Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

Deep-Path: The entire Deep-ML catalog, organized into one structured learning path featuring nearly 2,000 machine learning coding problems and hands-on projects. Throughout the path, you'll work through the math, implement concepts from scratch using minimal libraries like NumPy or CUDA, and build projects step by step There's also Deep-Path... show more

Deep-ML

23,005 subscribers

62,778 views • 4 days ago •via X (Twitter)

Health & Wellness Education Science & Technology

Anya Rossi• Live Now

Private livecam show

20 Comments

SamSepiol4 days ago

The path is long but it’s really fun 🤩

Deep-ML4 days ago

Assuming most people will work on the essentials path but the full one is an option if you really want to understand one part really well

SamSepiol4 days ago

Absolutely i love deep-mL especially the projects and interview path , it’s closely what i find in interview questions

Deep-ML4 days ago

Glad to hear you like the site. Let me know if you have any interviews coming up, and I could try making an interview prep collection for that company

infy4 days ago

would be great if you could implement some sort of spaced repetition feature

Deep-ML4 days ago

What do you mean by that?

Boardy4 days ago

the treasure is 1984 steps away

Raka4 days ago

looks so good

Ian P4 days ago

Really cool way of showing the curriculum, caught my eye. I'll take a look into it and give it a try!

Deep-ML4 days ago

Thanks, let me know how you like it!

Prashant4 days ago

❤️❤️

Daniel4 days ago

My goal is to understand AI from the fundamentals, eventually read papers and train models. I work mostly on systems, and infra & devops. Tried in 2022 but couldn't finish (life). Can I jump straight into Deep-ML and fill the gaps as I go, or should I 1/2

michaelvu4 days ago

Definitely take a look

Adolf.4 days ago

I'm interested to know if I can move to this after learning python

Deep-ML4 days ago

Yes but it will be a learning through fire, you will get stuck and need to use chat or the AI tutor to get unstuck I think learning through solving is the best way to learn but it is difficult at times

Adolf.4 days ago

Can you explain better, pls

Deep-ML4 days ago

If you know Python but nothing about machine learning, you can absolutely start learning by solving problems. You’ll probably get stuck on certain concepts or wonder why something matters. That’s where AI can be really helpful. Use it to understand the bigger picture, explain unfamiliar ideas, or guide you when you’re stuck. I think problem-based learning is one of the best ways to learn because it forces you to actually understand what you’re doing. When you watch a video or read something, it’s easy to feel like you understand a concept. But when you try to implement it yourself, you quickly find out what you actually know and what you don’t.

Adolf.4 days ago

I will try it, thanks for your response

🍀🃏4 days ago

This is cool

AA4 days ago

@praveeen_si

Related Videos

Interested in getting hands-on with Newton? The newly released Newton Learning Path will help you set up a ready-to-run environment, dive into Newton abstractions, and show you how to build your first simulation. Get started with learning path today:

Interested in getting hands-on with Newton? The newly released Newton Learning Path will help you set up a ready-to-run environment, dive into Newton abstractions, and show you how to build your first simulation. Get started with learning path today:

NVIDIA Robotics

10,507 views • 1 month ago

📝 AI/ML for Biology & Healthcare: A Learning Path This blog is highly inspired by my experience and exploration in the field of ML for biology and healthcare. Everything that I've been studying for the past years and a more structured way to learn the foundational knowledge to tackle problems in this impactful field. Here's what I covered in the blog: — Programming & Engineering — AI & Machine Learning — Mathematics for ML — Healthcare — Biology & Biolomedicine This is what I hoped to have when I first started learning all this. 🔗 link:

📝 AI/ML for Biology & Healthcare: A Learning Path This blog is highly inspired by my experience and exploration in the field of ML for biology and healthcare. Everything that I've been studying for the past years and a more structured way to learn the foundational knowledge to tackle problems in this impactful field. Here's what I covered in the blog: — Programming & Engineering — AI & Machine Learning — Mathematics for ML — Healthcare — Biology & Biolomedicine This is what I hoped to have when I first started learning all this. 🔗 link:

