E = mc²
∇²ψ = -8πmE/h² ψ
∂f/∂t + v·∇f = 0
∫₀^∞ e^(-x²) dx = √π/2
lim(n→∞) (1+1/n)^n = e
∂²u/∂t² = c²∇²u
det(A - λI) = 0
∑ᵢ₌₁ⁿ
∫∂Ω
∇×
∮
≈
L = -∑yᵢlog(ŷᵢ)
∇θJ(θ) = 0
σ(z) = 1/(1+e^(-z))
y = WX + b
w = w - α∇L(w)
Adam(w, α, β₁, β₂)
ReLU(x) = max(0, x)
softmax(xᵢ) = e^xᵢ / ∑e^xⱼ
○→○→○
[784]→[128]→[10]
Input→Hidden→Output
●●●→●●●→●
[L₁,L₂,L₃] Layers
Training Model...
Epoch: 1000
Loss: 0.001↓
Accuracy: 99.8%↑
Converging...
Improving...
Learning Rate: 0.001
Batch Size: 32
model.fit(X, y)
forward_pass()
backpropagate()
update_weights()
tf.keras.Sequential
torch.nn.Module
🧠
⚡
🔮
📈
🎯
[1 0 0; 0 1 0; 0 0 1]
[[θ₀, θ₁], [θ₂, θ₃]]
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