Non-linear Hypotheses

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

审阅

SB

One of the best course at Coursera, the content are very well versed, assignments and quiz are quite challenging and good, Andrew is one of the best guide we could have in our side.\n\nThanks Coursera

SS

May 17, 2019

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This is course just awesome. You get everything you wanted from this course. It covers on all topics in detail, helps in getting confidence in learning all the techiques and ideas in machine learning.

从本节课中

Neural Networks: Representation

Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks.

教学方

Andrew Ng

脚本

In this and in the next set of videos, I'd like to tell you about a learning algorithm called a Neural Network. We're going to first talk about the representation and then in the next set of videos talk about learning algorithms for it. Neutral networks is actually a pretty old idea, but had fallen out of favor for a while. But today, it is the state of the art technique for many different machine learning problems. So why do we need yet another learning algorithm? We already have linear regression and we have logistic regression, so why do we need, you know, neural networks? In order to motivate the discussion of neural networks, let me start by showing you a few examples of machine learning problems where we need to learn complex non-linear hypotheses. Consider a supervised learning classification problem where you have a training set like this. If you want to apply logistic regression to this problem, one thing you could do is apply logistic regression with a lot of nonlinear features like that. So here, g as usual is the sigmoid function, and we can include lots of polynomial terms like these. And, if you include enough polynomial terms then, you know, maybe you can get a hypotheses that separates the positive and negative examples. This particular method works well when you have only, say, two features - x1 and x2 - because you can then include all those polynomial terms of x1 and x2. But for many interesting machine learning problems would have a lot more features than just two. We've been talking for a while about housing prediction, and suppose you have a housing classification problem rather than a regression problem, like maybe if you have different features of a house, and you want to predict what are the odds that your house will be sold within the next six months, so that will be a classification problem. And as we saw we can come up with quite a lot of features, maybe a hundred different features of different houses. For a problem like this, if you were to include all the quadratic terms, all of these, even all of the quadratic that is the second or the polynomial terms, there would be a lot of them. There would be terms like x1 squared, x1x2, x1x3, you know, x1x4 up to x1x100 and then you have x2 squared, x2x3 and so on. And if you include just the second order terms, that is, the terms that are a product of, you know, two of these terms, x1 times x1 and so on, then, for the case of n equals 100, you end up with about five thousand features. And, asymptotically, the number of quadratic features grows roughly as order n squared, where n is the number of the original features, like x1 through x100 that we had. And its actually closer to n squared over two. So including all the quadratic features doesn't seem like it's maybe a good idea, because that is a lot of features and you might up overfitting the training set, and it can also be computationally expensive, you know, to be working with that many features. One thing you could do is include only a subset of these, so if you include only the features x1 squared, x2 squared, x3 squared, up to maybe x100 squared, then the number of features is much smaller. Here you have only 100 such quadratic features, but this is not enough features and certainly won't let you fit the data set like that on the upper left. In fact, if you include only these quadratic features together with the original x1, and so on, up to x100 features, then you can actually fit very interesting hypotheses. So, you can fit things like, you know, access a line of the ellipses like these, but you certainly cannot fit a more complex data set like that shown here. So 5000 features seems like a lot, if you were to include the cubic, or third order known of each others, the x1, x2, x3. You know, x1 squared, x2, x10 and x11, x17 and so on. You can imagine there are gonna be a lot of these features. In fact, they are going to be order and cube such features and if any is 100 you can compute that, you end up with on the order of about 170,000 such cubic features and so including these higher auto-polynomial features when your original feature set end is large this really dramatically blows up your feature space and this doesn't seem like a good way to come up with additional features with which to build none many classifiers when n is large. For many machine learning problems, n will be pretty large. Here's an example. Let's consider the problem of computer vision. And suppose you want to use machine learning to train a classifier to examine an image and tell us whether or not the image is a car. Many people wonder why computer vision could be difficult. I mean when you and I look at this picture it is so obvious what this is. You wonder how is it that a learning algorithm could possibly fail to know what this picture is. To understand why computer vision is hard let's zoom into a small part of the image like that area where the little red rectangle is. It turns out that where you and I see a car, the computer sees that. What it sees is this matrix, or this grid, of pixel intensity values that tells us the brightness of each pixel in the image. So the computer vision problem is to look at this matrix of pixel intensity values, and tell us that these numbers represent the door handle of a car. Concretely, when we use machine learning to build a car detector, what we do is we come up with a label training set, with, let's say, a few label examples of cars and a few label examples of things that are not cars, then we give our training set to the learning algorithm trained a classifier and then, you know, we may test it and show the new image and ask, "What is this new thing?". And hopefully it will recognize that that is a car. To understand why we need nonlinear hypotheses, let's take a look at some of the images of cars and maybe non-cars that we might feed to our learning algorithm. Let's pick a couple of pixel locations in our images, so that's pixel one location and pixel two location, and let's plot this car, you know, at the location, at a certain point, depending on the intensities of pixel one and pixel two. And let's do this with a few other images. So let's take a different example of the car and you know, look at the same two pixel locations and that image has a different intensity for pixel one and a different intensity for pixel two. So, it ends up at a different location on the figure. And then let's plot some negative examples as well. That's a non-car, that's a non-car . And if we do this for more and more examples using the pluses to denote cars and minuses to denote non-cars, what we'll find is that the cars and non-cars end up lying in different regions of the space, and what we need therefore is some sort of non-linear hypotheses to try to separate out the two classes. What is the dimension of the feature space? Suppose we were to use just 50 by 50 pixel images. Now that suppose our images were pretty small ones, just 50 pixels on the side. Then we would have 2500 pixels, and so the dimension of our feature size will be N equals 2500 where our feature vector x is a list of all the pixel testings, you know, the pixel brightness of pixel one, the brightness of pixel two, and so on down to the pixel brightness of the last pixel where, you know, in a typical computer representation, each of these may be values between say 0 to 255 if it gives us the grayscale value. So we have n equals 2500, and that's if we were using grayscale images. If we were using RGB images with separate red, green and blue values, we would have n equals 7500. So, if we were to try to learn a nonlinear hypothesis by including all the quadratic features, that is all the terms of the form, you know, Xi times Xj, while with the 2500 pixels we would end up with a total of three million features. And that's just too large to be reasonable; the computation would be very expensive to find and to represent all of these three million features per training example. So, simple logistic regression together with adding in maybe the quadratic or the cubic features - that's just not a good way to learn complex nonlinear hypotheses when n is large because you just end up with too many features. In the next few videos, I would like to tell you about Neural Networks, which turns out to be a much better way to learn complex hypotheses, complex nonlinear hypotheses even when your input feature space, even when n is large. And along the way I'll also get to show you a couple of fun videos of historically important applications of Neural networks as well that I hope those videos that we'll see later will be fun for you to watch as well.