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Neural Network with scikit-learn | 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

bookNeural Network with scikit-learn

Working with neural networks can be quite tricky, especially if you're trying to build them from scratch. Instead of manually coding algorithms and formulas, you can use ready-made tools such as the sklearn library.

To create the same model as in this section, we can use the MLPClassifier class from the sklearn library:

Note

Neural networks in sklearn determine the number of inputs and outputs based on the data they are trained on. Therefore, there is no need to set them manually.

Benefits of using libraries to build neural networks:

  1. Ease of use: You don't have to dive deep into the details of each algorithm. You can simply use ready-made methods and classes;
  2. Optimization: The sklearn library is optimized for performance, which can reduce the training time of your model;
  3. Extensive Documentation: sklearn provides extensive documentation with usage examples, which can greatly speed up the learning process;
  4. Compatibility: sklearn integrates well with other popular Python libraries such as numpy, pandas and matplotlib.

Here you can see basic commands of MLPClassifier usage:

Changing model parameters:

Training the model:

Predict output values:

Tarefa

Recreate the neural network using the sklearn library and train it on all features:

  1. Set up parameters of the model: 100 epochs, two hidden layers of 10 neurons each, learning rate is 0.5.
  2. Train the model.
  3. Evaluate the model.

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Tudo estava claro?

Como podemos melhorá-lo?

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Seção 2. Capítulo 7
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bookNeural Network with scikit-learn

Working with neural networks can be quite tricky, especially if you're trying to build them from scratch. Instead of manually coding algorithms and formulas, you can use ready-made tools such as the sklearn library.

To create the same model as in this section, we can use the MLPClassifier class from the sklearn library:

Note

Neural networks in sklearn determine the number of inputs and outputs based on the data they are trained on. Therefore, there is no need to set them manually.

Benefits of using libraries to build neural networks:

  1. Ease of use: You don't have to dive deep into the details of each algorithm. You can simply use ready-made methods and classes;
  2. Optimization: The sklearn library is optimized for performance, which can reduce the training time of your model;
  3. Extensive Documentation: sklearn provides extensive documentation with usage examples, which can greatly speed up the learning process;
  4. Compatibility: sklearn integrates well with other popular Python libraries such as numpy, pandas and matplotlib.

Here you can see basic commands of MLPClassifier usage:

Changing model parameters:

Training the model:

Predict output values:

Tarefa

Recreate the neural network using the sklearn library and train it on all features:

  1. Set up parameters of the model: 100 epochs, two hidden layers of 10 neurons each, learning rate is 0.5.
  2. Train the model.
  3. Evaluate the model.

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 7
toggle bottom row

bookNeural Network with scikit-learn

Working with neural networks can be quite tricky, especially if you're trying to build them from scratch. Instead of manually coding algorithms and formulas, you can use ready-made tools such as the sklearn library.

To create the same model as in this section, we can use the MLPClassifier class from the sklearn library:

Note

Neural networks in sklearn determine the number of inputs and outputs based on the data they are trained on. Therefore, there is no need to set them manually.

Benefits of using libraries to build neural networks:

  1. Ease of use: You don't have to dive deep into the details of each algorithm. You can simply use ready-made methods and classes;
  2. Optimization: The sklearn library is optimized for performance, which can reduce the training time of your model;
  3. Extensive Documentation: sklearn provides extensive documentation with usage examples, which can greatly speed up the learning process;
  4. Compatibility: sklearn integrates well with other popular Python libraries such as numpy, pandas and matplotlib.

Here you can see basic commands of MLPClassifier usage:

Changing model parameters:

Training the model:

Predict output values:

Tarefa

Recreate the neural network using the sklearn library and train it on all features:

  1. Set up parameters of the model: 100 epochs, two hidden layers of 10 neurons each, learning rate is 0.5.
  2. Train the model.
  3. Evaluate the model.

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!

Working with neural networks can be quite tricky, especially if you're trying to build them from scratch. Instead of manually coding algorithms and formulas, you can use ready-made tools such as the sklearn library.

To create the same model as in this section, we can use the MLPClassifier class from the sklearn library:

Note

Neural networks in sklearn determine the number of inputs and outputs based on the data they are trained on. Therefore, there is no need to set them manually.

Benefits of using libraries to build neural networks:

  1. Ease of use: You don't have to dive deep into the details of each algorithm. You can simply use ready-made methods and classes;
  2. Optimization: The sklearn library is optimized for performance, which can reduce the training time of your model;
  3. Extensive Documentation: sklearn provides extensive documentation with usage examples, which can greatly speed up the learning process;
  4. Compatibility: sklearn integrates well with other popular Python libraries such as numpy, pandas and matplotlib.

Here you can see basic commands of MLPClassifier usage:

Changing model parameters:

Training the model:

Predict output values:

Tarefa

Recreate the neural network using the sklearn library and train it on all features:

  1. Set up parameters of the model: 100 epochs, two hidden layers of 10 neurons each, learning rate is 0.5.
  2. Train the model.
  3. Evaluate the model.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Seção 2. Capítulo 7
Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
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