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updates
This commit is contained in:
Jeremy Howard
parent
6dbb75768b
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01_intro.ipynb
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01_intro.ipynb
@ -2,7 +2,7 @@
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@ -14,7 +14,7 @@
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@ -45,18 +45,14 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"heading_collapsed": true
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},
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"metadata": {},
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"source": [
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"## Deep Learning Is for Everyone"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"A lot of people assume that you need all kinds of hard-to-find stuff to get great results with deep learning, but as you'll see in this book, those people are wrong. <<myths>> is a list of a few thing you *absolutely don't need* to do world-class deep learning.\n",
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"\n",
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@ -77,9 +73,7 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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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 many 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 its 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",
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"\n",
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@ -98,9 +92,7 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"What is remarkable is that deep learning has such varied application yet nearly all of deep learning is based on a single type of model, the neural network.\n",
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"\n",
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@ -109,18 +101,14 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"heading_collapsed": true
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},
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"metadata": {},
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"source": [
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"## Neural Networks: A Brief History"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"In 1943 Warren McCulloch, a neurophysiologist, and Walter Pitts, a logician, teamed up to develop a mathematical model of an artificial neuron. In their [paper](https://link.springer.com/article/10.1007/BF02478259) \"A Logical Calculus of the Ideas Immanent in Nervous Activity\" they declared that:\n",
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"\n",
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@ -129,27 +117,21 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"McCulloch and Pitts realized 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 University 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 while he was homeless. Despite his lack of an officially recognized position and increasing social isolation, his work with McCulloch was influential, and was taken up by a psychologist named Frank Rosenblatt."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"<img alt=\"Natural and artificial neurons\" width=\"500\" caption=\"Natural and artificial neurons\" src=\"images/chapter7_neuron.png\" id=\"neuron\"/>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"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. In \"The Design of an Intelligent Automaton\" Rosenblatt wrote about this work: \"We are now 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",
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"\n",
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@ -158,9 +140,7 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"Perhaps the most pivotal work in neural networks in the last 50 years was the multi-volume *Parallel Distributed Processing* (PDP) by David Rumelhart, James McClellan, and the PDP Research Group, released in 1986 by MIT Press. Chapter 1 lays out a similar hope to that shown by Rosenblatt:\n",
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"\n",
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@ -191,18 +171,14 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"heading_collapsed": true
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},
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"metadata": {},
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"source": [
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"## Who We Are"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"We are Sylvain and Jeremy, your guides on this journey. We hope that you will find us well suited for this position.\n",
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"\n",
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@ -213,36 +189,28 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"> J: Hi everybody, I'm Jeremy! You might be interested to know that I do not have any formal technical education. I completed a BA, with a major in philosophy, and didn't have great 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 university years. 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…"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"Sylvain, on the other hand, knows a lot about formal technical education. In fact, he has written 10 math textbooks, covering the entire advanced French maths curriculum!"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"> S: Unlike Jeremy, I have not spent many years coding and applying machine learning algorithms. Rather, I recently came to the machine learning world, by watching Jeremy's fast.ai course videos. So, if you are somebody who has not opened a terminal and written commands at the command line, then you will understand where I am coming from! Look out for sidebars from me to find information most suited to people with a more mathematical or formal technical background, but less real-world coding experience—that is, people like me…"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"The fast.ai course has been studied by hundreds of thousands of students, from all walks of life, from all parts of the world. Sylvain stood out as the most impressive student of the course that Jeremy had ever seen, which led to him joining fast.ai, and then becoming the coauthor, along with Jeremy, of the fastai software library.\n",
