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The best Layer 2 is a Layer 1 that scales. Enhanced security, massive throughput, and predictable $KDA gas fees are the direct results. This has always been our conviction.

23,904 views • 1 year ago •via X (Twitter)

11 Comments

Fudo's profile picture
Fudo1 year ago

@Justin_Bons agrees, yet religion or legal entanglement seem to prevent him from taking up the topic with Kadena. Honestly makes his relentless walls about the importance of L1 scaling intolerable to read. I used to be a fan of his, but the silence is deafening

🇰🇼MesH🇰🇼's profile picture
🇰🇼MesH🇰🇼1 year ago

11/Jan 1/2021 = 0.1 11/Nov 11/2021 = 28$ 1/Jan 1 /2025 = ? $KDA

Life70's profile picture
Life701 year ago

Kadena is the best chain❤️

Michael Anton Fischer's profile picture
Michael Anton Fischer1 year ago

There is no second best. To be the optimal medium of exchange and unit of account, it's best to not have to convert between different moneys. And since network effects grow exponentially with the number of users, once a certain user threshold has been passed, any new monetary technology has to be significantly better than the dominant one to replace it.

NoVa🅚.ⓚda's profile picture
NoVa🅚.ⓚda1 year ago

Words

El Pepe 🪄's profile picture
El Pepe 🪄1 year ago

Trust the process 💯

Lucius's profile picture
Lucius1 year ago

Yes it is, But when the people will realize ?

ibnuumair KDA's profile picture
ibnuumair KDA1 year ago

It has been always

Jalal's profile picture
Jalal1 year ago

$KDA 🔥

NoStrings.KDA's profile picture
NoStrings.KDA1 year ago

Time will tell

ⓀIvars87 KDA's profile picture
ⓀIvars87 KDA1 year ago

Layer 1

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Backpropagation by hand ✍️ ~ 11 steps walkthrough below Backpropagation is the algorithm that actually trains a neural network, and it is where most people stop following along. It is not calculus you cannot do. It is matrix multiplication, working backward, one layer at a time. So I drew and calculated one entirely by hand. Goal: push the loss gradient back through a 3-layer network and land on a new value for every weight and bias. = 1. Given = A 3-layer perceptron, an input X, predictions Ypred = [0.5, 0.5, 0], and the truth Ytarget = [0, 1, 0]. = 2. Backprop gradient cells = Let us draw empty cells for every gradient we are about to compute. The shape of the answer comes first. = 3. Layer 3 softmax = We get dL/dz3 straight from Ypred minus Ytarget = [0.5, -0.5, 0]. No chain rule needed, and that shortcut is the whole reason softmax and cross-entropy are paired. = 4. Layer 3 weights and biases = Let us multiply dL/dz3 by [a2 | 1]. One multiplication gives the gradient for W3 and b3 together. = 5. Layer 2 activations = We multiply dL/dz3 by W3 to get dL/da2. The gradient moves back across a layer the same way the signal moved forward. = 6. Layer 2 ReLU = Let us pass it through the gate: keep the gradient where the activation was positive, zero it everywhere else. = 7. Layer 2 weights and biases = We multiply dL/dz2 by [a1 | 1]. The same figure as step 4, one layer up. = 8. Layer 1 activations = Let us multiply dL/dz2 by W2. = 9. Layer 1 ReLU = We apply the same gate again, now on a1. = 10. Layer 1 weights and biases = Let us multiply dL/dz1 by [x | 1], and every weight in the network now has a gradient. = 11. Update = We subtract, and the network has learned. In practice a learning rate scales this step. The gradients: dL/dz3 = [0.5, -0.5, 0] dL/da1 = [1, -2, 2, -1] dL/dz1 = [0, -2, 2, -1] The takeaway: matrix multiplication is all you need. Just like the forward pass, backpropagation is matrix multiplications end to end. You can do every one by hand, slowly and imperfectly, which is exactly why a GPU's ability to do them fast mattered so much to deep learning. 💾 Save this post!

Tom Yeh

964,224 views • 3 months ago