正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

Karpathy didn't make a course. He made THE course. 3 hours. Free. Tokenization. Attention. Hallucinations. Tool use. RLHF. DeepSeek. AlphaGo. Every behavior you've ever wondered about in an LLM - where it comes from, why it exists, how it was engineered. The gap between engineers who understand this and... show more

Rahul

142,080 subscribers

633,449 次观看 • 4 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Andrej Karpathy could have charged $10,000 for this course. He put it on YouTube. The man who built Tesla Autopilot from scratch. Co-founded OpenAI. Understands AI at a level most engineers at Google and Meta never reach. Sat down. Recorded 2 hours. No frameworks. No libraries. No shortcuts. Then dropped it for free. The gap between people who watch it this week and those who save it for later is not 2 hours. It is everything those 2 hours quietly unlock for the rest of your career.

Andrej Karpathy could have charged $10,000 for this course. He put it on YouTube. The man who built Tesla Autopilot from scratch. Co-founded OpenAI. Understands AI at a level most engineers at Google and Meta never reach. Sat down. Recorded 2 hours. No frameworks. No libraries. No shortcuts. Then dropped it for free. The gap between people who watch it this week and those who save it for later is not 2 hours. It is everything those 2 hours quietly unlock for the rest of your career.

NeilXbt

524,342 次观看 • 4 个月前

Andrej Karpathy just built GPT-2 from scratch on camera! Every line. Every component. Every optimization decision. Transformer blocks. Self-attention. MLP. Mixed precision. Flash Attention. AdamW. Distributed training across GPUs. 4 hours. Free. Not a lecture about how GPT-2 was built. The actual build. Running. Training. Evaluated against the original OpenAI checkpoints. The engineers who watch this do not just understand language models. They understand them at the level required to build the next one. The gap between using AI and being capable of building it is not a PhD program. It is 4 hours with the person who makes this look like something any determined engineer can do.

Andrej Karpathy just built GPT-2 from scratch on camera! Every line. Every component. Every optimization decision. Transformer blocks. Self-attention. MLP. Mixed precision. Flash Attention. AdamW. Distributed training across GPUs. 4 hours. Free. Not a lecture about how GPT-2 was built. The actual build. Running. Training. Evaluated against the original OpenAI checkpoints. The engineers who watch this do not just understand language models. They understand them at the level required to build the next one. The gap between using AI and being capable of building it is not a PhD program. It is 4 hours with the person who makes this look like something any determined engineer can do.

NeilXbt

37,778 次观看 • 4 个月前

THE GUY WHO BUILT GIT GAVE A FULL LECTURE AT GOOGLE BECAUSE 99% OF DEVELOPERS USE IT WRONG 70 minutes of pure firepower from Linus Torvalds himself - the man who wrote both Linux and Git from scratch. -> The moment you watch it, you realize why "I'll just figure out merge conflicts on the job" is the dumbest sentence in modern software engineering. He spends the first 10 minutes calling out every other version control system in existence and he's not joking. He literally tells Google engineers using Perforce to find a new tool. Git isn't a "nice-to-have" anymore -> it's the operating system of every codebase you'll ever touch. If the man who built it had to explain it to a room full of Google engineers, you don't get a pass either. Don't forget to bookmark & watch it today.

THE GUY WHO BUILT GIT GAVE A FULL LECTURE AT GOOGLE BECAUSE 99% OF DEVELOPERS USE IT WRONG 70 minutes of pure firepower from Linus Torvalds himself - the man who wrote both Linux and Git from scratch. -> The moment you watch it, you realize why "I'll just figure out merge conflicts on the job" is the dumbest sentence in modern software engineering. He spends the first 10 minutes calling out every other version control system in existence and he's not joking. He literally tells Google engineers using Perforce to find a new tool. Git isn't a "nice-to-have" anymore -> it's the operating system of every codebase you'll ever touch. If the man who built it had to explain it to a room full of Google engineers, you don't get a pass either. Don't forget to bookmark & watch it today.

slash1s

214,967 次观看 • 3 个月前

🚨 OpenAI 's own engineers just showed how to actually use OpenAI Codex properly. 60 minutes. free. built by the people who contribute to made it. watch the masterclass. bookmark it. worth more than every $900 coding course you almost bought. you’ve been using Codex like a simple coding tool… while it’s actually a full software engineering system. watch this, it could the best 62 minutes of your life:

