Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

Holy shit… someone just made DSA finally click. Not static notes Not boring pseudocode Not guessing what happens in memory Real data structures — animating step-by-step — visually. It’s called Data Structure Visualizations and it lets you watch algorithms run in real time. Here’s why this is different: Instead...

Suryanshti777's profile picture

Suryansh Tiwari

44,282 subscribers

14,627 görüntüleme • 7 ay önce •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

4 Yorum

Olayiwola Akinnagbe profil fotoğrafı
Olayiwola Akinnagbe7 ay önce

This is very helpful, will check it out. Same concept here also, distributed systems concepts visualised from first principles. Raft Consensus Visualizer GitHub:

Chahid Chirchi profil fotoğrafı
Chahid Chirchi7 ay önce

dude that's genius, finally get DSA!! thx

A profil fotoğrafı
A7 ay önce

Its has been there from a long time

Manish Kumar profil fotoğrafı
Manish Kumar7 ay önce

Thanks for reminding about this.

Benzer Videolar

Holy shit… someone just made machine learning click. Not static diagrams. Not math-heavy PDFs. Not black-box training. Real algorithms — training step-by-step — visually. It’s called Machine Learning Visualized and it lets you watch models learn in real time. Here’s why this is different: Instead of dumping theory first, it shows optimization happening live: • gradients moving • weights updating • decision boundaries shifting • loss decreasing • models converging You literally see learning happen. Everything is built from first principles: • Gradient Descent • Logistic Regression • Perceptron • PCA • K-Means • Neural Networks • Backpropagation No magic. Just math → code → visualization. Each chapter is a Jupyter notebook that derives the math then implements it then animates training. So you can watch: • neural nets shape decision surfaces • PCA rotate feature space • K-means clusters form live • gradient descent find minima • sigmoid reshape boundaries • backprop update weights step-by-step This solves a huge problem: Most ML resources teach: math → code → ??? → trained model This shows: math → code → learning process → result Which means you finally understand: • why gradients matter • how weights evolve • what loss landscapes look like • how convergence actually happens • why deep nets learn non-linear functions Even better: You can open any notebook modify parameters and watch behavior change instantly. Learning ML becomes interactive. Not passive. Not abstract. Not confusing. Just… visible. Perfect for: • beginners learning ML • devs moving into AI • interview prep • teaching concepts • understanding backprop • visual learners • building intuition This is the kind of resource that makes neural networks finally “click”. Link: We’re moving from: reading about ML → watching ML learn That’s a big shift. Because once you can see training, you stop memorizing… and start understanding. AI education just got visual.
1:08

Holy shit… someone just made machine learning click. Not static diagrams. Not math-heavy PDFs. Not black-box training. Real algorithms — training step-by-step — visually. It’s called Machine Learning Visualized and it lets you watch models learn in real time. Here’s why this is different: Instead of dumping theory first, it shows optimization happening live: • gradients moving • weights updating • decision boundaries shifting • loss decreasing • models converging You literally see learning happen. Everything is built from first principles: • Gradient Descent • Logistic Regression • Perceptron • PCA • K-Means • Neural Networks • Backpropagation No magic. Just math → code → visualization. Each chapter is a Jupyter notebook that derives the math then implements it then animates training. So you can watch: • neural nets shape decision surfaces • PCA rotate feature space • K-means clusters form live • gradient descent find minima • sigmoid reshape boundaries • backprop update weights step-by-step This solves a huge problem: Most ML resources teach: math → code → ??? → trained model This shows: math → code → learning process → result Which means you finally understand: • why gradients matter • how weights evolve • what loss landscapes look like • how convergence actually happens • why deep nets learn non-linear functions Even better: You can open any notebook modify parameters and watch behavior change instantly. Learning ML becomes interactive. Not passive. Not abstract. Not confusing. Just… visible. Perfect for: • beginners learning ML • devs moving into AI • interview prep • teaching concepts • understanding backprop • visual learners • building intuition This is the kind of resource that makes neural networks finally “click”. Link: We’re moving from: reading about ML → watching ML learn That’s a big shift. Because once you can see training, you stop memorizing… and start understanding. AI education just got visual.