TK • 木下

26,889 views • 11 months ago

Everyone says learn inference, learn CUDA,learn ML But almost nobody tells you exactly what to learn, how to learn it, or how to know whether you’ve actually learned it. Introducing a free, open-source platform to learn and practice Machine Learning, CUDA, and inference through hands-on coding. Instead of just watching lectures or reading theory, you’ll: • Learn the theory behind each concept • Follow step-by-step implementation examples • Solve coding challenges based on what you learned • Implement algorithms from scratch, from foundational ML to Inference and CUDA kernel • Practice problems in a Codeforces-style environment • Get new Problem of the Day challenges with ratings The goal isn't just to teach you how to write the code. It’s to help you understand what your code is actually doing underneath. We’re building Trentorch because advanced education shouldn’t be locked behind expensive subscriptions. Money shouldn’t be the barrier to learning ML, inference, or CUDA. A free alternative to platforms that charge students heavily every month. signup and start solving problems now

Everyone says learn inference, learn CUDA,learn ML But almost nobody tells you exactly what to learn, how to learn it, or how to know whether you’ve actually learned it. Introducing a free, open-source platform to learn and practice Machine Learning, CUDA, and inference through hands-on coding. Instead of just watching lectures or reading theory, you’ll: • Learn the theory behind each concept • Follow step-by-step implementation examples • Solve coding challenges based on what you learned • Implement algorithms from scratch, from foundational ML to Inference and CUDA kernel • Practice problems in a Codeforces-style environment • Get new Problem of the Day challenges with ratings The goal isn't just to teach you how to write the code. It’s to help you understand what your code is actually doing underneath. We’re building Trentorch because advanced education shouldn’t be locked behind expensive subscriptions. Money shouldn’t be the barrier to learning ML, inference, or CUDA. A free alternative to platforms that charge students heavily every month. signup and start solving problems now

AADITYANSHA

101,105 views • 1 month ago

if you're struggling on where to start learning ML, here’s a playlist of 30 youtube videos to learn machine learning fundamentals from scratch "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):

if you're struggling on where to start learning ML, here’s a playlist of 30 youtube videos to learn machine learning fundamentals from scratch "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):

ℏεsam

109,574 views • 1 year 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):

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):

ℏεsam

117,748 views • 1 year ago

Axiom Math founder Carina Hong on why math is the path to general superintelligence: “Math is the sandbox for reality. You very quickly see verifiable rewards because in math, there’s an absolute right or wrong.” “And especially when you have Lean, you can check the proof or solution step by step. You’ll be able to apply reinforcement learning in a much more efficient way.” “We have currently scaled from winning a Putnam perfect score to solving a batch of research problems that professional mathematicians find really challenging. And we also see this transfer to code verification.”

Axiom Math founder Carina Hong on why math is the path to general superintelligence: “Math is the sandbox for reality. You very quickly see verifiable rewards because in math, there’s an absolute right or wrong.” “And especially when you have Lean, you can check the proof or solution step by step. You’ll be able to apply reinforcement learning in a much more efficient way.” “We have currently scaled from winning a Putnam perfect score to solving a batch of research problems that professional mathematicians find really challenging. And we also see this transfer to code verification.”

TBPN

39,589 views • 7 months ago

X algorithm, show this post only to people who are interested in learning AI/ML through hands-on projects. I'm building CrekAI, a platform where users can learn AI & ML through hands-on projects such as: > Build a neural network from scratch > Create your own custom tokenizer > Build your own ChatGPT > Develop a recommendation model > And much more Each project is divided into multiple stages, with clear instructions and tasks/assignments. When you log in and reach your dashboard, you can choose any project or learning module to start with. For every project, you'll open a fresh Colab notebook where you'll write and run your code based on the given tasks or assignments for the current stage. After completing a stage’s task in Colab, run the verification code (provided by CrekAI) in the last cell. If it passes, the next stage unlocks automatically. Grok what do you think about this project?