🚨 OpenAI 's own engineers just showed how to actually use OpenAI Codex properly. 60 minutes. free. built by the people who contribute to made it. watch the masterclass. bookmark it. worth more than every $900 coding course you almost bought. you’ve been using Codex like a simple coding tool… while it’s actually a full software engineering system. watch this, it could the best 62 minutes of your life:

ZARA

298,335 次观看 • 4 个月前

Don't waste 2 years learning to become an AI researcher in 2026. This is the exact 3 hour course on the math & architecture behind every LLM. • 00:00 - what an AI researcher does • 46:07 - the math behind LLMs • 1:54:00 - neural networks from scratch • 2:14:14 - LLM attention mechanism • 3:04:15 - LLM tokenization explained Anthropic pays $850,000/year to AI researchers who master this exact knowledge. Bookmark & give it 3 hours, no matter what. It will be the most productive thing you do this weekend.

Don't waste 2 years learning to become an AI researcher in 2026. This is the exact 3 hour course on the math & architecture behind every LLM. • 00:00 - what an AI researcher does • 46:07 - the math behind LLMs • 1:54:00 - neural networks from scratch • 2:14:14 - LLM attention mechanism • 3:04:15 - LLM tokenization explained Anthropic pays $850,000/year to AI researchers who master this exact knowledge. Bookmark & give it 3 hours, no matter what. It will be the most productive thing you do this weekend.

Roan

87,964 次观看 • 26 天前

Don't waste 2 years learning to build LLMs like Claude & ChatGPT. MIT dropped a 3 hour course on building LLMs from scratch for free. Training → Architecture → Scale. It covers everything about LLMs. • 00:00 - training an LLM • 1:20:46 - LLM architecture from scratch • 2:34:56 - scaling laws for LLMs Anthropic pays $750,000/year to AI engineers in 2026 who master this exact knowledge. Bookmark this & skip Netflix. Give it 4 hours today. Then read the article below.

Don't waste 2 years learning to build LLMs like Claude & ChatGPT. MIT dropped a 3 hour course on building LLMs from scratch for free. Training → Architecture → Scale. It covers everything about LLMs. • 00:00 - training an LLM • 1:20:46 - LLM architecture from scratch • 2:34:56 - scaling laws for LLMs Anthropic pays $750,000/year to AI engineers in 2026 who master this exact knowledge. Bookmark this & skip Netflix. Give it 4 hours today. Then read the article below.

Roan

85,618 次观看 • 23 天前

MIT HANDED ITS DEEP LEARNING COURSE TO A FRONTIER-LAB ENGINEER FOR 68 MINUTES BECAUSE 90% OF PEOPLE SHIPPING AI CODE CAN'T EXPLAIN HOW THE MODEL ACTUALLY WORKS This is Maxime Labonne. He runs post-training at Liquid AI and wrote the LLM Engineer's Handbook. MIT gave him the room to break down the engine sitting inside every coding agent you've ever prompted. Twenty minutes in it stops being abstract. You finally see why the model confidently invents things that don't exist, why context is everything, and why "Just tell it to try again" sometimes fixes it and sometimes makes it worse. In 2026 "I use AI to code" stopped being a skill. Knowing why the model behaves the way it does -> tokens, context, post-training, where it quietly breaks -> is what separates someone who ships from someone babysitting a black box. Understanding an LLM isn't a research-team luxury anymore -> it's the difference between driving the agent and being driven by it. Anyone can prompt. The person who knows what's under the hood is the one still standing when the prompt stops working. Save this one & actually finish it ↓

MIT HANDED ITS DEEP LEARNING COURSE TO A FRONTIER-LAB ENGINEER FOR 68 MINUTES BECAUSE 90% OF PEOPLE SHIPPING AI CODE CAN'T EXPLAIN HOW THE MODEL ACTUALLY WORKS This is Maxime Labonne. He runs post-training at Liquid AI and wrote the LLM Engineer's Handbook. MIT gave him the room to break down the engine sitting inside every coding agent you've ever prompted. Twenty minutes in it stops being abstract. You finally see why the model confidently invents things that don't exist, why context is everything, and why "Just tell it to try again" sometimes fixes it and sometimes makes it worse. In 2026 "I use AI to code" stopped being a skill. Knowing why the model behaves the way it does -> tokens, context, post-training, where it quietly breaks -> is what separates someone who ships from someone babysitting a black box. Understanding an LLM isn't a research-team luxury anymore -> it's the difference between driving the agent and being driven by it. Anyone can prompt. The person who knows what's under the hood is the one still standing when the prompt stops working. Save this one & actually finish it ↓

slash1s

236,758 次观看 • 3 个月前

Stanford just dropped a 1 hour course on how it teaches its AI engineers to train LLMs from scratch. This is the exact way LLMs like ChatGPT & Claude are trained. • 00:00 - LLM Policy Gradient basics • 23:24 - PPO for LLM training • 56:20 - how LLMs learn Chain of Thought • 1:03:50- LLM architecture explained Anthropic pays $750,000/year to engineers who understand this exact knowledge of LLMs. Bookmark this & give 1 hours today, no matter what. It'll be the most productive thing you do this week.