Suryansh Tiwari

132,906 görüntüleme • 7 ay önce

Staring at DSA problems and still not getting it..? lets you watch the solution happen step by step, index by index no more guessing... you literally see it
1:28

Staring at DSA problems and still not getting it..? lets you watch the solution happen step by step, index by index no more guessing... you literally see it

Vaishnavi

25,388 görüntüleme • 7 ay önce

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.
2:53

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.

Santiago

274,092 görüntüleme • 2 yıl önce

MIT 6.006 Introduction to Algorithms. nodes, pointers, and parent relationships in binary trees.
2:35

MIT 6.006 Introduction to Algorithms. nodes, pointers, and parent relationships in binary trees.

tetsuo

49,706 görüntüleme • 5 ay önce

Found this free site that lets you play with ML algorithms in real time Adjust a parameter.... the model updates instantly.... no code, no setup Link - neural networks, decision trees, SVM, k-means.... all interactive
1:44

Found this free site that lets you play with ML algorithms in real time Adjust a parameter.... the model updates instantly.... no code, no setup Link - neural networks, decision trees, SVM, k-means.... all interactive

Vaishnavi

25,380 görüntüleme • 7 ay önce

🚨 BRYAN JOHNSON: “We already live in a world run by algorithms. When you use Netflix, you’re not picking your content, the algorithm is for you. We don’t have free will. We’re already immeshed in this system of algorithms and we can’t see it. The irony is that we’re already this dystopia that we think is happening.”
1:30

🚨 BRYAN JOHNSON: “We already live in a world run by algorithms. When you use Netflix, you’re not picking your content, the algorithm is for you. We don’t have free will. We’re already immeshed in this system of algorithms and we can’t see it. The irony is that we’re already this dystopia that we think is happening.”

Autism Capital 🧩

186,577 görüntüleme • 1 yıl önce

Defensive Line Drills Explained Learn how to coach and apply D-line drills the right way, not just watch them on social media. Every drill is explained step by step so players, coaches, and parents understand what to look for, how to teach it, and how to get real results on the field. If you want better pass rush, stronger run defense, and a clear system for developing defensive linemen, this is where you start. Stop guessing and start training with purpose. Get My FREE Defensive Line Resources 👇
1:03:24

Defensive Line Drills Explained Learn how to coach and apply D-line drills the right way, not just watch them on social media. Every drill is explained step by step so players, coaches, and parents understand what to look for, how to teach it, and how to get real results on the field. If you want better pass rush, stronger run defense, and a clear system for developing defensive linemen, this is where you start. Stop guessing and start training with purpose. Get My FREE Defensive Line Resources 👇

Nicholas Dheilly (Dline Coach)

11,474 görüntüleme • 6 ay önce

Andrew Ng built agentic knowledge graph that runs agents loops from scratch: his agents searched thousands of tools through the graph, found the "right" one by meaning, and called it agent efficiency increased by 90% here's how: step 1 → embed every tool, search by meaning - not just keywords step 2 → but "similar" ≠ "correct" - it grabs the wrong one, skips the step before it step 3 → map tools as a graph - nodes are tools, edges are what runs before what step 4 → retrieve the path, not one tool - every tool the task needs, in order step 5 → bake the process into the graph - the sequence becomes data, not a guess most agents fail here and no one knows why - 1-hour brilliant course about it watch & bookmark - then read full course about graph engineering ↓
1:04:49

Andrew Ng built agentic knowledge graph that runs agents loops from scratch: his agents searched thousands of tools through the graph, found the "right" one by meaning, and called it agent efficiency increased by 90% here's how: step 1 → embed every tool, search by meaning - not just keywords step 2 → but "similar" ≠ "correct" - it grabs the wrong one, skips the step before it step 3 → map tools as a graph - nodes are tools, edges are what runs before what step 4 → retrieve the path, not one tool - every tool the task needs, in order step 5 → bake the process into the graph - the sequence becomes data, not a guess most agents fail here and no one knows why - 1-hour brilliant course about it watch & bookmark - then read full course about graph engineering ↓

codila

17,040 görüntüleme • 3 ay önce

This charlatan who’s never step foot in Iraq, he uploads a highway, where they’ve planted a few trees in the mid-section - not the neighbourhoods where people live. Here’s footage just behind the tree planted highway and of how run down Fallujah is.
1:59

This charlatan who’s never step foot in Iraq, he uploads a highway, where they’ve planted a few trees in the mid-section - not the neighbourhoods where people live. Here’s footage just behind the tree planted highway and of how run down Fallujah is.