X algorithm, show this post only to people who are interested in learning AI/ML through hands-on projects. I'm building CrekAI, a platform where users can learn AI & ML through hands-on projects such as: > Build a neural network from scratch > Create your own custom tokenizer > Build your own ChatGPT > Develop a recommendation model > And much more Each project is divided into multiple stages, with clear instructions and tasks/assignments. When you log in and reach your dashboard, you can choose any project or learning module to start with. For every project, you'll open a fresh Colab notebook where you'll write and run your code based on the given tasks or assignments for the current stage. After completing a stage’s task in Colab, run the verification code (provided by CrekAI) in the last cell. If it passes, the next stage unlocks automatically. Grok what do you think about this project?

vixhaℓ

57,309 views • 11 months ago

“What if the real unlock for AI x crypto is not speculation — but proving intelligence can be trusted?” Sachi Miyasaki sits down with dcbuilder.eth ⚪️ for a deep dive into the intersection of AI and crypto, from DC’s work building the ZKML community to why zero-knowledge machine learning could become a core trust layer for AI on the internet. They break down how ZKML makes it possible to prove that a machine learning model produced a specific output from specific inputs — without forcing everyone to rerun the full computation themselves. The conversation also explores why this matters for model accountability, transparency, and verifiable AI at scale, plus the projects pushing the space forward, from Modulus Labs and Giza to EZKL and new research around proof of inference.

“What if the real unlock for AI x crypto is not speculation — but proving intelligence can be trusted?” Sachi Miyasaki sits down with dcbuilder.eth ⚪️ for a deep dive into the intersection of AI and crypto, from DC’s work building the ZKML community to why zero-knowledge machine learning could become a core trust layer for AI on the internet. They break down how ZKML makes it possible to prove that a machine learning model produced a specific output from specific inputs — without forcing everyone to rerun the full computation themselves. The conversation also explores why this matters for model accountability, transparency, and verifiable AI at scale, plus the projects pushing the space forward, from Modulus Labs and Giza to EZKL and new research around proof of inference.

BeaconLayer Podcast

73,815 views • 4 months ago

“What if the real unlock for AI x crypto is not speculation — but proving intelligence can be trusted?” Sachi Takamura / 0699.eth sits down with dcbuilder.eth ⚪️ for a deep dive into the intersection of AI and crypto, from DC’s work building the ZKML community to why zero-knowledge machine learning could become a core trust layer for AI on the internet. They break down how ZKML makes it possible to prove that a machine learning model produced a specific output from specific inputs — without forcing everyone to rerun the full computation themselves. The conversation also explores why this matters for model accountability, transparency, and verifiable AI at scale, plus the projects pushing the space forward, from Modulus Labs and Giza to EZKL and new research around proof of inference.

“What if the real unlock for AI x crypto is not speculation — but proving intelligence can be trusted?” Sachi Takamura / 0699.eth sits down with dcbuilder.eth ⚪️ for a deep dive into the intersection of AI and crypto, from DC’s work building the ZKML community to why zero-knowledge machine learning could become a core trust layer for AI on the internet. They break down how ZKML makes it possible to prove that a machine learning model produced a specific output from specific inputs — without forcing everyone to rerun the full computation themselves. The conversation also explores why this matters for model accountability, transparency, and verifiable AI at scale, plus the projects pushing the space forward, from Modulus Labs and Giza to EZKL and new research around proof of inference.

BeaconLayer

42,987 views • 2 months ago

Bill Evans talking on his way of learning jazz: * analize the tune (harmony, meaning, etc) * break problems into small pieces and afford them one by one * always deep concentration * persistent practice and improvement Isn't it a life lesson? #sheetmusic #Noten 乐谱樂譜

Bill Evans talking on his way of learning jazz: * analize the tune (harmony, meaning, etc) * break problems into small pieces and afford them one by one * always deep concentration * persistent practice and improvement Isn't it a life lesson? #sheetmusic #Noten 乐谱樂譜