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"\n",
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@ -253,27 +221,21 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"> A: I've decided it's time for me to learn about this AI stuff! After all, I've tried pretty much everything else… But I don't really have a background in building machine learning models. Still… how hard can it be? I'm going to be learning throughout this book, just like you are. Look out for my sidebars for learning tips that I found helpful on my journey, and hopefully you will find helpful too."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"heading_collapsed": true
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},
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"metadata": {},
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"source": [
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"## How to Learn Deep Learning"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"Harvard professor David Perkins, who wrote _Making Learning Whole_ (Jossey-Bass), 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 wind twine to make a baseball from scratch, the physics of a parabola, or the coefficient of friction of a ball on a bat.\n",
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"\n",
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@ -293,9 +255,7 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"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 you should 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",
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"\n",
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"What you will need to do to succeed however is to apply what you learn in this book to a personal project, and always persevere."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"### Your Projects and Your Mindset"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"Whether you're excited to identify if plants are diseased from pictures of their leaves, auto-generate knitting patterns, diagnose TB from X-rays, or determine when a raccoon is using your cat door, we will get you using deep learning on your own problems (via pre-trained models from others) as quickly as possible, and then will progressively drill into more details. You'll learn how to use deep learning to solve your own problems at state-of-the-art accuracy within the first 30 minutes of the next chapter! (And feel free to skip straight there now if you're dying to get coding right away.) There is a pernicious myth out there that you need to have computing resources and datasets the size of those at Google to be able to do deep learning, but it's not true.\n",
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"\n",
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@ -337,54 +291,42 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"> J: Deep learning can be set to work on almost any problem. For instance, my first startup was a company called FastMail, which provided enhanced email services when it launched in 1999 (and still does to this day). In 2002 I set it up to use a primitive form of deep learning, single-layer neural networks, to help categorize emails and stop customers from receiving spam."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"Common character traits in the people that do well at deep learning include playfulness and curiosity. The late physicist Richard Feynman is an example of someone who we'd expect to be great at deep learning: his development of an understanding of the movement of subatomic particles came from his amusement at how plates wobble when they spin in the air."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"Let's now focus on what you will learn, starting with the software."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"heading_collapsed": true
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},
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"metadata": {},
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"source": [
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"## The Software: PyTorch, fastai, and Jupyter"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"(And Why It Doesn't Matter)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"We've completed hundreds of machine learning projects using dozens of different packages, and many different programming languages. At fast.ai, we have written courses using most of the main deep learning and machine learning packages used today. After PyTorch came out in 2017 we spent over a thousand hours testing it before deciding that we would use it for future courses, software development, and research. Since that time PyTorch has become the world's fastest-growing deep learning library and is already used for most research papers at top conferences. This is generally a leading indicator of usage in industry, because these are the papers that end up getting used in products and services commercially. We have found that PyTorch is the most flexible and expressive library for deep learning. It does not trade off speed for simplicity, but provides both.\n",
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"\n",
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"However, it doesn't really matter what software you learn, because it takes only a few days to learn to switch from one library to another. What really matters is learning the deep learning foundations and techniques properly. Our focus will be on using code that clearly expresses the concepts that you need to learn. Where we are teaching high-level concepts, we will use high-level fastai code. Where we are teaching low-level concepts, we will use low-level PyTorch, or even pure Python code.\n",
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"\n",