Disputing Sunnah

15,312 görüntüleme • 1 yıl önce

Infrastructure defines what scales. Electricity enabled industry. The internet enabled software. Now AI is moving toward systems that run continuously, across data, execution, and feedback. 💡At OptimAI, we’re building that infrastructure. A decentralized search layer for real-time data. A network of nodes powering continuous retrieval and validation. Agents that execute tasks directly on your device. This is not theoretical - we’re already seeing it in production, with millions of nodes and growing usage across the network. We’re putting together a short documentary to show how this shift is happening, and where it leads next. If you’re building, exploring, or just getting started - this is the layer to pay attention to. Welcome to OptimAI.
2:02

Infrastructure defines what scales. Electricity enabled industry. The internet enabled software. Now AI is moving toward systems that run continuously, across data, execution, and feedback. 💡At OptimAI, we’re building that infrastructure. A decentralized search layer for real-time data. A network of nodes powering continuous retrieval and validation. Agents that execute tasks directly on your device. This is not theoretical - we’re already seeing it in production, with millions of nodes and growing usage across the network. We’re putting together a short documentary to show how this shift is happening, and where it leads next. If you’re building, exploring, or just getting started - this is the layer to pay attention to. Welcome to OptimAI.

OptimAI Network

17,506 görüntüleme • 6 ay önce

Need to get an impacted wisdom tooth removed? It's really not as scary as you think! This step- by-step animation shows how precision tools and modern techniques make the extraction process safe and highly efficient. Understanding the procedure is the
0:35

Need to get an impacted wisdom tooth removed? It's really not as scary as you think! This step- by-step animation shows how precision tools and modern techniques make the extraction process safe and highly efficient. Understanding the procedure is the

Teta Ella

57,763 görüntüleme • 4 ay önce

A DEVELOPER WALKED ON STAGE DRESSED AS A 1973 ENGINEER AND "PREDICTED" THE FUTURE OF PROGRAMMING. THE TWIST: EVERYTHING HE DESCRIBED WAS ALREADY INVENTED 40 YEARS EARLIER AND WE STILL REFUSE TO USE IT. 32 minutes from Bret Victor, doing the most quietly savage talk on our entire industry. -> The idea that lands: we write code as step-by-step text instructions and call that "Just how programming is". He shows four better ways -- all discovered in the 60s and 70s, all abandoned. Manipulate the data directly instead of typing blind code. Tell the machine your goal instead of every tiny step. We saw all this, then walked away. Why? The moment you're sure you know what programming is, you stop seeing anything better. That certainty is the cage. And now AI is dragging us back to exactly what he begged for -- you describe the goal in plain words, the machine works out the how. The future he mourned is arriving anyway. You thought text files were just how code works. This is the talk that shows it was a choice, and maybe the wrong one. Watch this one. It'll ruin how you see your job ↓
32:29

A DEVELOPER WALKED ON STAGE DRESSED AS A 1973 ENGINEER AND "PREDICTED" THE FUTURE OF PROGRAMMING. THE TWIST: EVERYTHING HE DESCRIBED WAS ALREADY INVENTED 40 YEARS EARLIER AND WE STILL REFUSE TO USE IT. 32 minutes from Bret Victor, doing the most quietly savage talk on our entire industry. -> The idea that lands: we write code as step-by-step text instructions and call that "Just how programming is". He shows four better ways -- all discovered in the 60s and 70s, all abandoned. Manipulate the data directly instead of typing blind code. Tell the machine your goal instead of every tiny step. We saw all this, then walked away. Why? The moment you're sure you know what programming is, you stop seeing anything better. That certainty is the cage. And now AI is dragging us back to exactly what he begged for -- you describe the goal in plain words, the machine works out the how. The future he mourned is arriving anyway. You thought text files were just how code works. This is the talk that shows it was a choice, and maybe the wrong one. Watch this one. It'll ruin how you see your job ↓

