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Introducing TensorTonic. A platform to actually learn ML by building it. > Practice ML algorithms in LC style > Replicate cutting-edge ML papers > Prepare with company-wise AI interview questions + blogs. v1 is out. accelerating hard. website link in comments.

pdawg

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163,602 views • 10 months ago •via X (Twitter)

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Introducing Tensortonic research > Implement ML papers in cloud-native IDEs > Breakdown of all papers to architecture, math, and code > State-of-the-art papers like Transformers, BERT, ViT, DDPM, VAE, GANs and many more

Introducing Tensortonic research > Implement ML papers in cloud-native IDEs > Breakdown of all papers to architecture, math, and code > State-of-the-art papers like Transformers, BERT, ViT, DDPM, VAE, GANs and many more

pdawg

67,269 views • 7 months 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

You won’t learn by highlighting words in a textbook. Learning is done via feedback loop. Learn something new -> try to apply it -> fuck up/win -> get feedback -> try again. Learning is actually fun when done properly. Try it.

You won’t learn by highlighting words in a textbook. Learning is done via feedback loop. Learn something new -> try to apply it -> fuck up/win -> get feedback -> try again. Learning is actually fun when done properly. Try it.

Andrew Tate

408,417 views • 10 months 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 • 9 months ago

This is the coolest tool you'll see today! Introducing Drawdata! 🚀 It allows you to draw a 2-D dataset of any shape in a jupyter notebook. A very handy tool for learning & understanding the behaviour of ML algorithms! Check this out👇

This is the coolest tool you'll see today! Introducing Drawdata! 🚀 It allows you to draw a 2-D dataset of any shape in a jupyter notebook. A very handy tool for learning & understanding the behaviour of ML algorithms! Check this out👇

Akshay 🚀

112,206 views • 2 years ago

Introducing the context course: a free course on doing ML with agent context. You will learn how to train models, optimize inferences, and build datasets, all by defining harness context with`SKILLS.md`, Plugins, MCP, Subagents, and Hooks. The course includes: - Weekly live AMA on YouTube - Weekly practical projects for ML with context - Instructions in Pi, Codex, Claude, and Opencode - Tutorials and guides on fundamentals - Interactive Quizzes Learn to give AI agents the right knowledge, tools, and structure to actually get work done. Skills, MCP servers, plugins, multi-agent workflows, and building an agent from scratch. Join here:

Introducing the context course: a free course on doing ML with agent context. You will learn how to train models, optimize inferences, and build datasets, all by defining harness context with`SKILLS.md`, Plugins, MCP, Subagents, and Hooks. The course includes: - Weekly live AMA on YouTube - Weekly practical projects for ML with context - Instructions in Pi, Codex, Claude, and Opencode - Tutorials and guides on fundamentals - Interactive Quizzes Learn to give AI agents the right knowledge, tools, and structure to actually get work done. Skills, MCP servers, plugins, multi-agent workflows, and building an agent from scratch. Join here:

Ben Burtenshaw

16,905 views • 4 months ago

Is a fair memecoin launch actually possible? Introducing Fair Launch Pools by Metadrop Create a coin for free on BASE + ETH (SOL very soon) Want to buy the coin? Find coin -> Contribute ETH to pool -> Get in early -> Claim coins -> Send it

Is a fair memecoin launch actually possible? Introducing Fair Launch Pools by Metadrop Create a coin for free on BASE + ETH (SOL very soon) Want to buy the coin? Find coin -> Contribute ETH to pool -> Get in early -> Claim coins -> Send it

💐Guru 💐

43,726 views • 2 years ago

Humiliating for Reform UK's Laila Cunningham on #Newsnight LC, "We've been very clear" VD, "You haven't bee that clear, his position has hifted over weeks" LC, "Not really, he's never said boots on the ground" VD, "I didn't suggest he said boots on the ground" LC, "The position has always been that Keir Starmer should have left the US use our bases from the beginning" VD, "No, that did change over weeks" LC, "Not from Farage" VD, "Yes it did" LC, "He said the US should use our bases" VD, "He did, then Tice said a similar thing, then Jenrick said no" ML, "If we let them use our bases it would be an aggressive war against another sovereign nation" LC, "We are allies" ML, "We weren't consulted as allies, this is an act of aggression by Israel and the USA" LC, "Iran is not innocent in all this" ML, "Didn't say they're innocent" LC, "They say death to the west" ML, "You don't attack every nation you disagree with" LC, "They're a danger to us" ML, "What danger were they to us" LC, "They kept saying death to the west, they sent terrorists to our country" ML, "Who" LC, "Four terrorist attacks were stopped, they're a hostile state, we never said boots on the ground" VD, "Andrea Jenkyns didn't rule out UK boots on the ground, and now you're smiling because?" LC, "I didn't hear what she said"

Humiliating for Reform UK's Laila Cunningham on #Newsnight LC, "We've been very clear" VD, "You haven't bee that clear, his position has hifted over weeks" LC, "Not really, he's never said boots on the ground" VD, "I didn't suggest he said boots on the ground" LC, "The position has always been that Keir Starmer should have left the US use our bases from the beginning" VD, "No, that did change over weeks" LC, "Not from Farage" VD, "Yes it did" LC, "He said the US should use our bases" VD, "He did, then Tice said a similar thing, then Jenrick said no" ML, "If we let them use our bases it would be an aggressive war against another sovereign nation" LC, "We are allies" ML, "We weren't consulted as allies, this is an act of aggression by Israel and the USA" LC, "Iran is not innocent in all this" ML, "Didn't say they're innocent" LC, "They say death to the west" ML, "You don't attack every nation you disagree with" LC, "They're a danger to us" ML, "What danger were they to us" LC, "They kept saying death to the west, they sent terrorists to our country" ML, "Who" LC, "Four terrorist attacks were stopped, they're a hostile state, we never said boots on the ground" VD, "Andrea Jenkyns didn't rule out UK boots on the ground, and now you're smiling because?" LC, "I didn't hear what she said"

Farrukh

256,594 views • 5 months ago

Grok 4.5 and Muse Spark 1.1 are now available on the AI/ML platform for testing. What a week; all 3 models in the comparison dropped just recently. There will be a lot to experiment with. AI/ML has tested the new models on prompts for playable mini-games, including a Fruit Ninja-style slicer and a Crossy Road clone. All three are live in the Playground and via API for side-by-side runs on output and cost.

