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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... a sorted list 3. Bubble sort to sort a list 4. Merge sort will also sort lists 5. Quicksort to sort the list and do it fast 6. Dijkstra to find the shortest path in a graph 7. Breadth-first Search (BFS) for trees or graphs 8. Depth-first search (DFS) for trees or graphs 9. Huffman for doing data compression 10. Anything related to dynamic programming Learning about algorithms is like getting tattoos: you never have enough. Here are another 5 algorithms that will help you go beyond the basics: 11. Kruskal for the finding minimum spanning tree 12. Floyd Warshall, shortest paths in a graph 13. Union Find to detect cycles in a graph 14. Bellman-Ford, shortest path in a graph 15. Lee for finding the shortest path in a maze If you are serious about this topic, I recommend learning about algorithms' space and time complexity. People usually refer to this topic as "Big O" notation. You should build a good intuition about the performance of different algorithms and learn how to evaluate them. Machine Learning will rule the next 50 years, so the next 10 algorithms you can't ignore are the following: 16. Linear Regression 17. Logistic Regression 18. Decision Trees 19. Bayes' theorem 20. k-Nearest Neighbors (kNN) 21. Every algorithm related to neural networks 22. K-means 23. Random forest 24. Gradient boosting algorithms 25. Any dimensionality reduction algorithm (PCA, for instance) There are many more mind-blowing algorithms! I haven't found a better way to understand how computers work from a first-principles point of view than reading about different algorithms. Take a look at the attached video.show more

Santiago

454,815 subscribers

274,092 views • 2 years ago •via X (Twitter)

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9 Comments

John Crickett2 years ago

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

Santiago2 years ago

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 Junco2 years ago

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

Byron Goodman2 years ago

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 Consulting2 years ago

21 Every algorithm related to neural networks 😂

Santiago2 years ago

Yup :)

Edward Manda2 years ago

What’s the name of the book 📖?

Anirudh Sharma2 years ago

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

inner voice priv/acc2 years ago

Seriously! 📺 vids!

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1,202,757 views • 8 months ago

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.

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.

Khurram Javed

52,110 views • 11 months ago

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:

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 views • 1 year ago

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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Channels Television

13,574 views • 5 months ago

If you could only learn one thing that will be relevant for the next 10-20 years, focus on learning how to deal with data. The future is not about faster hardware, smarter algorithms, or better ideas. The future is about DATA, and those who know how to deal with it will stay relevant much longer than anyone else. I recorded a video to show you how easy it is to get started. In the video, I'm using Kestra. For a long time, I was a fan of AirFlow. Then, I moved to AWS Step Functions. Today, I only use Kestra. Kestra is open-source (repo link below) and kind enough to sponsor my work. The video will show you how easy it is to do the following: 1. Run Kestra locally (literally, one command) 2. Build a simple flow 3. Run Python scripts as part of your flow 4. Connect to HuggingFace models If you have never built a data pipeline, open Kestra's Quick Start Guide and follow their examples. (I think it will take you one weekend to feel comfortable with the application and build the courage you need to get into more serious work.)

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Santiago

51,100 views • 1 year ago

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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Tech With Tim