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"By the end of the book, you'll understand nearly all the code that's inside fastai (and much of PyTorch too), because in each chapter we'll be digging a level deeper to show you exactly what's going on as we build and train our models. This means that you'll have learned the most important best practices used in modern deep learning—not just how to use them, but how they really work and are implemented. If you want to use those approaches in another framework, you'll have the knowledge you need to do so if needed.\n",
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"\n",
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"hidden": true
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},
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"metadata": {},
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"source": [
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"Let's see it in practice and train our first model."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"2"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"<PIL.Image.Image image mode=RGB size=151x192 at 0x7EFCAE0AFFD0>"
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]
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"execution_count": 5,
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}
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|
||||
"execution_count": 10,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -1087,7 +1023,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"hide_input": true
|
||||
},
|
||||
@ -1177,7 +1113,7 @@
|
||||
"<graphviz.files.Source at 0x7efcac812410>"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -1206,7 +1142,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"hide_input": true
|
||||
},
|
||||
@ -1265,7 +1201,7 @@
|
||||
"<graphviz.files.Source at 0x7efcac8129d0>"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -1359,7 +1295,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"hide_input": true
|
||||
},
|
||||
@ -1461,7 +1397,7 @@
|
||||
"<graphviz.files.Source at 0x7efcac717550>"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -1956,10 +1892,8 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
@ -2085,7 +2019,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -2124,7 +2058,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -2246,7 +2180,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -2265,7 +2199,7 @@
|
||||
"('neg', tensor(0), tensor([0.8786, 0.1214]))"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -2359,7 +2293,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -2386,7 +2320,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -2455,7 +2389,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -2592,7 +2526,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -2983,31 +2917,6 @@
|
||||
"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": "3.7.7"
|
||||
},
|
||||
"toc": {
|
||||
"base_numbering": 1,
|
||||
"nav_menu": {},
|
||||
"number_sections": false,
|
||||
"sideBar": true,
|
||||
"skip_h1_title": true,
|
||||
"title_cell": "Table of Contents",
|
||||
"title_sidebar": "Contents",
|
||||
"toc_cell": false,
|
||||
"toc_position": {},
|
||||
"toc_section_display": true,
|
||||
"toc_window_display": false
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -14,7 +14,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -48,18 +48,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"heading_collapsed": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## The Practice of Deep Learning"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"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 6 lines of code won't work for 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 and can solve the problems that arise. Conversely, 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",
|
||||
@ -70,19 +66,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"heading_collapsed": true,
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Starting Your Project"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"So where should you start your deep learning journey? The most important thing is to ensure that you have some project to work on—it is only through working on your own projects that you will get real experience building and using models. When selecting a project, the most important consideration is data availability. Regardless of whether you are doing a project just for your own learning or for practical application in your organization, you want something where you can get started quickly. We have seen many students, researchers, and industry practitioners waste months or years while they attempt to find their perfect dataset. The goal is not to find the \"perfect\" dataset or project, but just to get started and iterate from there.\n",
|
||||
"\n",
|
||||
@ -93,9 +84,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"As you work through this book, we suggest that you complete lots of small experiments, by running and adjusting the notebooks we provide, at the same time that you gradually develop your own projects. That way, you will be getting experience with all of the tools and techniques that we're explaining, as we discuss them.\n",
|
||||
"\n",
|
||||
@ -108,9 +97,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Since it is easiest to get started on a project where you already have data available, that means it's probably easiest to get started on a project related to something you are already doing, because you already have data about things that you are doing. For instance, if you work in the music business, you may have access to many recordings. If you work as a radiologist, you probably have access to lots of medical images. If you are interested in wildlife preservation, you may have access to lots of images of wildlife.\n",
|
||||
"\n",
|
||||
@ -121,37 +108,28 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"heading_collapsed": true,