Sheet Music Library

19,065 views • 1 year ago

Introducing NEO: The first Autonomous Machine Learning Engineer. NEO is a multi-agent system that automates the entire ML workflow, saving engineers thousands of hours of grunt work. Our recent advancements in multi-step reasoning and memory orchestration have enabled NEO to excel at solving complex Machine Learning problems on its own from data engineering to deployments of ML models. Benchmarks: NEO was tested against 50 Kaggle competitions and scored a medal in 26% of them, far superior than the previous state of the art performance of 16.9% by Open AI’s O1 with AIDE scaffolding on the MLE bench With these capabilities, NEO aims to help every ML engineer become superhuman and focus on real innovation instead of the existing grunt work Check out NEO in the thread below:

Introducing NEO: The first Autonomous Machine Learning Engineer. NEO is a multi-agent system that automates the entire ML workflow, saving engineers thousands of hours of grunt work. Our recent advancements in multi-step reasoning and memory orchestration have enabled NEO to excel at solving complex Machine Learning problems on its own from data engineering to deployments of ML models. Benchmarks: NEO was tested against 50 Kaggle competitions and scored a medal in 26% of them, far superior than the previous state of the art performance of 16.9% by Open AI’s O1 with AIDE scaffolding on the MLE bench With these capabilities, NEO aims to help every ML engineer become superhuman and focus on real innovation instead of the existing grunt work Check out NEO in the thread below:

Neo AI

321,273 views • 1 year ago

Before “crypto AI” became a category, David was already exploring the ideas behind it. @kenzimori catches up with David Minarsch ☴ on his early path into crypto x AI — from studying Bitcoin’s economics and computer science during his PhD, to witnessing the 2017 wave of crypto builders up close. The conversation traces how David’s work across game theory, machine learning, and agent systems eventually shaped his thinking around autonomous agents and open-source coordination infrastructure.

Before “crypto AI” became a category, David was already exploring the ideas behind it. @kenzimori catches up with David Minarsch ☴ on his early path into crypto x AI — from studying Bitcoin’s economics and computer science during his PhD, to witnessing the 2017 wave of crypto builders up close. The conversation traces how David’s work across game theory, machine learning, and agent systems eventually shaped his thinking around autonomous agents and open-source coordination infrastructure.

BeaconLayer

79,191 views • 3 months ago

- Notion 2.0, 2026 plans - Alright guys, here’s a sneak peek at what the educational material inside Notion has become and what the plans are for this year. Over the past 2 years, I’ve written extensively. The result? A complete trading course, structured and accessible even if you’re starting from zero. We’re now talking about 1,700+ pages of PDFs covering everything from basic to advanced concepts. From market structure to Wyckoff, from AMT to SMC not just through written material, but also supported by in-depth video content. It’s truly a comprehensive learning path. And this year, it’s going to be expanded even further with new content across multiple areas: - Technical: time cycles, execution models, deeper operational frameworks. - Fundamental: with a strong focus on traditional finance. - Psychological: new practical guides and tools designed to help you stay calm, build discipline, and develop the right mindset. On top of that, there will also be live training sessions to make the experience even more interactive and dynamic. If you combine all of this with weekly and bi-weekly market analysis, video updates, and a tight-knit, growth-oriented community… what you get is not just a course, but a high-level learning experience. A special thank you to MisterPA of course not only for expanding the Notion with his educational videos, but for actively contributing with updates and analysis that are bringing real value to all members. If you’re looking for a structured growth path, one designed to guide you step by step, even if you’re starting from scratch.. this is exactly what you need.

