Neural Symphony

Layers deep, neurons ignite
Input, hidden, output - a digital sight
Weights and biases, the network's might
Backpropagation sets it right

Feed forward, calculate
Activation function, elevate
(Sigmoid, ReLU, take your pick)

It's a neural symphony
(Multilayer Perceptron)
Mathematics in harmony
(Deep learning revolution)
From input to output, watch it flow
Patterns emerge, knowledge grows
In this neural symphony
(MLP, can't you see?)

Hidden layers, where magic unfolds
Each neuron a story untold
Gradient descent, errors controlled
Optimizing till the truth is gold

Adjust the weights, minimize loss
(Partial derivatives, mathematical boss)
Backpropagate, no data loss
(Chain rule application, learning across)

It's a neural symphony
(Multilayer Perceptron)
Mathematics in harmony
(Deep learning revolution)
From input to output, watch it flow
Patterns emerge, knowledge grows
In this neural symphony
(MLP, can't you see?)

Tensors flowing, dimensions high
Backpropagate, no data loss
Gradient descent, errors controlled
Optimizing till the truth is gold

Adjust the weights, minimize loss
Backpropagate, no data loss
Chain rule application, learning across

It's a neural symphony
(Multilayer Perceptron)
Mathematics in harmony
(Deep learning revolution)
From input to output, watch it flow
Patterns emerge, knowledge grows
In this neural symphony
(MLP, can't you see?)

One-zero-one-zero, neural code
Artificial neurons, knowledge overload
MLP, a mathematical ode
To the power of the algorithmic mode

Tensors flowing, dimensions high
Backpropagate, no data loss
Backpropagate, no data loss
Backpropagate, no data loss
Adjust the weights, minimize loss
Backpropagate, no data loss
Rule application, learning across
Chain rule application, mirror across
Adjust the weights, minimize loss
Backpropagate, no data loss
Nonlinearity, oh my, oh my
Stochastic gradient descent, we try
Learning rate tuned, watch it fly
Overfitting? Regularize!
Dropout layers, optimize