slash1s

688,900 görüntüleme • 4 ay önce

If you're having a hard time understanding what's happening in France right now, just watch this clip from Lioness. It articulates this situation perfectly. We're watching a 50-year war on our institutions, driven by the Red-Green alliance using an army of "useful idiots" to tear down the West from within. THIS IS WHAT THEY ALWAYS DO. France is just showing us what the end of that road looks like. America, look at the upcoming midterms. From Senate seats to local school boards, the DSA is executing this blueprint step-by-step. France is what happens when you let them in. We stop them at the ballot box now, or we're next.
3:52

If you're having a hard time understanding what's happening in France right now, just watch this clip from Lioness. It articulates this situation perfectly. We're watching a 50-year war on our institutions, driven by the Red-Green alliance using an army of "useful idiots" to tear down the West from within. THIS IS WHAT THEY ALWAYS DO. France is just showing us what the end of that road looks like. America, look at the upcoming midterms. From Senate seats to local school boards, the DSA is executing this blueprint step-by-step. France is what happens when you let them in. We stop them at the ballot box now, or we're next.

Glenn Beck

430,883 görüntüleme • 29 gün önce

Head of Claude Code: "I'm not prompting my agents anymore, I'm building graphs and loops so they can build the agents for me." In 10 minutes he shows exactly where agentic engineering is going and how not to get left behind. Definitely something you cannot afford to skip. Watch it, then read the step-by-step guide below on how to build a system that improves itself.
10:55

Head of Claude Code: "I'm not prompting my agents anymore, I'm building graphs and loops so they can build the agents for me." In 10 minutes he shows exactly where agentic engineering is going and how not to get left behind. Definitely something you cannot afford to skip. Watch it, then read the step-by-step guide below on how to build a system that improves itself.

Anatoli Kopadze

383,261 görüntüleme • 2 ay önce

Head of Claude Code: "I'm not prompting my agents anymore, I'm building graphs and loops so they can build the agents for me." In 10 minutes he shows exactly where agentic engineering is going and how not to get left behind. Definitely something you cannot afford to skip. Watch it, then read the step-by-step guide below on how to build a system that improves itself.
11:10

Head of Claude Code: "I'm not prompting my agents anymore, I'm building graphs and loops so they can build the agents for me." In 10 minutes he shows exactly where agentic engineering is going and how not to get left behind. Definitely something you cannot afford to skip. Watch it, then read the step-by-step guide below on how to build a system that improves itself.

Anatoli Kopadze

609,685 görüntüleme • 2 ay önce

🚨Breaking: Google just dropped a new Guided Learning feature in Gemini. Perfect for students instead of just giving answers, it actually teaches. You can get images, diagrams, videos, and quizzes to learn step by step and see how much you’ve understood.
0:36

🚨Breaking: Google just dropped a new Guided Learning feature in Gemini. Perfect for students instead of just giving answers, it actually teaches. You can get images, diagrams, videos, and quizzes to learn step by step and see how much you’ve understood.

AshutoshShrivastava

79,363 görüntüleme • 1 yıl önce

• watch algorithms train step-by-step • see gradients, weights, decision boundaries evolve • math + visuals + code in one place
1:08

• watch algorithms train step-by-step • see gradients, weights, decision boundaries evolve • math + visuals + code in one place

Vaishnavi

39,971 görüntüleme • 7 ay önce

Nothing is real anymore. These are not real people. Here's how I created this AI memory reel... Step by step + PROMPT
0:41

Nothing is real anymore. These are not real people. Here's how I created this AI memory reel... Step by step + PROMPT

Min Choi

172,945 görüntüleme • 6 ay önce