Stanford just dropped a 1 hour course on how it teaches its AI engineers to train LLMs from scratch. This is the exact way LLMs like ChatGPT & Claude are trained. • 00:00 - LLM Policy Gradient basics • 23:24 - PPO for LLM training • 56:20 - how LLMs learn Chain of Thought • 1:03:50- LLM architecture explained Anthropic pays $750,000/year to engineers who understand this exact knowledge of LLMs. Bookmark this & give 1 hours today, no matter what. It'll be the most productive thing you do this week.

Roan

37,436 次观看 • 1 个月前

Anthropic engineer: "The fastest way to make an agent better isn't a smarter model, it's the right loop and graph around it." In 60 minutes she breaks down how Anthropic engineers use Claude Code, build agents that catch their own mistakes and improve with every run. This beats any $500 agentic engineering course you can find. Watch it, then read the full guide on loops and graphs below.

Anthropic engineer: "The fastest way to make an agent better isn't a smarter model, it's the right loop and graph around it." In 60 minutes she breaks down how Anthropic engineers use Claude Code, build agents that catch their own mistakes and improve with every run. This beats any $500 agentic engineering course you can find. Watch it, then read the full guide on loops and graphs below.

Anatoli Kopadze

135,011 次观看 • 23 天前

New short course: Attention in Transformers: Concepts and Code in PyTorch. Last week we released a course on how LLM transformers work. This week, go deeper and learn about the technical ideas behind the attention mechanism, and see how to code it in PyTorch. This course is built with Joshua Starmer, Founder and CEO of StatQuest. The attention mechanism was a breakthrough that led to transformers, the architecture powering large language models like ChatGPT. Transformers, introduced in the 2017 paper: "Attention is All You Need" by Viswani and others, took off because of its highly scalable design. In this course, you’ll learn how the attention mechanism, a key element of transformer-based LLMs, works and implement it in PyTorch. You'll develop deep intuition about building reliable, functional, and scalable AI applications. What you will do: - Understand the evolution of the attention mechanism, a key breakthrough that led to transformers. - Learn the relationships between word embeddings, positional embeddings, and attention. - Learn about the Query, Key, and Value matrices, and how to produce and use them in attention. - Walk through the math required to calculate self-attention and masked self-attention to learn why and how they work. - Understand the difference between self-attention and masked self-attention and how one is used in the encoder to build context-aware embeddings and the other is used in the decoder for generative outputs. - Learn the details of the encoder-decoder architecture, cross-attention, and multi-head attention and how they are all incorporated into a transformer. - Use PyTorch to code a class that implements self-attention, masked self-attention, and multi-head attention. There're lots of exciting technical details in this course. Please sign up here:

New short course: Attention in Transformers: Concepts and Code in PyTorch. Last week we released a course on how LLM transformers work. This week, go deeper and learn about the technical ideas behind the attention mechanism, and see how to code it in PyTorch. This course is built with Joshua Starmer, Founder and CEO of StatQuest. The attention mechanism was a breakthrough that led to transformers, the architecture powering large language models like ChatGPT. Transformers, introduced in the 2017 paper: "Attention is All You Need" by Viswani and others, took off because of its highly scalable design. In this course, you’ll learn how the attention mechanism, a key element of transformer-based LLMs, works and implement it in PyTorch. You'll develop deep intuition about building reliable, functional, and scalable AI applications. What you will do: - Understand the evolution of the attention mechanism, a key breakthrough that led to transformers. - Learn the relationships between word embeddings, positional embeddings, and attention. - Learn about the Query, Key, and Value matrices, and how to produce and use them in attention. - Walk through the math required to calculate self-attention and masked self-attention to learn why and how they work. - Understand the difference between self-attention and masked self-attention and how one is used in the encoder to build context-aware embeddings and the other is used in the decoder for generative outputs. - Learn the details of the encoder-decoder architecture, cross-attention, and multi-head attention and how they are all incorporated into a transformer. - Use PyTorch to code a class that implements self-attention, masked self-attention, and multi-head attention. There're lots of exciting technical details in this course. Please sign up here:

Andrew Ng

132,400 次观看 • 1 年前

Don't waste 2 years learning to use LLMs like Claude & ChatGPT. Andrej Karpathy, the godfather of AI, dropped a 2 hour course on how he personally uses LLMs daily. • 00:00 - LLMs simplified • 22:49 - which LLM to actually use • 42:00 - one prompt for full research • 1:13:57 - how to code with LLMs • 1:37:04 - NotebookLM podcast generation This course will teach you more about using LLMs than most AI engineers learn in their entire career. Bookmark this & give 2 hours today, no matter what. Then read the article below.

Don't waste 2 years learning to use LLMs like Claude & ChatGPT. Andrej Karpathy, the godfather of AI, dropped a 2 hour course on how he personally uses LLMs daily. • 00:00 - LLMs simplified • 22:49 - which LLM to actually use • 42:00 - one prompt for full research • 1:13:57 - how to code with LLMs • 1:37:04 - NotebookLM podcast generation This course will teach you more about using LLMs than most AI engineers learn in their entire career. Bookmark this & give 2 hours today, no matter what. Then read the article below.

Roan

56,500 次观看 • 27 天前

Met a guy making $1.6 million/year as an LLM engineer. I asked him how he learned LLMs from scratch. He sent me the exact video that got him in. A 1 hour course on how LLMs actually work. He shows how transformers inside LLMs like ChatGPT & Claude are actually built. I watched it last night. Halfway through, I realized LLM architecture is way simpler than they make it look. Bookmark this and read the article below. • 00:00 - LLM foundations • 04:21 - LLM tokenization • 05:43 - LLMs vector embeddings • 22:16 - attention mechanism of LLM • 43:42 - LLM multi head attention

Met a guy making $1.6 million/year as an LLM engineer. I asked him how he learned LLMs from scratch. He sent me the exact video that got him in. A 1 hour course on how LLMs actually work. He shows how transformers inside LLMs like ChatGPT & Claude are actually built. I watched it last night. Halfway through, I realized LLM architecture is way simpler than they make it look. Bookmark this and read the article below. • 00:00 - LLM foundations • 04:21 - LLM tokenization • 05:43 - LLMs vector embeddings • 22:16 - attention mechanism of LLM • 43:42 - LLM multi head attention

Roan

80,961 次观看 • 1 个月前

Anthropic pays its ML engineers $500K+/year to deploy what Percy Liang just taught for free in Lecture 3 of CS221. 80 minutes. one whiteboard. everything between "a neuron" and the model running in your IDE right now. three things click after this one: / why two Claude prompts that "mean the same thing" land in different regions of weight space / why fine-tuning sometimes makes a model dumber, not smarter / what RLHF actually changes when Anthropic aligns Claude same $90K Stanford sequence. lecture 3 of ~20. free. the gap between "using Claude" and "understanding it" is exactly one lecture wide.

Anthropic pays its ML engineers $500K+/year to deploy what Percy Liang just taught for free in Lecture 3 of CS221. 80 minutes. one whiteboard. everything between "a neuron" and the model running in your IDE right now. three things click after this one: / why two Claude prompts that "mean the same thing" land in different regions of weight space / why fine-tuning sometimes makes a model dumber, not smarter / what RLHF actually changes when Anthropic aligns Claude same $90K Stanford sequence. lecture 3 of ~20. free. the gap between "using Claude" and "understanding it" is exactly one lecture wide.

dunik

24,391 次观看 • 3 个月前

🐱 Rap for us! That K'Kornnaphat! Where did you get that? I have nothing to do with it. 👩‍🦱 Yea. Have you two cleared things up yet? 🐱 Clear it up! 👩‍🦱 Where was K'Kornnaphat from? You changed the lyrics. 🐱 Did you realize that the lyrics had Kornnaphat? 🐶 Of course! I was the one who sang it. 👩‍🦱 Did you know that it isn't in the original lyrics. 🐶 Of course! 🐱 Really? 👩‍🦱 Who added it, you or the producer? 🐶 Producer! 🐱 The producer added it, for real! 👩‍🦱 But you're the one who sang it? 🐶 I was the one who sang it. 👩‍🦱 Like, you're totally into it. 🐶 I don't know how to add it. I don't know. I can't get the beat right. 👩‍🦱 But you added from your inner. 🐶 Yes. The beat fits just right. 👩‍🦱 You've never talked about it anywhere else? 🐶 Never kub. 👩‍🦱 So, now everyone knows where it came from. Whoever added it, but I sang it! 🐶 RIGHT! #วาดฝันวันวิวาห์ #InLoveForeverTheSeries #LingOrm #หลิงออม