- Notion 2.0, 2026 plans - Alright guys, here’s a sneak peek at what the educational material inside Notion has become and what the plans are for this year. Over the past 2 years, I’ve written extensively. The result? A complete trading course, structured and accessible even if you’re starting from zero. We’re now talking about 1,700+ pages of PDFs covering everything from basic to advanced concepts. From market structure to Wyckoff, from AMT to SMC not just through written material, but also supported by in-depth video content. It’s truly a comprehensive learning path. And this year, it’s going to be expanded even further with new content across multiple areas: - Technical: time cycles, execution models, deeper operational frameworks. - Fundamental: with a strong focus on traditional finance. - Psychological: new practical guides and tools designed to help you stay calm, build discipline, and develop the right mindset. On top of that, there will also be live training sessions to make the experience even more interactive and dynamic. If you combine all of this with weekly and bi-weekly market analysis, video updates, and a tight-knit, growth-oriented community… what you get is not just a course, but a high-level learning experience. A special thank you to MisterPA of course not only for expanding the Notion with his educational videos, but for actively contributing with updates and analysis that are bringing real value to all members. If you’re looking for a structured growth path, one designed to guide you step by step, even if you’re starting from scratch.. this is exactly what you need.

ZERO IKA 🗡️

29,565 views • 8 months ago

Full episode of the first-ever Math Academy podcast with Jason Roberts: 0:00 - Introduction 4:00 - Applying the MA Way to X Growth 7:40 - Status of the ML Course and its Kick-Ass Coding Projects (Part 1) 25:50 - Jason's Near-Infinite List of Important Things 34:20 - The ML Course Has Been a Massive Undertaking 42:10 - Breadth-First Development 44:30 - Status of the ML Course and its Kick-Ass Coding Projects (Part 2) 50:15 - Why Math Academy Needs To Do a CS Course 56:45 - The Never-Ending Stream of Confusion 1:00:30 - The Story of Eurisko, the Most Advanced Math/CS Track in the USA 1:24:20 - Intuition Through Repetition: Machine Learning Edition 1:29:40 - The Importance of Spaced Review 1:43:30 - Upcoming Course Roadmap 1:47:40 - Spaced Repetition 2.0: Accounting For and Discouraging Reference Reliance 1:54:45 - Overhelping: A Pathology of the Over-Involved Parent/Tutor 1:59:21 - Yes, You Need to be Automatic on Math Facts (and Yes, Rapid-Fire Training is Coming) 2:04:55 - What Happens When Students Don't Know Their Math Facts 2:05:50 - The Horror of Attempting to Teach a Class When Students Have Multi-Year Deficits in Fundamental Skills 2:11:30 - Integrating Coding Into the Math Curriculum 2:18:00 - Combining Math and Coding is the Closest Thing to a Real-Life Superpower 2:18:55 - Creating a Full Math Degree and Getting Full College Credit 2:22:15 - The Power of Pre-Learning: The Greatest Educational Life Hack

Full episode of the first-ever Math Academy podcast with Jason Roberts: 0:00 - Introduction 4:00 - Applying the MA Way to X Growth 7:40 - Status of the ML Course and its Kick-Ass Coding Projects (Part 1) 25:50 - Jason's Near-Infinite List of Important Things 34:20 - The ML Course Has Been a Massive Undertaking 42:10 - Breadth-First Development 44:30 - Status of the ML Course and its Kick-Ass Coding Projects (Part 2) 50:15 - Why Math Academy Needs To Do a CS Course 56:45 - The Never-Ending Stream of Confusion 1:00:30 - The Story of Eurisko, the Most Advanced Math/CS Track in the USA 1:24:20 - Intuition Through Repetition: Machine Learning Edition 1:29:40 - The Importance of Spaced Review 1:43:30 - Upcoming Course Roadmap 1:47:40 - Spaced Repetition 2.0: Accounting For and Discouraging Reference Reliance 1:54:45 - Overhelping: A Pathology of the Over-Involved Parent/Tutor 1:59:21 - Yes, You Need to be Automatic on Math Facts (and Yes, Rapid-Fire Training is Coming) 2:04:55 - What Happens When Students Don't Know Their Math Facts 2:05:50 - The Horror of Attempting to Teach a Class When Students Have Multi-Year Deficits in Fundamental Skills 2:11:30 - Integrating Coding Into the Math Curriculum 2:18:00 - Combining Math and Coding is the Closest Thing to a Real-Life Superpower 2:18:55 - Creating a Full Math Degree and Getting Full College Credit 2:22:15 - The Power of Pre-Learning: The Greatest Educational Life Hack

