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。

Can you calculate a one-node neural network by hand ✍️

Tom Yeh

55,940 subscribers

10,362 次观看 • 9 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

Can you calculate a Vision Transformer (ViT) by hand? ✍️ If you can, come join me next week at the LLM Paper Club. I will extend ViT to Llama 1 -> 2 -> 3 -> 4 live. Event link below.

Can you calculate a Vision Transformer (ViT) by hand? ✍️ If you can, come join me next week at the LLM Paper Club. I will extend ViT to Llama 1 -> 2 -> 3 -> 4 live. Event link below.

Tom Yeh

79,441 次观看 • 1 年前

MLP by hand✍️Excel ~ I designed this exercise to show how to calculate a four-level network by hand, along with a graph and matching pytorch code. Also, I made a medium and a large version to show how it scales. 👇Join the 'AI Math' community. Download xlsx.

MLP by hand✍️Excel ~ I designed this exercise to show how to calculate a four-level network by hand, along with a graph and matching pytorch code. Also, I made a medium and a large version to show how it scales. 👇Join the 'AI Math' community. Download xlsx.

Tom Yeh

62,253 次观看 • 1 年前

you can and should ask claude to train a neural network and then compile into excel formulas

you can and should ask claude to train a neural network and then compile into excel formulas

nate parrott

185,354 次观看 • 11 个月前

#b3d tip: To keep complex node trees organised, you can use Node Groups. They reduce part of a node tree to a single node, which you can enter/exit by pressing TAB. You can also create inputs, so that you can control values inside the Node Group without having to open it.

#b3d tip: To keep complex node trees organised, you can use Node Groups. They reduce part of a node tree to a single node, which you can enter/exit by pressing TAB. You can also create inputs, so that you can control values inside the Node Group without having to open it.

Jan van den Hemel

39,369 次观看 • 3 年前

Can a single neural network policy generalize over poses, objects, obstacles, backgrounds, scene arrangements, in-hand objects, and start/goal states? Introducing Neural MP: A generalist policy for solving motion planning tasks in the real world 🤖 1/N

Can a single neural network policy generalize over poses, objects, obstacles, backgrounds, scene arrangements, in-hand objects, and start/goal states? Introducing Neural MP: A generalist policy for solving motion planning tasks in the real world 🤖 1/N

Murtaza Dalal

114,720 次观看 • 1 年前

Run a node. Join the network. Use your Bitcoin. Each added participant contributes more than you can imagine.

Run a node. Join the network. Use your Bitcoin. Each added participant contributes more than you can imagine.

Bitcoin News

22,099 次观看 • 1 年前

Hopfield recall Spiking Neural network now this one is cool isn't it? -open source, link in comment- This is a Spiking Neural Network (SNN) that can learn and recall patterns through Hebbian learning, similar to a Hopfield network but using biologically-inspired spiking dynamics rather than energy-based settling.

Hopfield recall Spiking Neural network now this one is cool isn't it? -open source, link in comment- This is a Spiking Neural Network (SNN) that can learn and recall patterns through Hebbian learning, similar to a Hopfield network but using biologically-inspired spiking dynamics rather than energy-based settling.

echo.hive

31,452 次观看 • 7 个月前

DeepSeek by hand ✍️ in Excel. You can explore and study in your web browser 👉

DeepSeek by hand ✍️ in Excel. You can explore and study in your web browser 👉

Tom Yeh

164,904 次观看 • 1 年前

I animated how a neural network learns, from one neuron to backpropagation, simple math, no calculus required. Best 8 minutes you can spend if it has only ever been a black box to you.

I animated how a neural network learns, from one neuron to backpropagation, simple math, no calculus required. Best 8 minutes you can spend if it has only ever been a black box to you.

tetsuo

48,685 次观看 • 2 个月前

The universal approximation theorem states that a neural network with one hidden layer can approximate continuous functions on compact sets with any desired precision.

The universal approximation theorem states that a neural network with one hidden layer can approximate continuous functions on compact sets with any desired precision.

ₕₐₘₚₜₒₙ

218,781 次观看 • 2 年前

visualization of a simple neural network

visualization of a simple neural network

ℏεsam

79,706 次观看 • 1 年前

How a neural network sees and interprets a character

How a neural network sees and interprets a character

Massimo

1,959,678 次观看 • 1 年前

Transformer by hand✍️

Transformer by hand✍️

Tom Yeh

29,650 次观看 • 9 个月前

These imagined futures can be action-conditioned to produce different outcomes. For example, the videos below are generated entirely by the neural network by simply using different prompts

These imagined futures can be action-conditioned to produce different outcomes. For example, the videos below are generated entirely by the neural network by simply using different prompts

Tesla AI

406,108 次观看 • 3 年前

Movements begin with small steps. One node. One builder. One moment that grows into something bigger. Ricardo released the latest Core Node & network updates and the momentum is real. 👉 If you want to take part in shaping the OptimAI intelligence layer, now is the perfect time. Spin up your node, join the network, and let’s build the future of Agentic AI together.

Movements begin with small steps. One node. One builder. One moment that grows into something bigger. Ricardo released the latest Core Node & network updates and the momentum is real. 👉 If you want to take part in shaping the OptimAI intelligence layer, now is the perfect time. Spin up your node, join the network, and let’s build the future of Agentic AI together.

OptimAI Network

22,938 次观看 • 9 个月前