🐱 Rap for us! That K'Kornnaphat! Where did you get that? I have nothing to do with it. 👩‍🦱 Yea. Have you two cleared things up yet? 🐱 Clear it up! 👩‍🦱 Where was K'Kornnaphat from? You changed the lyrics. 🐱 Did you realize that the lyrics had Kornnaphat? 🐶 Of course! I was the one who sang it. 👩‍🦱 Did you know that it isn't in the original lyrics. 🐶 Of course! 🐱 Really? 👩‍🦱 Who added it, you or the producer? 🐶 Producer! 🐱 The producer added it, for real! 👩‍🦱 But you're the one who sang it? 🐶 I was the one who sang it. 👩‍🦱 Like, you're totally into it. 🐶 I don't know how to add it. I don't know. I can't get the beat right. 👩‍🦱 But you added from your inner. 🐶 Yes. The beat fits just right. 👩‍🦱 You've never talked about it anywhere else? 🐶 Never kub. 👩‍🦱 So, now everyone knows where it came from. Whoever added it, but I sang it! 🐶 RIGHT! #วาดฝันวันวิวาห์ #InLoveForeverTheSeries #LingOrm #หลิงออม

Mnz 'ภีรชน์

25,545 次观看 • 6 天前

When this video was first texted to me, I assumed it was from Iran. The way she screams, she was wearing a hijab, and the way the law enforcement approached her it looked no different from the way Islamic Republic authorities arrest women. It's from Massachusetts, of course. There's no reason to detain someone in this manner who isn't a danger to the public. None at all.

When this video was first texted to me, I assumed it was from Iran. The way she screams, she was wearing a hijab, and the way the law enforcement approached her it looked no different from the way Islamic Republic authorities arrest women. It's from Massachusetts, of course. There's no reason to detain someone in this manner who isn't a danger to the public. None at all.

Yashar Ali 🐘

327,617 次观看 • 1 年前

New short course on Reinforcement Learning from Human Feedback! RLHF is one of the key techniques that led to the rise of modern LLMs. It is used to align LLMs with human preferences, to make them more honest, helpful and harmless, by (i) learning a reward function that mimics human preferences, as expressed in human-provided labels, then, (ii) tuning an LLM to generate outputs that receive a high reward. In this course, taught by Nikita Namjoshi, Developer Advocate for GenAI at Google Cloud, you'll learn the details of how RLHF works, including how to apply it to tune an LLM for your own applications. You'll also use an open source library to tune a base LLM to align with human preferences expressed in a training set, and evaluate the tuned model by comparing its responses before and after RLHF-tuning. Please sign up here!

New short course on Reinforcement Learning from Human Feedback! RLHF is one of the key techniques that led to the rise of modern LLMs. It is used to align LLMs with human preferences, to make them more honest, helpful and harmless, by (i) learning a reward function that mimics human preferences, as expressed in human-provided labels, then, (ii) tuning an LLM to generate outputs that receive a high reward. In this course, taught by Nikita Namjoshi, Developer Advocate for GenAI at Google Cloud, you'll learn the details of how RLHF works, including how to apply it to tune an LLM for your own applications. You'll also use an open source library to tune a base LLM to align with human preferences expressed in a training set, and evaluate the tuned model by comparing its responses before and after RLHF-tuning. Please sign up here!

Andrew Ng

205,569 次观看 • 2 年前

3-HOUR PROMPTING COURSE FROM ANTHROPIC. The internal course used to train Claude's own team. I got the full recording. Free below: 00:15 - How $500k Engineers Prompt Claude 33:21 - The Rules Anthropic Never Published 1:33:39 - The Prompt That Replaces Coding 2:11:51 - Make Claude Prompt Itself Delete every prompting thread you've saved. This replaces all of them. Watch it today. Then read the guide below and build your first loop.