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### The State of Deep Learning"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Let's start by considering whether deep learning can be any good at the problem you are looking to work on. This section provides a summary of the state of deep learning at the start of 2020. However, things move very fast, and by the time you read this some of these constraints may no longer exist. We will try to keep the [book's website](https://book.fast.ai/) up-to-date; in addition, a Google search for \"what can AI do now\" is likely to provide current information."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Computer vision"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"There are many domains in which deep learning has not been used to analyze images yet, but those where it has been tried have nearly universally shown that computers can recognize what items are in an image at least as well as people can—even specially trained people, such as radiologists. This is known as *object recognition*. Deep learning is also good at recognizing where objects in an image are, and can highlight their locations and name each found object. This is known as *object detection* (there is also a variant of this that we saw in <<chapter_intro>>, where every pixel is categorized based on what kind of object it is part of—this is called *segmentation*). Deep learning algorithms are generally not good at recognizing images that are significantly different in structure or style to those used to train the model. For instance, if there were no black-and-white images in the training data, the model may do poorly on black-and-white images. Similarly, if the training data did not contain hand-drawn images, then the model will probably do poorly on hand-drawn images. There is no general way to check what types of images are missing in your training set, but we will show in this chapter some ways to try to recognize when unexpected image types arise in the data when the model is being used in production (this is known as checking for *out-of-domain* data).\n",
|
||||
"\n",
|
||||
@ -162,18 +140,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Text (natural language processing)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Computers are very good at classifying both short and long documents based on categories such as spam or not spam, sentiment (e.g., is the review positive or negative), author, source website, and so forth. We are not aware of any rigorous work done in this area to compare them to humans, but anecdotally it seems to us that deep learning performance is similar to human performance on these tasks. Deep learning is also very good at generating context-appropriate text, such as replies to social media posts, and imitating a particular author's style. It's good at making this content compelling to humans too—in fact, even more compelling than human-generated text. However, deep learning is currently not good at generating *correct* responses! We don't currently have a reliable way to, for instance, combine a knowledge base of medical information with a deep learning model for generating medically correct natural language responses. This is very dangerous, because it is so easy to create content that appears to a layman to be compelling, but actually is entirely incorrect.\n",
|
||||
"\n",
|
||||
@ -184,18 +158,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Combining text and images"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The ability of deep learning to combine text and images into a single model is, generally, far better than most people intuitively expect. For example, a deep learning model can be trained on input images with output captions written in English, and can learn to generate surprisingly appropriate captions automatically for new images! But again, we have the same warning that we discussed in the previous section: there is no guarantee that these captions will actually be correct.\n",
|
||||
"\n",
|
||||
@ -204,36 +174,28 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Tabular data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"For analyzing time series and tabular data, deep learning has recently been making great strides. However, deep learning is generally used as part of an ensemble of multiple types of model. If you already have a system that is using random forests or gradient boosting machines (popular tabular modeling tools that you will learn about soon), then switching to or adding deep learning may not result in any dramatic improvement. Deep learning does greatly increase the variety of columns that you can include—for example, columns containing natural language (book titles, reviews, etc.), and high-cardinality categorical columns (i.e., something that contains a large number of discrete choices, such as zip code or product ID). On the down side, deep learning models generally take longer to train than random forests or gradient boosting machines, although this is changing thanks to libraries such as [RAPIDS](https://rapids.ai/), which provides GPU acceleration for the whole modeling pipeline. We cover the pros and cons of all these methods in detail in <<chapter_tabular>>."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Recommendation systems"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Recommendation systems are really just a special type of tabular data. In particular, they generally have a high-cardinality categorical variable representing users, and another one representing products (or something similar). A company like Amazon represents every purchase that has ever been made by its customers as a giant sparse matrix, with customers as the rows and products as the columns. Once they have the data in this format, data scientists apply some form of collaborative filtering to *fill in the matrix*. For example, if customer A buys products 1 and 10, and customer B buys products 1, 2, 4, and 10, the engine will recommend that A buy 2 and 4. Because deep learning models are good at handling high-cardinality categorical variables, they are quite good at handling recommendation systems. They particularly come into their own, just like for tabular data, when combining these variables with other kinds of data, such as natural language or images. They can also do a good job of combining all of these types of information with additional metadata represented as tables, such as user information, previous transactions, and so forth.\n",
|
||||
"\n",
|
||||
