A breakdown of the neural network learning loop, from the individual neuron's math to the global process of backpropagation and weight updates.
Apr 25, 20261 min listen3 chapters
What you'll learn
The core learning loop: forward pass, loss, and backpropagation
How individual neurons perform weighted sums and activations
The role of gradient descent in adjusting network weights
How local mathematical operations create global behavior
The Learning Loop
diagram
note
Core loop
input goes in
each layer transforms it
output is compared to the target
the error is used to adjust weights
equation
y^=f(x;W,b)L=loss(y^,y)
Training Dynamics
diagram
illustration
The Anatomy of a Neuron
diagram
equation
z=w⋅x+ba=σ(z)θ←θ−η∇θL
note
Core loop
forward pass: data goes in, prediction comes out
loss: measure how wrong the prediction is
backpropagation: compute which weights caused the error
gradient descent: nudge weights to reduce the loss
note
One neuron
A neuron computes a weighted sum, adds a bias, then applies an activation.
Input → mix → threshold / squash → output
Transcript
Hi Siya, good to see you.Neural networks are interesting because they turn lots of tiny local rules into one global behavior, and the trick is in how those pieces fit together.Hi Siya, good to see you.Do you want the intuition first, the math path, or a concrete example like image classification?
Yes — if you mean **how neural networks are “trained” or “built,”** I can draw that, and the cleanest picture is the learning loop: input goes in, prediction comes out, then error pushes the weights to change.Let me sketch that first, and then I’ll show the tiny piece that makes it learn.
Absolutely — I can draw one.Let me sketch the core machinery of a neural network first, then I’ll connect it to your “how are they dreamed?” question, because I think you may be asking how they’re trained.Absolutely — I can draw one.Let me sketch the core machinery of a neural network first, then I’ll connect it to your “how are they dreamed?” question, because I think you may be asking how they’re trained.
Let’s start at the very beginning: a neural network is just a stack of simple math units that turn inputs into outputs.Each unit takes numbers in, mixes them with weights, adds a bias, and passes the result through an activation function — that’s the tiny “decision” step.Let’s start at the very beginning: a neural network is just a stack of simple math units that turn inputs into outputs.Each unit takes numbers in, mixes them with weights, adds a bias, and passes the result through an activation function — that’s the tiny “decision” step.