3-HOUR PROMPTING COURSE FROM ANTHROPIC. The internal course used to train Claude's own team. I got the full recording. Free below: 00:15 - How $500k Engineers Prompt Claude 33:21 - The Rules Anthropic Never Published 1:33:39 - The Prompt That Replaces Coding 2:11:51 - Make Claude Prompt Itself Delete every prompting thread you've saved. This replaces all of them. Watch it today. Then read the guide below and build your first loop.

Raytar

15,420 次观看 • 1 个月前

Sam Altman: "One person who knows how to use AI can do a week of work in 20 minutes." In 30 minutes he breaks down exactly what those people do differently, and how you can too. The gap isn't between AI and humans. It's between people who know AI and people who don't. Watch it, then read what knowing AI can actually do below

Sam Altman: "One person who knows how to use AI can do a week of work in 20 minutes." In 30 minutes he breaks down exactly what those people do differently, and how you can too. The gap isn't between AI and humans. It's between people who know AI and people who don't. Watch it, then read what knowing AI can actually do below

Mahax

17,829 次观看 • 16 天前

IN 1999 MIT FILMED A MATH LECTURE THAT QUIETLY BECAME THE FOUNDATION OF EVERY AI MODEL YOU'VE EVER USED AND ALMOST NO ONE WAS TAUGHT TO SEE IT THAT WAY 39 minutes from Gilbert Strang, who taught this at MIT for over 60 years -- the linear algebra course an entire generation of engineers and data scientists grew up on. -> The shift it creates: you stop seeing matrices as boring grids of numbers and start seeing them as the language of space, data, and motion itself. School drilled you to crunch matrices by hand and never told you why. Strang shows you what they actually mean. Every neural net, every embedding, every model you prompt is linear algebra running underneath. The math you skipped is the engine of the thing you use all day. Memorizing the steps was never the skill -> seeing what the numbers do is. This is where it finally clicks. Most people fear linear algebra and move on. The ones who watched this see straight into how AI actually works. Bookmark & Watch it today, this one's a legend ↓

IN 1999 MIT FILMED A MATH LECTURE THAT QUIETLY BECAME THE FOUNDATION OF EVERY AI MODEL YOU'VE EVER USED AND ALMOST NO ONE WAS TAUGHT TO SEE IT THAT WAY 39 minutes from Gilbert Strang, who taught this at MIT for over 60 years -- the linear algebra course an entire generation of engineers and data scientists grew up on. -> The shift it creates: you stop seeing matrices as boring grids of numbers and start seeing them as the language of space, data, and motion itself. School drilled you to crunch matrices by hand and never told you why. Strang shows you what they actually mean. Every neural net, every embedding, every model you prompt is linear algebra running underneath. The math you skipped is the engine of the thing you use all day. Memorizing the steps was never the skill -> seeing what the numbers do is. This is where it finally clicks. Most people fear linear algebra and move on. The ones who watched this see straight into how AI actually works. Bookmark & Watch it today, this one's a legend ↓

slash1s

433,431 次观看 • 2 个月前

MIT filmed the single hour that makes all of calculus one subject instead of two. Free, uncut, chalk only. Almost nobody watches it. This is 18.01, Single Variable Calculus, lecture 19, taught by David Jerison. The First Fundamental Theorem of Calculus. Slopes and areas were separate problems for centuries. Different tools, different people, different questions. This lecture shows they were the same thing pointed in opposite directions. Every gradient computed in every training run rides on that connection. Most engineers use it daily and have never seen the proof that makes it legal. Watch the board work in the middle. He builds the link line by line, real students in the room, no software, no animation. A machine learning engineer I know rewatched it before an interview and said the chain from loss to update finally read as 1 object instead of 3 tricks. Full course free on YouTube under Creative Commons. You've been using the theorem. You've never seen it earned.

MIT filmed the single hour that makes all of calculus one subject instead of two. Free, uncut, chalk only. Almost nobody watches it. This is 18.01, Single Variable Calculus, lecture 19, taught by David Jerison. The First Fundamental Theorem of Calculus. Slopes and areas were separate problems for centuries. Different tools, different people, different questions. This lecture shows they were the same thing pointed in opposite directions. Every gradient computed in every training run rides on that connection. Most engineers use it daily and have never seen the proof that makes it legal. Watch the board work in the middle. He builds the link line by line, real students in the room, no software, no animation. A machine learning engineer I know rewatched it before an interview and said the chain from loss to update finally read as 1 object instead of 3 tricks. Full course free on YouTube under Creative Commons. You've been using the theorem. You've never seen it earned.

obssnnn

124,305 次观看 • 1 个月前