an archive of medical scans (since they need to be able to check how their patients are progressing over time), but those scans may not have structured labels containing a list of diagnoses or interventions (since radiologists generally create free text natural language reports, not structured data). We'll be discussing labeling approaches a lot in this book, since it's such an important issue in practice.\n",
"\n",
"Since these kinds of machine learning models can only make *predictions* (i.e. attempt to replicate labels), this can result in a significant gap between organizational goals and model capabilities. For instance, in this book you'll learn how to create a *recommendation system* that can predict what products a user might purchase. This is often used in e-commerce, such as to customize products shown on a home page, by showing the highest-ranked items. But such a model is generally created by looking at a user and their buying history (*inputs*) and what they went on to buy or look at (*labels*), which means that the model is likely to tell you about products they already have, or already know about, rather than new products that they are most likely to be interested in hearing about. That's very different to what, say, an expert at your local bookseller might do, where they ask questions to figure out your taste, and then tell you about authors or series that you've never heard of before."
]
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"### How our image recognizer works"
]
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"metadata": {},
"source": [
"### What our image recognizer learned"
]
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"source": [
"### Image recognizers can tackle non-image tasks"
]
},
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"cell_type": "markdown",
"metadata": {},
"source": [
"### Jargon recap"
]
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"source": [
"## Deep learning is not just for image classification"
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"### Sidebar: Datasets: food for models"
]
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"source": [
"### End sidebar"
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"source": [
"## Validation sets and test sets"
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"source": [
"### Use judgment in defining test sets"
]
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"source": [
"## A _Choose Your Own Adventure_ moment"
]
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"cell_type": "markdown",
"metadata": {},
"source": [
"## Questionnaire"
]
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"source": [
"It can be hard to know in pages and pages of prose what are the key things you really need to focus on and remember. So we've prepared a list of questions and suggested steps to complete at the end of each chapter. All the answers are in the text of the chapter, so if you're not sure about anything here, re-read that part of the text and make sure you understand it. Answers to all these questions are also available on the [book website](https://book.fast.ai). You can also visit [the forums](https://forums.fast.ai) if you get stuck to get help from other folks studying this material."
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"1. Do you need these for deep learning?\n",
" - Lots of math T / F\n",
" - Lots of data T / F\n",
" - Lots of expensive computers T / F\n",
" - A PhD T / F\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. 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 is a GPU?\n",
"1. Open a notebook and execute a cell containing: `1+1`. What happens?\n",
"1. Follow through each cell of the stripped version of the notebook for this chapter. Before executing each cell, guess what will happen.\n",
"1. Complete the Jupyter Notebook online appendix.\n",
"1. Why is it hard to use a traditional computer program to recognize images in a photo?\n",
"1. What did Samuel mean by \"Weight Assignment\"?\n",
"1. What term do we normally use in deep learning for what Samuel called \"Weights\"?\n",
"1. Draw a picture that summarizes Arthur Samuel's view of a machine learning model\n",
"1. Why is it hard to understand why a deep learning model makes a particular prediction?\n",
"1. What is the name of the theorem that a neural network can solve any mathematical problem to any level of accuracy?\n",
"1. What do you need in order to train a model?\n",
"1. How could a feedback loop impact the rollout of a predictive policing model?\n",
"1. Do we always have to use 224x224 pixel images with the cat recognition model?\n",
"1. What is the difference between classification and regression?\n",
"1. What is a validation set? What is a test set? Why do we need them?\n",
"1. What will fastai do if you don't provide a validation set?\n",
"1. Can we always use a random sample for a validation set? Why or why not?\n",
"1. What is overfitting? Provide an example.\n",
"1. What is a metric? How does it differ to \"loss\"?\n",
"1. How can pretrained models help?\n",
"1. What is the \"head\" of a model?\n",
"1. What kinds of features do the early layers of a CNN find? How about the later layers?\n",
"1. Are image models only useful for photos?\n",
"1. What is an \"architecture\"?\n",
"1. What is segmentation?\n",
"1. What is `y_range` used for? When do we need it?\n",
"1. What are \"hyperparameters\"?\n",
"1. What's the best way to avoid failures when using AI in an organization?"
]
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"source": [
"### Further research"
]
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"source": [
"Each chapter also has a \"further research\" with questions that aren't fully answered in the text, or include more advanced assignments. Answers to these questions aren't on the book website--you'll need to do your own research!"
]
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"source": [
"1. Why is a GPU useful for deep learning? How is a CPU different, and why is it less effective for deep learning?\n",
"1. Try to think of three areas where feedback loops might impact use of machine learning. See if you can find documented examples of that happening in practice."
]
}
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