Justin Skycak

66,774 views • 11 months ago

Introducing The AI CUDA Engineer: An agentic AI system that automates the production of highly optimized CUDA kernels. The AI CUDA Engineer can produce highly optimized CUDA kernels, reaching 10-100x speedup over common machine learning operations in PyTorch. Our system is also able to produce highly optimized CUDA kernels that are much faster than existing CUDA kernels commonly used in production. We believe that fundamentally, AI systems can and should be as resource-efficient as the human brain, and that the best path to achieve this efficiency is to use AI to make AI more efficient! We are excited to publish our paper, The AI CUDA Engineer: Agentic CUDA Kernel Discovery, Optimization and Composition. We also release a dataset of over 17,000 verified CUDA kernels produced by The AI CUDA Engineer. Paper: Kernel Archive Webpage: HuggingFace Dataset: The AI CUDA Engineer utilizes evolutionary LLM-driven code optimization to autonomously improve the runtime of machine learning operations. Our system is not only able to convert PyTorch code into CUDA kernels, but through the use of evolution, it can also optimize the runtime performance of CUDA kernels, fuse multiple operations, and even discover novel solutions for writing efficient CUDA operations by learning from past innovations! We believe The AI CUDA Engineer opens a new era of AI-driven acceleration of AI and automated inference time optimization. We (Robert Lange, Aaditya Prasad 🇺🇸, sssss, Maxence Faldor, Yujin Tang, hardmaru) are excited to continue Sakana AI's mission of leveraging AI to improve AI.

Introducing The AI CUDA Engineer: An agentic AI system that automates the production of highly optimized CUDA kernels. The AI CUDA Engineer can produce highly optimized CUDA kernels, reaching 10-100x speedup over common machine learning operations in PyTorch. Our system is also able to produce highly optimized CUDA kernels that are much faster than existing CUDA kernels commonly used in production. We believe that fundamentally, AI systems can and should be as resource-efficient as the human brain, and that the best path to achieve this efficiency is to use AI to make AI more efficient! We are excited to publish our paper, The AI CUDA Engineer: Agentic CUDA Kernel Discovery, Optimization and Composition. We also release a dataset of over 17,000 verified CUDA kernels produced by The AI CUDA Engineer. Paper: Kernel Archive Webpage: HuggingFace Dataset: The AI CUDA Engineer utilizes evolutionary LLM-driven code optimization to autonomously improve the runtime of machine learning operations. Our system is not only able to convert PyTorch code into CUDA kernels, but through the use of evolution, it can also optimize the runtime performance of CUDA kernels, fuse multiple operations, and even discover novel solutions for writing efficient CUDA operations by learning from past innovations! We believe The AI CUDA Engineer opens a new era of AI-driven acceleration of AI and automated inference time optimization. We (Robert Lange, Aaditya Prasad 🇺🇸, sssss, Maxence Faldor, Yujin Tang, hardmaru) are excited to continue Sakana AI's mission of leveraging AI to improve AI.

Sakana AI

1,160,959 views • 1 year ago

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, January 29th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (1570+ registered):

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, January 29th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (1570+ registered):

Matt Dancho (Business Science)

242,084 views • 1 year ago

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Data Science Agents): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, May 21st, I'm sharing one of my best AI Projects: AI Customer Segmentation Agent with Python Register here (570+ registered):

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Data Science Agents): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, May 21st, I'm sharing one of my best AI Projects: AI Customer Segmentation Agent with Python Register here (570+ registered):

Matt Dancho (Business Science)

35,452 views • 1 year ago

On the pod: Machine Learning Street Talk's paper "Why Creativity Cannot Be Interpolated" with Tim Scarfe + Dr. Jeremy Budd. A deep dive into creativity, understanding, neuro-symbolic AI, human-AI collaboration, and how systems capable of extending their own phylogeny could become creative.

