Top #neural dweets

function u(t) {

c.width=1920 // clear the canvas
for(i=0;i<9;i++)
x.fillRect(400+i*100+S(t)*300,400,50,200) // draw 50x200 rects

} // 122/140

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    u(t) is called 60 times per second.
    t: Elapsed time in seconds.
    S: Shorthand for Math.sin.
    C: Shorthand for Math.cos.
    T: Shorthand for Math.tan.
    R: Function that generates rgba-strings, usage ex.: R(255, 255, 255, 0.5)
    c: A 1920x1080 canvas.
    x: A 2D context for that canvas.

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rodrigo.siqueira

function u(t) {

η=.1
ℵ=1/t
for(X=-3;X<3;X+=.01){
y=X**3-X*2
ℵ=ℵ-η*(ℵ**3-2*ℵ)
x.fillRect(940+X*400,-y*100+500,5,5)
}
x.fillRect(910+ℵ*400,470,60,60)
throw(ℵ)

Aug 21 2019 1:45 PM UTC

}// 140/140

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Gradient Descent (iterative) algorithm used to calculate sqrt(2). The big dots are the two roots -sqrt(2) and +sqrt(2) of equation Y=X**3-X*2. The Gradient Descent is an optimization algorithm for finding a local minimum of a function near a starting point, taking successive steps in the direction of the negative of the gradient. x[n+1]=x[n]−η(∇f)xn where η is the "Learning Rate". Gradient Descent is widely used for training Machine Learning models. #neural #ai

u/rodrigo.siqueira

Simple version that calculates sqrt(2). If you change initial "x" to -1, it will converge to -sqrt(2), that is the other root of the equation Y=X**3-X*2. Code: x = 1; learning_rate = .1; for(i=0;i<50;i++) { x = x - learning_rate * (x**3 - 2*x) } throw(x);

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rodrigo.siqueira

function u(t) {

for(H=300,c.width|=i=2e3;i--;)A=i<H?(B=1,0):i<600?(B=-.2,1):i<900?(B=0,-.2):B=0,x.fillRect(i,600-(2*t&1)*(A-A*(μ=.5-C(i%H/94)/2)+B*μ)*H,6,6)

Mar 18 2019 5:57 PM UTC

}// 140/140

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Pulses of a single neuron in a Spiking Neural Network (SNN) using cosine interpolation with 4 intervals: depolarization (rising curve), repolarization (falling curve), refractory (recovery period) and resting potential (continuous line). #neural #ai

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rodrigo.siqueira

remix of d/14191 by u/rodrigo.siqueira

function u(t) {

f=(A,B,s,z)=>s>1&&f(X=A+S(z)*s*1.6,Y=B+s*C(z*1.2),s/2,S(z/2)*10)
for(i=1e4;i--;)f(0,0,250,i+t),x.fillRect(X+960,Y+510,.8,.8)

May 26 2019 9:40 PM UTC

}// 124/140

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Connectome (brain circuit)

u/katkip

WOOW
u/rodrigo.siqueira

#neural

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rodrigo.siqueira

function u(t) {

N=156*t;if(t<1)for(W=c.width|=i=8;i--;)for(j=8;j--;)x.lineTo(0,i*N),x.lineTo(W,j*N),x.moveTo(W,i*N),x.stroke()

Mar 17 2019 1:23 AM UTC

}// 110/140

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Multi-layer fully connected neural network in 110 characters. #neural

u/katkip

#ai
u/Jezza

Can it dweet?

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rodrigo.siqueira

remix of d/23595 by u/TGY

function u(t) {

c.width|=N=115
for(i=8;i--;)for(j=9;j--;x.lineWidth=3)
x.lineTo(N,i*N+N),x.lineTo(960,60+j*N+S(N*t*i)*15+1),x.lineTo(1920,i*N+N)
x.stroke()

Aug 20 2021 10:55 PM UTC

}// 139/140

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Active 3-Layer Neural Network #neuralnetwork #neural #ai

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