18,305 views • 6 months ago

Exposing where 20 MILLION more Democrat voters illegally came from and the algorithms put in place to rig the 2026 midterm elections. 2022 Voter Count: 🟥Rep: 54.2M 🟦Dem: 51.3M 2026 Voter Count: 🟥Rep: 55.6M 🟦Dem: 70.4M Republicans gained a little over a million. But... If you add all of the voters illegally registered without U.S. citizenship from each state from 2022 to 2026, you get around 20,283,937, which is right around the ridiculous number of voters that Democrats what you to believe that they legally gained, that NOBODY is buying. On top of this you have the algorithms put in place to give Democrats an advantage for every vote. So each state does NOT cheat in the same exact way due to different laws, equipment, and a multitude of variables. This is where these algorithms come into play, which were first intoduced in the early 2000s from Clinton Curtis who designed the 51% to 49% algorithm to rig elections. These type of algorithms do not attribute 1 vote per 1 vote. In fact, 1 vote could equal MORE or LESS, .75% per vote/1.25% per vote, etc., giving candidates a massive lead or disadvantage for literrally every vote attributed. Today you are seeing races in specific states have ALL the same outcome, for example, every CA election magically ended in a 53% to 46%, favoring Democrats. Or a graph that has been circulating showing a 15% band or advantage given to one party or candidate per race, showing that these algorithms are still in place, just with different numbers and percentages attributed and added to the scripts to run these algorithms to steal elections. None of this is new, it's just getting more advanced and harder to detect as some of the scripts can erase themselves when they complete their task, hiding all and any evidence that it even existed, leaving an audit completely useless. It's time to wake up ladies and gentleman, we are closer than ever to losing this country, and if they manage to gain even more power, they will make sure we never win another election ever again and this country will fall, and the rest of the world will follow. Speak up, stand up, and fight back for the truth and accountability.

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The SCIF

493,806 views • 2 days ago

🇫🇷 Macron thinks the way social media algorithms work is becoming a serious problem for democracy. He argues that people should be able to choose their own algorithms and warns that, unless social networks change, populism and political extremes will continue to emerge. He's raising an interesting question, though. Why don't we get to choose what algorithms decide to feed us every single day? Writer: Lynn

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Mario Nawfal

45,343 views • 1 month ago

🚨🇬🇧🇺🇸 New:- Sadiq Kahn attacks President Trump, American influencers & algorithms which suggests London is not a safe or desirable City to visit “There is a lot of disinformation, misinformation and lies about how safe London is” “A lot of of these lies come from America” “The algorithms are designed to promote hate - we recognise if you are mono-ethnic populist - why you might have a problem with a progressive city like London”

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Concerned Citizen

15,442 views • 7 months ago

a playlist of 30 youtube videos to learn machine learning fundamentals from scratch if you're struggling on where to start learning ML, this list goes this "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

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ℏεsam

117,748 views • 1 year ago

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

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 🌸🚜 ☮️

40,592 views • 9 months ago

In 1956, Edsger Dijkstra needed to demonstrate a new computer called the ARMAC. He wanted a problem anyone could understand: finding the shortest route between two cities in the Netherlands. One morning, while shopping in Amsterdam with his fiancée, they stopped for coffee. Sitting at an outdoor café, Dijkstra worked out the shortest-path algorithm in about 20 minutes. No pencil. No paper. He later explained that having nothing to write on forced him to eliminate unnecessary complexity. The demonstration used a map of just 64 Dutch cities, allowing each city to be identified with six bits. He didn’t publish the algorithm until 1959. Seventy years later, Dijkstra’s algorithm remains foundational to route planning and the shortest-path calculations used by OSPF routers. One of computer science’s most influential algorithms began over a cup of coffee.

In 1956, Edsger Dijkstra needed to demonstrate a new computer called the ARMAC. He wanted a problem anyone could understand: finding the shortest route between two cities in the Netherlands. One morning, while shopping in Amsterdam with his fiancée, they stopped for coffee. Sitting at an outdoor café, Dijkstra worked out the shortest-path algorithm in about 20 minutes. No pencil. No paper. He later explained that having nothing to write on forced him to eliminate unnecessary complexity. The demonstration used a map of just 64 Dutch cities, allowing each city to be identified with six bits. He didn’t publish the algorithm until 1959. Seventy years later, Dijkstra’s algorithm remains foundational to route planning and the shortest-path calculations used by OSPF routers. One of computer science’s most influential algorithms began over a cup of coffee.

