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25 algorithms every programmer should know: Let's start with my top favorite 10. If nothing else, you should read about these algorithms and have a good idea of how they work: 1. Linear search to find an element in a list 2. Binary search to find an element on...

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Santiago

451,254 subscribers

273,905 次观看 • 2 年前 •via X (Twitter)

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John Crickett 的头像
John Crickett2 年前

99% of programmers will never write one of this professionally. Ironically the ones I have done aren't on this list.

Santiago 的头像
Santiago2 年前

They won't. But the usefulness of learning these algorithms is not to write them later. That's the argument I make in the video.

Raul Junco 的头像
Raul Junco2 年前

The main benefit of understanding algorithms is the skills you learn to solve problems efficiently and effectively.

Byron Goodman 的头像
Byron Goodman2 年前

I've never implemented DFS except in college, and graduate school. I have implemented A* and variations (BFS) many, many times. The problem with DFS is incomplete graphs and the bias introduced. A* requires more memory in comparison, but memory is cheap. Dijkstra's algorithm is basically A* so you can take that one off as well.

Calc Consulting 的头像
Calc Consulting2 年前

21 Every algorithm related to neural networks 😂

Santiago 的头像
Santiago2 年前

Yup :)

Edward Manda 的头像
Edward Manda2 年前

What’s the name of the book 📖?

Anirudh Sharma 的头像
Anirudh Sharma2 年前

Learning algorithms is not only useful for interviews but also beneficial for becoming a better problem solver.

inner voice priv/acc 的头像
inner voice priv/acc2 年前

Seriously! 📺 vids!

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In graph theory, there are algorithms that find the shortest path between two nodes. I made one with pure CSS (including the graph drawing). Drag the nodes, and the shortest path will update in real-time! A demo powered by all the modern CSS features🤩
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In graph theory, there are algorithms that find the shortest path between two nodes. I made one with pure CSS (including the graph drawing). Drag the nodes, and the shortest path will update in real-time! A demo powered by all the modern CSS features🤩

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1,200,799 次观看 • 4 个月前

Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.
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Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.

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52,110 次观看 • 6 个月前

OPENAI WHISTLEBLOWER'S MOTHER: SUCHIR WAS A GENIUS Suchir Balaji's mother, Poornima Ramaro: "When he was in high school, for 2 years, every weekend, day and night, he was practicing algorithms. He downloaded data related to machine learning from many different universities. Way before anyone else knew about AI, he was already a champion in machine learning and algorithms. At the age of 11, he started programming. Within 6 months, he used to ask me questions that I could not answer on algorithms. He was very advanced." Source:
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OPENAI WHISTLEBLOWER'S MOTHER: SUCHIR WAS A GENIUS Suchir Balaji's mother, Poornima Ramaro: "When he was in high school, for 2 years, every weekend, day and night, he was practicing algorithms. He downloaded data related to machine learning from many different universities. Way before anyone else knew about AI, he was already a champion in machine learning and algorithms. At the age of 11, he started programming. Within 6 months, he used to ask me questions that I could not answer on algorithms. He was very advanced." Source:

Mario Nawfal

588,952 次观看 • 1 年前

In the world of business, when it comes to your personal wealth, getting a job, an investment, and things like that, algorithms don't help you here. You have to be intentional about that. You have to go out and find it. You have to go and search. - Ugochukwu Obi-Chukwu, Founder & CEO, Nairametrics, speaking on solutions and opportunities for unemployed graduates. #ChannelsYouthForum #CTVTweets #May29
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In the world of business, when it comes to your personal wealth, getting a job, an investment, and things like that, algorithms don't help you here. You have to be intentional about that. You have to go out and find it. You have to go and search. - Ugochukwu Obi-Chukwu, Founder & CEO, Nairametrics, speaking on solutions and opportunities for unemployed graduates. #ChannelsYouthForum #CTVTweets #May29

Channels Television

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Santiago

51,012 次观看 • 1 年前

This is a full course on famous computer science algorithms. I'll walk you through the topics that matter most, from recursion and search to sorting, pathfinding, and dynamic programming. So if you want these topics explained in a more practical way, watch this video.
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This is a full course on famous computer science algorithms. I'll walk you through the topics that matter most, from recursion and search to sorting, pathfinding, and dynamic programming. So if you want these topics explained in a more practical way, watch this video.

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ℏεsam

117,570 次观看 • 1 年前

Re learning how to do a rubix cube! Fun fact. Just under 2 decades ago. I used to bet people I could do it under 5 mins… and I did. It’s all algorithms . Anyway need to get my time down. Here I do it under 10 as I explain the process .If you want me to show the algorithms I can break down doing the 2nd and 3rd layer. 1st layer should be logical and easy
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Re learning how to do a rubix cube! Fun fact. Just under 2 decades ago. I used to bet people I could do it under 5 mins… and I did. It’s all algorithms . Anyway need to get my time down. Here I do it under 10 as I explain the process .If you want me to show the algorithms I can break down doing the 2nd and 3rd layer. 1st layer should be logical and easy

leilani dowding 🌸🚜 ☮️

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103,737 次观看 • 10 个月前

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74,261 次观看 • 7 个月前

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3:20

.Dwarkesh Patel's episode with Eric Jang is awesome. Eric has a rare gift for making complicated ideas feel simple, which is exactly what struck me when I first met him at a dinner and immediately peppered him with questions. If you are looking to hear more from him, listen to our conversation from a couple years ago! "The history of neural networks and actually many things in AI have been inspired a lot by biology, like genetic algorithms, evolutionary algorithms, processes in both neuroscience and psychology and biology and so forth. I think drawing from nature is a great way to get inspiration... And this is going to get me a lot of flack from the scientific community, but I feel like in the last decade and a half-ish of deep learning progress, the vast majority of major contributions have come from people who did not really adhere to that way of thinking, but more like, how do I push as much data as fast as possible onto my GPU? It just empirically seems to me that people who are very attached to the idea of replicating a particular nature inspired architecture at the expense of enabling brute force compute... do not make the best algorithms. There's this essay by Sara Hooker called the the hardware lottery where it's arguing that people who focused on maximizing and designing their algorithms to suit the hardware so that it could run the brute force thing as fast as possible tended to win. "

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39,759 次观看 • 1 个月前

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Coronavirus Plushie

109,126 次观看 • 1 年前

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108,861 次观看 • 1 年前

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79,842 次观看 • 2 个月前

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They want to HIDE this from you Millions of Brits are being secretly profiled in the UK and the Department for Work and Pensions refuses to publish details about its algorithms. Dig into our report, 'Suspicion by Design'⤵️

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