Grok 4.5 and Muse Spark 1.1 are now available on the AI/ML platform for testing. What a week; all 3 models in the comparison dropped just recently. There will be a lot to experiment with. AI/ML has tested the new models on prompts for playable mini-games, including a Fruit Ninja-style slicer and a Crossy Road clone. All three are live in the Playground and via API for side-by-side runs on output and cost.

🚨 AI News | TestingCatalog

37,973 views • 2 months ago

Databricks launches Free Edition! Explore and learn the latest data and AI technologies for free. Users can experiment with the full range of use cases – from building AI agents and applications to analyzing data with SQL – on the same platform used by millions of professionals. We’re proud to help today’s students and enthusiasts build real skills on real tools in today's competitive AI and ML job market.

Databricks launches Free Edition! Explore and learn the latest data and AI technologies for free. Users can experiment with the full range of use cases – from building AI agents and applications to analyzing data with SQL – on the same platform used by millions of professionals. We’re proud to help today’s students and enthusiasts build real skills on real tools in today's competitive AI and ML job market.

Databricks

25,085 views • 1 year ago

Today, I am launching Paper Breakdown. - PBD gets you academic paper recommendations and lets you study CS/ML/AI research with LLM agents. - It highlights relevant sections directly in the actual PDF - generates flowcharts/illustrations too - we provide an in-build screenshot tool to send images to the agent directly from the paper. - we also got agentic paper search that allows you to search our database of 70,000+ CS and ML Arxiv papers in seconds using natural language. I have been building PBD for almost half a year - it all started as a means for me to keep up with research and use AI to produce visuals and scripts for my own YouTube videos. I have developed it enough to confidently recommend it to you. Visit our landing page to learn more.

Today, I am launching Paper Breakdown. - PBD gets you academic paper recommendations and lets you study CS/ML/AI research with LLM agents. - It highlights relevant sections directly in the actual PDF - generates flowcharts/illustrations too - we provide an in-build screenshot tool to send images to the agent directly from the paper. - we also got agentic paper search that allows you to search our database of 70,000+ CS and ML Arxiv papers in seconds using natural language. I have been building PBD for almost half a year - it all started as a means for me to keep up with research and use AI to produce visuals and scripts for my own YouTube videos. I have developed it enough to confidently recommend it to you. Visit our landing page to learn more.

AVB

14,579 views • 8 months ago

Introducing NEO: The first Autonomous Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34.2% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on OpenAI's MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:

Introducing NEO: The first Autonomous Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34.2% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on OpenAI's MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:

Neo AI

861,393 views • 1 year ago

Introducing NEO: The first Agentic Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on the MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:

Introducing NEO: The first Agentic Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on the MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:

Neo AI

19,571,775 views • 1 year ago

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.

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

132,607 views • 5 months ago

What does it mean when an AI model is “open weights” and how is it different than “open source”? Welcome to the next episode of the Open Model Series with merve, Hugging Face AI/ML engineer & open-sourceress! Every week we will discuss open-source & open-weight AI: what it is, how it works, and how you can run them on your own compute. Episode 3 out next week; episode 1 linked below. Please leave all your questions about open-source & open-weight AI in the comments. This is a super important topic that is novel to most people, and it can be challenging to grasp - so your questions are shared with many others.

What does it mean when an AI model is “open weights” and how is it different than “open source”? Welcome to the next episode of the Open Model Series with merve, Hugging Face AI/ML engineer & open-sourceress! Every week we will discuss open-source & open-weight AI: what it is, how it works, and how you can run them on your own compute. Episode 3 out next week; episode 1 linked below. Please leave all your questions about open-source & open-weight AI in the comments. This is a super important topic that is novel to most people, and it can be challenging to grasp - so your questions are shared with many others.

Harper Carroll

32,624 views • 22 days ago

We’re thrilled to announce the official launch of AIOZ AI V1, a Web3 collaborative AI platform powered by AIOZ DePINs! Sign up and get started here: With AIOZ AI, we’re setting a new Web3 standard for AI development and innovation. Here’s what makes it unique: → Web3 AI marketplace for models and datasets → AI compute powered by AIOZ DePINs → Monetization opportunities for contributors in a growing AI ecosystem Whether you're building, contributing, or reimagining AI solutions, AIOZ AI V1 is your platform. Explore the platform and join us in the DePIN Ecosystem for AI. Read the full introduction on our blog here:

We’re thrilled to announce the official launch of AIOZ AI V1, a Web3 collaborative AI platform powered by AIOZ DePINs! Sign up and get started here: With AIOZ AI, we’re setting a new Web3 standard for AI development and innovation. Here’s what makes it unique: → Web3 AI marketplace for models and datasets → AI compute powered by AIOZ DePINs → Monetization opportunities for contributors in a growing AI ecosystem Whether you're building, contributing, or reimagining AI solutions, AIOZ AI V1 is your platform. Explore the platform and join us in the DePIN Ecosystem for AI. Read the full introduction on our blog here:

AIOZ Network

600,449 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

321,235 views • 1 year ago