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Aprende Forward Propagation | Neural Network from Scratch
Introduction to Neural Networks
course content

Contenido del Curso

Introduction to Neural Networks

Introduction to Neural Networks

1. Concept of Neural Network
2. Neural Network from Scratch
3. Conclusion

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Forward Propagation

You have already implemented forward propagation for a single layer in the previous chapter. Now, the goal is to implement complete forward propagation, from inputs to outputs.

To implement the entire forward propagation process, you need to define the forward() method in the Perceptron class. This method performs forward propagation layer by layer by calling the respective method for each layer:

python

The inputs pass through the first hidden layer, with each layer's outputs serving as inputs for the next, until reaching the final layer to produce the final output.

Tarea

Swipe to start coding

Your goal is to implement forward propagation for the perceptron:

  1. Iterate over the layers of the perceptron.
  2. Pass x through each layer in the network sequentially.
  3. Return the final output after all layers have processed the input.

If the forward() method is implemented correctly, the perceptron should output a single number between 0 and 1 when given certain inputs (e.g, [1, 0]).

Solución

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¿Cómo podemos mejorarlo?

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Sección 2. Capítulo 5
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book
Forward Propagation

You have already implemented forward propagation for a single layer in the previous chapter. Now, the goal is to implement complete forward propagation, from inputs to outputs.

To implement the entire forward propagation process, you need to define the forward() method in the Perceptron class. This method performs forward propagation layer by layer by calling the respective method for each layer:

python

The inputs pass through the first hidden layer, with each layer's outputs serving as inputs for the next, until reaching the final layer to produce the final output.

Tarea

Swipe to start coding

Your goal is to implement forward propagation for the perceptron:

  1. Iterate over the layers of the perceptron.
  2. Pass x through each layer in the network sequentially.
  3. Return the final output after all layers have processed the input.

If the forward() method is implemented correctly, the perceptron should output a single number between 0 and 1 when given certain inputs (e.g, [1, 0]).

Solución

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 2. Capítulo 5
Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
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