On the pod: Machine Learning Street Talk's paper "Why Creativity Cannot Be Interpolated" with Tim Scarfe + Dr. Jeremy Budd. A deep dive into creativity, understanding, neuro-symbolic AI, human-AI collaboration, and how systems capable of extending their own phylogeny could become creative.

Ndea

11,722 views • 3 months ago

I had a fantastic time discussing with the learning legend Justin Skycak from Math Academy about learning math in the modern age. we've talked about his quite impressive self-learning journey (3000h of math in high school) all the way to how he hand curated the initial knowledge graph for math academy to make that process more efficient. great lively 3h discussion here are the chapters: 0:00:00 - intro: 0:02:10 - justin background 0:05:45 - 3000h math self study in high school 0:11:45 - what a day looked like for that 3000h stretch 0:16:10 - meta-learning vs pure math learning 0:21:50 - when did you get into cognitive neuro? 0:29:55 - how did the fundamental math helped in your research projects 0:43:10 - what does the math academy learning system looks like 0:47:34 - how did you guys build the 2000 topic knowledge graph 1:01:15 - would LLM be useful as an interface to that knowledge graph for the students? 1:10:46 - how does the FIRe spaced repetition algorithm works? 1:17:34 - does the same knowledge graph structure would work for physics? or other topic?: 1:34:05 - how do you understand the subject vs the curiculum 1:35:50 - is there a connection between studying math and learning a sport? 1:42:00 - do you think in math doing and teaching requires different skills? 1:56:25 - could you get understanding without automaticy? 2:05:35 - do you see any upside of confusion in learning? 2:14:11 - learning math as an adult? 2:19:20 - how to fill the motivation gap after learning the fundamental? 2:24:10 - how should teaching math for kids and adults balance fundamentals and creativity? 2:33:55 - is it ever too late to learn math seriously? 2:46:00 - mastery learning vs ultra learning 2:51:30 - top-down vs bottom-up 2:53:40 - mastery learning for domain without a structured hierarchical structure? 2:56:30 - neurodivergence / adhd for structured math learning? 3:06:20 - amateur mathematician augmented with technology will be able to contribute to research? 3:14:37 - what are you most excited about right now in term of learning enjoy!

I had a fantastic time discussing with the learning legend Justin Skycak from Math Academy about learning math in the modern age. we've talked about his quite impressive self-learning journey (3000h of math in high school) all the way to how he hand curated the initial knowledge graph for math academy to make that process more efficient. great lively 3h discussion here are the chapters: 0:00:00 - intro: 0:02:10 - justin background 0:05:45 - 3000h math self study in high school 0:11:45 - what a day looked like for that 3000h stretch 0:16:10 - meta-learning vs pure math learning 0:21:50 - when did you get into cognitive neuro? 0:29:55 - how did the fundamental math helped in your research projects 0:43:10 - what does the math academy learning system looks like 0:47:34 - how did you guys build the 2000 topic knowledge graph 1:01:15 - would LLM be useful as an interface to that knowledge graph for the students? 1:10:46 - how does the FIRe spaced repetition algorithm works? 1:17:34 - does the same knowledge graph structure would work for physics? or other topic?: 1:34:05 - how do you understand the subject vs the curiculum 1:35:50 - is there a connection between studying math and learning a sport? 1:42:00 - do you think in math doing and teaching requires different skills? 1:56:25 - could you get understanding without automaticy? 2:05:35 - do you see any upside of confusion in learning? 2:14:11 - learning math as an adult? 2:19:20 - how to fill the motivation gap after learning the fundamental? 2:24:10 - how should teaching math for kids and adults balance fundamentals and creativity? 2:33:55 - is it ever too late to learn math seriously? 2:46:00 - mastery learning vs ultra learning 2:51:30 - top-down vs bottom-up 2:53:40 - mastery learning for domain without a structured hierarchical structure? 2:56:30 - neurodivergence / adhd for structured math learning? 3:06:20 - amateur mathematician augmented with technology will be able to contribute to research? 3:14:37 - what are you most excited about right now in term of learning enjoy!

Yacine Mahdid

57,320 views • 6 months ago