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Introduction to Neural Networks
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Conteúdo do Curso

Introduction to Neural Networks

Introduction to Neural Networks

1. Concept of Neural Network
What is a Neural Network?Neural Network StructureWhat is a Neuron?Activation FunctionsForward and Backward PropagationApplications of Deep Learning in the Real World
2. Neural Network from Scratch
Single Neuron ImplementationPerceptron LayersForward PropagationBackward PropagationData PreprocessingModel Training and EvaluationNeural Network with scikit-learn
3. Conclusion
Neural Networks or Traditional ModelsOther types of Neural NetworksManual Hyperparameter TuningAutomatic Hyperparameter TuningFinal QuizSummary

Perceptron 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.

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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Tudo estava claro?

Seção 2. Capítulo 2
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Perceptron 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.

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.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

Seção 2. Capítulo 2
toggle bottom row

Perceptron 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.

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.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

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.

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