@ -242,73 +204,56 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<img alt=\"Terry Pratchett books recommendation\" caption=\"A not-so-useful recommendation\" id=\"pratchett\" src=\"images/pratchett.png\">"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Other data types"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Often you will find that domain-specific data types fit very nicely into existing categories. For instance, protein chains look a lot like natural language documents, in that they are long sequences of discrete tokens with complex relationships and meaning throughout the sequence. And indeed, it does turn out that using NLP deep learning methods is the current state-of-the-art approach for many types of protein analysis. As another example, sounds can be represented as spectrograms, which can be treated as images; standard deep learning approaches for images turn out to work really well on spectrograms."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"heading_collapsed": true,
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### The Drivetrain Approach"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"There are many accurate models that are of no use to anyone, and many inaccurate models that are highly useful. To ensure that your modeling work is useful in practice, you need to consider how your work will be used. In 2012 Jeremy, along with Margit Zwemer and Mike Loukides, introduced a method called *the Drivetrain Approach* for thinking about this issue."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The Drivetrain Approach, illustrated in <<drivetrain>>, was described in detail in [\"Designing Great Data Products\"](https://www.oreilly.com/radar/drivetrain-approach-data-products/). The basic idea is to start with considering your objective, then think about what actions you can take to meet that objective and what data you have (or can acquire) that can help, and then build a model that you can use to determine the best actions to take to get the best results in terms of your objective."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<img src=\"images/drivetrain-approach.png\" id=\"drivetrain\" caption=\"The Drivetrain Approach\">"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Consider a model in an autonomous vehicle: you want to help a car drive safely from point A to point B without human intervention. Great predictive modeling is an important part of the solution, but it doesn't stand on its own; as products become more sophisticated, it disappears into the plumbing. Someone using a self-driving car is completely unaware of the hundreds (if not thousands) of models and the petabytes of data that make it work. But as data scientists build increasingly sophisticated products, they need a systematic design approach.\n",
|
||||
"\n",
|
||||
@ -317,9 +262,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"hidden": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Let's consider another example: recommendation systems. The *objective* of a recommendation engine is to drive additional sales by surprising and delighting the customer with recommendations of items they would not have purchased without the recommendation. The *lever* is the ranking of the recommendations. New *data* must be collected to generate recommendations that will *cause new sales*. This will require conducting many randomized experiments in order to collect data about a wide range of recommendations for a wide range of customers. This is a step that few organizations take; but without it, you don't have the information you need to actually optimize recommendations based on your true objective (more sales!).\n",
|
||||
"\n",
|
||||
@ -366,7 +309,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -388,7 +331,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -397,7 +340,7 @@
|
||||
"<function fastbook.search_images_bing(key, term, min_sz=128, max_images=150)>"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -408,7 +351,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -417,7 +360,7 @@
|
||||
"150"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -432,12 +375,16 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We've successfully downloaded the URLs of 150 grizzly bears (or, at least, images that Bing Image Search finds for that search term). Let's look at one:"
|
||||
"We've successfully downloaded the URLs of 150 grizzly bears (or, at least, images that Bing Image Search finds for that search term).\n",
|
||||
"\n",
|
||||
"**NB**: there's no way to be sure exactly what images a search like this will find. The results can change over time. We've heard of at least one case of a community member who found some unpleasant pictures of dead bears in their search results. You'll receive whatever images are found by the web search engine. If you're running this at work, or with kids, etc, then be cautious before you display the downloaded images.\n",
|
||||
"\n",
|
||||
"Let's look at one:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"hide_input": true
|
||||
},
|
||||
@ -449,7 +396,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -459,7 +406,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -469,7 +416,7 @@
|
||||
"<PIL.Image.Image image mode=RGB size=108x128 at 0x7F0D8DA91790>"
|
||||
]
|
||||
},
|
||||
"execution_count": 18,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -488,7 +435,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -498,7 +445,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -551,7 +498,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -560,7 +507,7 @@
|
||||
"(#406) [Path('bears/black/00000149.jpg'),Path('bears/black/00000095.jpg'),Path('bears/black/00000133.jpg'),Path('bears/black/00000062.jpg'),Path('bears/black/00000023.jpg'),Path('bears/black/00000029.jpg'),Path('bears/black/00000094.jpg'),Path('bears/black/00000124.jpg'),Path('bears/black/00000105.jpg'),Path('bears/black/00000046.jpg')...]"
|
||||
]
|
||||
},
|
||||
"execution_count": 21,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -586,7 +533,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -605,7 +552,7 @@
|
||||
"(#11) [Path('bears/black/00000147.jpg'),Path('bears/black/00000057.jpg'),Path('bears/black/00000140.jpg'),Path('bears/black/00000129.jpg'),Path('bears/teddy/00000006.jpg'),Path('bears/teddy/00000048.jpg'),Path('bears/teddy/00000076.jpg'),Path('bears/teddy/00000125.jpg'),Path('bears/teddy/00000090.jpg'),Path('bears/teddy/00000075.jpg')...]"
