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Perceptron Layers | Neural Network from Scratch
Introduction to Neural Networks
course content

Conteúdo do Curso

Introduction to Neural Networks

Introduction to Neural Networks

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

bookPerceptron Layers

Perceptron is the name of the simplest neural network, consisting of only one hidden layer. However, in order to be able to solve more complex problems, we will create a variation of perceptron called multilayer perceptron (MLP). A multilayer perceptron consists of multiple hidden layers. The structure of a multilayer perceptron looks like this:

  1. An input layer: It receives the input data;
  2. Hidden layers: These layers process the data and extract patterns. We have two hidden layers in our model;
  3. Output layer: Produces the final prediction or classifications.

In general, each layer consists of multiple neurons, and the output from one layer becomes the input for the next layer.

Tarefa

Set up the basic structure of the perceptron by implementing its layers.

  1. Create neurons and specify number of their inputs.
  2. Active neurons for forward propagation.
  3. Define three layers: 2 hidden layers and 1 output layer.

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Seção 2. Capítulo 2
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bookPerceptron Layers

Perceptron is the name of the simplest neural network, consisting of only one hidden layer. However, in order to be able to solve more complex problems, we will create a variation of perceptron called multilayer perceptron (MLP). A multilayer perceptron consists of multiple hidden layers. The structure of a multilayer perceptron looks like this:

  1. An input layer: It receives the input data;
  2. Hidden layers: These layers process the data and extract patterns. We have two hidden layers in our model;
  3. Output layer: Produces the final prediction or classifications.

In general, each layer consists of multiple neurons, and the output from one layer becomes the input for the next layer.

Tarefa

Set up the basic structure of the perceptron by implementing its layers.

  1. Create neurons and specify number of their inputs.
  2. Active neurons for forward propagation.
  3. Define three layers: 2 hidden layers and 1 output layer.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 2. Capítulo 2
toggle bottom row

bookPerceptron Layers

Perceptron is the name of the simplest neural network, consisting of only one hidden layer. However, in order to be able to solve more complex problems, we will create a variation of perceptron called multilayer perceptron (MLP). A multilayer perceptron consists of multiple hidden layers. The structure of a multilayer perceptron looks like this:

  1. An input layer: It receives the input data;
  2. Hidden layers: These layers process the data and extract patterns. We have two hidden layers in our model;
  3. Output layer: Produces the final prediction or classifications.

In general, each layer consists of multiple neurons, and the output from one layer becomes the input for the next layer.

Tarefa

Set up the basic structure of the perceptron by implementing its layers.

  1. Create neurons and specify number of their inputs.
  2. Active neurons for forward propagation.
  3. Define three layers: 2 hidden layers and 1 output layer.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Perceptron is the name of the simplest neural network, consisting of only one hidden layer. However, in order to be able to solve more complex problems, we will create a variation of perceptron called multilayer perceptron (MLP). A multilayer perceptron consists of multiple hidden layers. The structure of a multilayer perceptron looks like this:

  1. An input layer: It receives the input data;
  2. Hidden layers: These layers process the data and extract patterns. We have two hidden layers in our model;
  3. Output layer: Produces the final prediction or classifications.

In general, each layer consists of multiple neurons, and the output from one layer becomes the input for the next layer.

Tarefa

Set up the basic structure of the perceptron by implementing its layers.

  1. Create neurons and specify number of their inputs.
  2. Active neurons for forward propagation.
  3. Define three layers: 2 hidden layers and 1 output layer.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Seção 2. Capítulo 2
Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
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