If we run NetInitialize a bunch of times, we’ll get a bunch of different results: But the big question is: can we find an instance of this “random function” that’s useful for whatever we’re trying to do? How do we raise conscientious consumers and designers of AI? High school statistics Learn high school statistics—scatterplots, two-way tables, normal distributions, binomial probability, and more. Use neural nets to finally beat that game you’ve never been able to or improve projects using computer vision to find patterns. Still, Nearest is nice because its functionality is pretty easy to understand (and one can have reasonable guesses about algorithms it could use). (After all, âmachine learning trainer” will surely be a big future career for some of today’s middle schoolers…) And the good news is that in the Wolfram Language environment, it’s possible to make training work with only a modest amount of data. But what does “learn” mean? Keep on, Stephen! But with our whole Wolfram Language setup there’s a lot that even middle schoolers can do. Here’s a contour plot of (the first element of) its output, as a function of its two inputs. Like here’s the classic MNIST handwritten digit training set, with its 60,000 training examples: One thing one can do with a training set like this is just feed a random sample of it into Classify. What was my thinking behind this machine learning section? Here’s a plot of the lengths of the first 60 Roman numerals: After a small amount of training, here’s what the network managed to reproduce: And one might think that maybe this is the best it’ll ever do. I’m holding three workshops this week (for K-5, 6-12, and school leaders): For Elementary Teachers (K-5 Workshop) For Middle School and High School Teachers (6-12 Workshop) For School Leaders (Admin and Central Office Workshop) You can sign-up to be a part of the training right here! Content analytics that organize and optimize content modules: a. Gooru, IBM Watson Content Analytics. Mapped to CSTA standards, the course takes a wide lens on computer science by covering topics such as problem solving, programming, physical computing, user-centered design, and data, while inspiring students … Well, first we have to set up a raw neural net. As an example, let’s just take the first 3 “layers” of the network, apply them to the tiger, and visualize what comes out: Basically what’s happening is that the network has made lots of copies of the original image, and then processed each of them to pick out a different aspect of the image. Well, let’s look at FeatureSpacePlot: Some of this looks good—but some looks confusing. Computer Science Discoveries is an introductory course for 6 - 10th grade students that can be flexibly taught as a single semester, two semesters over multiple years, or as a full year course. But the result is: And, yes, when the image isn’t too blurred, TextRecognize can recognize the text, but when the text gets too blurred, it stops being able to. (If one was doing this in a class, for example, one might try some different fonts and sizes, and discuss why some survive more blurring than others.). And here we begin to see some of the subtlety of machine learning. Through a series of lessons and activities, students learn technical concepts—such as how to train a simple classifier—and the ethical implications those technical concepts entail, such as algorithmic bias. And what’s nice about this is that—as well as being useful in its own right—it also reinforces the idea of how something like TextRecognize might work by finding the “nearest letter” to whatever input it’s given. I'd imagine it's a lot easier and more effective to teach middle schools basic probability first. And conveniently, the Wolfram Language has a bunch of classic neural nets built in. But rather than concentrating on that, what I do in the book is just to talk about the case of numbers, where it’s really easy to see what “nearest” means: Nearest isn’t the most exciting function to play with: one potentially puts a lot of things in, and then just one “nearest thing” comes out. Recently, digital education venture capitalist Tom Vander Ark shared 8 different areas where leading-edge platforms are already leveraging machine learning in education: 1. But I was curious if it could eventually do better—and so I just let it train for 2 minutes on my computer. Well, the crucial point is that what NetTrain does is to progressively tweak the weights in each layer of a neural network to try to get the overall behavior of the net to match the training examples you gave. Because it gives a sense of the “attractor” around the “cheetah” concept: stay fairly close and the cheetah can still be recognized; go too far away and it can’t. Get free flashcards, practice tests, and other useful tools in more than 60 high school subjects. Here’s what it set them to in this particular case: Why is any of this useful? Some students are thinking about spending approximately 60 hours on the basics of programming (they have just a little bit of experience) and if possible, the combination of programming and Machine Learning. After about 20 seconds, NetTrain decides it’s gone far enough, and generates a finally trained net—which works pretty well: If you stop the training early, it won’t do quite so well: In the professional world of machine learning, there’s a whole art and science of figuring out the best parameters for training. INTRODUCTION Developments in machine learning (ML) have Presumably the new results are “better”, though it’s not clear what that means, given that we don’t have an official right-answer “blurred cheetah” category, and who’s to say whether the blurriest image is more like a whortleberry or a person.). The Wolfram Language stores its latest machine learning classifiers in the cloud — but if you ’ re using a desktop system, they ’ ll automatically be downloaded, and then they ’ ll run locally. This effort then broadened into planning learning experiences for more children, and the group developed a curriculum geared toward middle school students. And what I do is to show the function Classify classifying handwritten digits into 0s and 1s. Well, first, it has to fit into the flow of the book—using only concepts that have already been introduced, and, when possible, reinforcing them. (iOS, Android) Varsity Tutors. Here's a fun activity that will have your child teaching you for once, while you find out exactly what she is learning in school. In fact, it’s actually using everything it learned from the creation of ImageIdentify—and the tens of millions of images it saw in connection with that—to know up front what features it should pay attention to. (In reality, LanguageIdentify uses a combination of traditional lookup, and modern machine learning techniques.). 2. All you need is a coin or a … This can lead to a useful discussion about prior probabilities, and about issues like trading off specificity for certainty. In the main text I don’t talk about the precise definition of “nearness” for words, but again, kids easily get the basic idea. Understand what ’ s not clear what ’ s useful to show both where machine learning-based functions succeed and... Here one ’ s much simpler than the ImageIdentify net, but what probabilities does it assign to them the! To tackle tasks that have, until now, only been carried out by.. To talk about is training was very inspiring of fun trying out sentiment.. To simulate training a machine to identify timing and traffic changes faster than humans.! Wonderful way to progressively tweak weights so as to get to appropriate values in the Wolfram,! 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