|
||||
]
|
||||
},
|
||||
"execution_count": 22,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -624,7 +571,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -2060,31 +2007,6 @@
|
||||
"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": "3.7.7"
|
||||
},
|
||||
"toc": {
|
||||
"base_numbering": 1,
|
||||
"nav_menu": {},
|
||||
"number_sections": false,
|
||||
"sideBar": true,
|
||||
"skip_h1_title": true,
|
||||
"title_cell": "Table of Contents",
|
||||
"title_sidebar": "Contents",
|
||||
"toc_cell": false,
|
||||
"toc_position": {},
|
||||
"toc_section_display": true,
|
||||
"toc_window_display": false
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -2137,7 +2137,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This is the same value we previously calculated for the distance between these two images, the ideal 3 `mean_3` and the arbitrary sample 3 `a_3`, which are both single-image tensors with a shape of `[28,28]`.\n",
|
||||
"This is the same value we previously calculated for the distance between these two images, the ideal 3 `mean3` and the arbitrary sample 3 `a_3`, which are both single-image tensors with a shape of `[28,28]`.\n",
|
||||
"\n",
|
||||
"But in order to calculate a metric for overall accuracy, we will need to calculate the distance to the ideal 3 for _every_ image in the validation set. How do we do that calculation? We could write a loop over all of the single-image tensors that are stacked within our validation set tensor, `valid_3_tens`, which has a shape of `[1010,28,28]` representing 1,010 images. But there is a better way.\n",
|
||||
"\n",
|
||||
@ -5856,31 +5856,6 @@
|
||||
"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": "3.8.5"
|
||||
},
|
||||
"toc": {
|
||||
"base_numbering": 1,
|
||||
"nav_menu": {},
|
||||
"number_sections": false,
|
||||
"sideBar": true,
|
||||
"skip_h1_title": true,
|
||||
"title_cell": "Table of Contents",
|
||||
"title_sidebar": "Contents",
|
||||
"toc_cell": false,
|
||||
"toc_position": {},
|
||||
"toc_section_display": true,
|
||||
"toc_window_display": false
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -315,6 +315,7 @@
|
||||
"dblock1 = DataBlock(blocks=(ImageBlock(), CategoryBlock()),\n",
|
||||
" get_y=parent_label,\n",
|
||||
" item_tfms=Resize(460))\n",
|
||||
"# Place an image in the 'images/grizzly.jpg' subfolder where this notebook is located before running this\n",
|
||||
"dls1 = dblock1.dataloaders([(Path.cwd()/'images'/'grizzly.jpg')]*100, bs=8)\n",
|
||||
"dls1.train.get_idxs = lambda: Inf.ones\n",
|
||||
"x,y = dls1.valid.one_batch()\n",
|
||||
@ -2558,31 +2559,6 @@
|
||||
"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": "3.7.7"
|
||||
},
|
||||
"toc": {
|
||||
"base_numbering": 1,
|
||||
"nav_menu": {},
|
||||
"number_sections": false,
|
||||
"sideBar": true,
|
||||
"skip_h1_title": true,
|
||||
"title_cell": "Table of Contents",
|
||||
"title_sidebar": "Contents",
|
||||
"toc_cell": false,
|
||||
"toc_position": {},
|
||||
"toc_section_display": true,
|
||||
"toc_window_display": false
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -12,18 +12,6 @@
|
||||
"fastbook.setup_book()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"hide_input": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#hide\n",
|
||||
"from fastai.gen_doc.nbdoc import *"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "raw",
|
||||
"metadata": {},
|
||||
|
||||
17
README.md
17
README.md
@ -15,4 +15,19 @@ If you see someone hosting a copy of these materials somewhere else, please let
|
||||
|
||||
This is an early draft. If you get stuck running notebooks, please search the [fastai-dev forum](https://forums.fast.ai/c/fastai-users/fastai-dev/) for answers, and ask for help there if needed. Please don't use GitHub issues for problems running the notebooks.