Strace

20,510 views • 5 days ago

Delta Airlines to begin using artificial intelligence algorithms to scrape your emails and more to determine the maximum amount you’re willing to spend on tickets, then adjust your price Example: If you have a death in the family, they’ll know it’s urgent and the price will go up ‘Delta says it's "fully reengineering how we price" airfare - leaning into Al algorithms that set the maximum price you're willing to spend, based on surveillance technology. About 3% of Delta's domestic ticket prices are now determined by artificial intelligence - and Delta plans to raise that to 20% by the end of the year.’ - More Perfect Union “Delta Airlines just announced on their earnings call that they're going to start using some of these algorithms to price some of their tickets. And imagine a world in which you have a death in the family, you have to go home for a funeral. And they know that through scraping your email, that is, you know, announcing when the service is going to be. They know you're in a tight spot, and so you're going to be charged much more because of that. There's so much data collected about individual people by data brokers, by this whole shadowy ecosystem that's just tracking you through your phone, through your browsing history. — There is a serious worry that companies will be able to do this.” Imagine how many other businesses are going to start doing the same thing

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Wall Street Apes

1,335,794 views • 1 year ago

Algorithms is a song that explores the idea that Robots can’t experience being in the moment where as humans can and that is the beauty that separates us….. for now Click to pre-save Algorithms:

Algorithms is a song that explores the idea that Robots can’t experience being in the moment where as humans can and that is the beauty that separates us….. for now Click to pre-save Algorithms:

KasabianHQ

34,962 views • 3 years ago

.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. "

.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. "

Sabrina Halper

39,759 views • 5 months ago

$Extropic founder Beff (e/acc) on why running probabilistic AI on deterministic hardware is quietly the biggest local optimum in computing "You have Landauer's principle. Essentially you have a minimum cost of erasing a bit." "Every time you reset a bit in a classical computer, your bit is actually random. You don't know what state it is, and then you have to reset it to a deterministic state. So you have a Maxwell's demon for every bit, for every clock cycle. I'm tired of paying the demon tax." "If you go with fractional bits, you don't need to do full bit operations. Running hardware probabilistically is just much more power efficient." "All the algorithms we want to run these days, whether it's PageRank, whether it's recommender systems, whether it's diffusion models, whether it's transformers, they're all probabilistic algorithms." "Why are we paying the Maxwell's demon tax to run probabilistic algorithms on deterministic hardware? It just makes zero sense, and we've just been caught in a local optima." "It takes a true set of autists that are like, screw the whole stack, we're going to start from scratch. This has the bones to eat the world." Extropic$

Extropic founder Beff (e/acc) on why running probabilistic AI on deterministic hardware is quietly the biggest local optimum in computing "You have Landauer's principle. Essentially you have a minimum cost of erasing a bit." "Every time you reset a bit in a classical computer, your bit is actually random. You don't know what state it is, and then you have to reset it to a deterministic state. So you have a Maxwell's demon for every bit, for every clock cycle. I'm tired of paying the demon tax." "If you go with fractional bits, you don't need to do full bit operations. Running hardware probabilistically is just much more power efficient." "All the algorithms we want to run these days, whether it's PageRank, whether it's recommender systems, whether it's diffusion models, whether it's transformers, they're all probabilistic algorithms." "Why are we paying the Maxwell's demon tax to run probabilistic algorithms on deterministic hardware? It just makes zero sense, and we've just been caught in a local optima." "It takes a true set of autists that are like, screw the whole stack, we're going to start from scratch. This has the bones to eat the world." Extropic

MTS

33,856 views • 3 months ago

Algorithms Are Colonising African Youths Nigerian historian Iyo Obietonbara argues that social media algorithms are a tool of colonialism, influencing what African youths consider valuable. Instead of building organisations to struggle for liberation, many of our young people are making videos about trivial matters. Do you agree? Disagree? Drop us a comment. For more like this, follow The Spearhead.

Algorithms Are Colonising African Youths Nigerian historian Iyo Obietonbara argues that social media algorithms are a tool of colonialism, influencing what African youths consider valuable. Instead of building organisations to struggle for liberation, many of our young people are making videos about trivial matters. Do you agree? Disagree? Drop us a comment. For more like this, follow The Spearhead.

The Spearhead

529,010 views • 4 months ago