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To make some of the issues, we’re going to be talking about simpler. We’re going to focus in on some sense very simple kinds of problems. Problems that take inputs just like what we’ve been looking at. Be the graph or less, whatever happens to be and processes that to get an answer, and the

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[MUSIC PLAYING] Stanford University. There’s a global shortage of radiologists. According to the World Health Organization, 2/3 of the planet’s population, over 4 billion people, have insufficient access to radiology services. AI can help that. We developed a deep-learning algorithm, which we call CheXNet, to interpret chest X-ray images. That can take, as input, any

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About three years ago I became the national coach of the United States International Math Olympian Team. I was very happy for a day thinking this is very interesting. But the next day I started to think that maybe I should do something with this. And I decided that I wanted to focus not only

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This brings us to a slightly more interesting problem called the Top K Problem. Here’s the idea. Imagine we’ve got our list of values L, and there’s n values in the list. We can compute the max or the second max, the second largest, just like we’ve been doing, but in some sense, it doesn’t

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Hello friends, Welcome back! In this video, we will study about Travelling Salesman Problem. And will see how do we solve Travelling Salesman Problem using Branch & Bound Approach Travelling Salesman Problem (TSP) is a very important problem in Algorithms which is frequently asked in the semester exams And because this problem is bit lengthy,

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Let’s look at a very simple piece of code that implements this planning algorithm. We have a grid here as before with 0s and 1s. You’re familiar with it. The start location on the top left, the goal location on the bottom right. We can set up arbitrary obstacles like a wall over here and

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the mode of research right now in theoretical computer science is you think of a problem and you come up with an algorithm that solve this problem this is Boaz Barak, a professor at Harvard University I think we are starting to see more general themes that kind of repeat themselves the same tool or

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All right. There’s one more class of randomized optimization algorithms that is really attractive. It’s, it’s, the, it’s very interesting to people. It has proven itself time and time and again. And it’s this notion of genetic algorithms. So the main insight that’s exploited in the genetic algorithm setting is this. So let’s imagine we’ve

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