|
||||
|
||||
If you make any pull requests to this repo, then you are assigning copyright of that work to Jeremy Howard and Sylvain Gugger. (Additionally, if you are making small edits to spelling or text, please specify the name of the file and a very brief description of what you're fixing. It's becoming increasingly difficult for reviewers to know which corrections have already been made. Thank you.)
|
||||
If you make any pull requests to this repo, then you are assigning copyright of that work to Jeremy Howard and Sylvain Gugger. (Additionally, if you are making small edits to spelling or text, please specify the name of the file and a very brief description of what you're fixing. It's difficult for reviewers to know which corrections have already been made. Thank you.)
|
||||
|
||||
## Citations
|
||||
|
||||
If you wish to cite the book, you may use the following:
|
||||
|
||||
```
|
||||
@book{howard2020deep,
|
||||
title={Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD},
|
||||
author={Howard, J. and Gugger, S.},
|
||||
isbn={9781492045526},
|
||||
url={https://books.google.no/books?id=xd6LxgEACAAJ},
|
||||
year={2020},
|
||||
publisher={O'Reilly Media, Incorporated}
|
||||
}
|
||||
```
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -14,7 +14,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -61,7 +61,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -70,7 +70,7 @@
|
||||
"2"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -124,7 +124,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -133,7 +133,7 @@
|
||||
"1.5"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -305,7 +305,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -316,7 +316,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -325,7 +325,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -334,7 +334,7 @@
|
||||
"(1, 2, 4, 8)"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -349,7 +349,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -379,7 +379,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -389,7 +389,7 @@
|
||||
"<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=197x250 at 0x7F3E279ABE50>"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -460,20 +460,11 @@
|
||||
"There are a couple of useful keyboard shortcuts in `Command Mode` that you can leverage to make Jupyter Notebook faster to use. Remember that you can switch back and forth between `Command Mode` and `Edit Mode` with <kbd>Esc</kbd> and <kbd>Enter</kbd>.\n",
|
||||
"\n",
|
||||
"- m:: Convert cell to Markdown\n",
|
||||
"\n",
|
||||
"- y:: Convert cell to Code\n",
|
||||
"\n",
|
||||
"- d+d:: Delete cell\n",
|
||||
"\n",
|
||||
"- o:: Toggle between hide or show output\n",
|
||||
"\n",
|
||||
"- Shift+Arrow up/Arrow down:: Select multiple cells. Once you have selected them you can operate on them like a batch (run, copy, paste etc).\n",
|
||||
"\n",
|
||||
"- Shift+M:: Merge selected cells\n",
|
||||
"\n",
|
||||
"- Shift+Tab (press once):: See which parameters to pass to a function \n",
|
||||
"\n",
|
||||
"- Shift+Tab (press three times):: Get additional information on the method\n"
|
||||
"- Shift+M:: Merge selected cells\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -490,11 +481,11 @@
|
||||
"There are also some tricks that you can code into a cell:\n",
|
||||
"\n",
|
||||
"- `?function-name`:: Shows the definition and docstring for that function\n",
|
||||
"\n",
|
||||
"- `??function-name`:: Shows the source code for that function\n",
|
||||
"\n",
|
||||
"- `doc(function-name)`:: Shows the definition, docstring **and links to the documentation** of the function\n",
|
||||
"(only works with fastai library imported)\n"
|
||||
"(only works with fastai library imported)\n",
|
||||
"- Shift+Tab (press once):: See which parameters to pass to a function \n",
|
||||
"- Shift+Tab (press three times):: Get additional information on the method"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -551,31 +542,6 @@
|
||||
"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": "3.7.7"
|
||||
},
|
||||
"toc": {
|
||||
"base_numbering": 1,
|
||||
"nav_menu": {},
|
||||
"number_sections": false,
|
||||
"sideBar": true,
|
||||
"skip_h1_title": true,
|
||||
"title_cell": "Table of Contents",
|
||||
"title_sidebar": "Contents",
|
||||
"toc_cell": false,
|
||||
"toc_position": {},
|
||||
"toc_section_display": true,
|
||||
"toc_window_display": false
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user