Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Manual Hyperparameter Tuning | Conclusion
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

Manual Hyperparameter Tuning

Tarefa

Until now, we have used the neural network only for the classification task. Now try to solve the regression problem using MLPRegression.

To do this, we will get the real world California Housing dataset and try to teach the model to predict median home value for households in tens of thousands of US dollars. Your goal is to have the Mean Absolute Error (MAE) be less than 0.4.

  1. Experiment with the hidden layers (use 1-2 hidden layers with 10-30 neurons for each).
  2. Experiment with the number of epochs (change number of epochs from 10 to 50).
  3. Experiment with the learning rate (change it in from 0.0001 to 1).

Make sure your MAE is less than 0.4 before submitting.

Tarefa

Until now, we have used the neural network only for the classification task. Now try to solve the regression problem using MLPRegression.

To do this, we will get the real world California Housing dataset and try to teach the model to predict median home value for households in tens of thousands of US dollars. Your goal is to have the Mean Absolute Error (MAE) be less than 0.4.

  1. Experiment with the hidden layers (use 1-2 hidden layers with 10-30 neurons for each).
  2. Experiment with the number of epochs (change number of epochs from 10 to 50).
  3. Experiment with the learning rate (change it in from 0.0001 to 1).

Make sure your MAE is less than 0.4 before submitting.

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

Tudo estava claro?

Seção 3. Capítulo 3
toggle bottom row

Manual Hyperparameter Tuning

Tarefa

Until now, we have used the neural network only for the classification task. Now try to solve the regression problem using MLPRegression.

To do this, we will get the real world California Housing dataset and try to teach the model to predict median home value for households in tens of thousands of US dollars. Your goal is to have the Mean Absolute Error (MAE) be less than 0.4.

  1. Experiment with the hidden layers (use 1-2 hidden layers with 10-30 neurons for each).
  2. Experiment with the number of epochs (change number of epochs from 10 to 50).
  3. Experiment with the learning rate (change it in from 0.0001 to 1).

Make sure your MAE is less than 0.4 before submitting.

Tarefa

Until now, we have used the neural network only for the classification task. Now try to solve the regression problem using MLPRegression.

To do this, we will get the real world California Housing dataset and try to teach the model to predict median home value for households in tens of thousands of US dollars. Your goal is to have the Mean Absolute Error (MAE) be less than 0.4.

  1. Experiment with the hidden layers (use 1-2 hidden layers with 10-30 neurons for each).
  2. Experiment with the number of epochs (change number of epochs from 10 to 50).
  3. Experiment with the learning rate (change it in from 0.0001 to 1).

Make sure your MAE is less than 0.4 before submitting.

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

Tudo estava claro?

Seção 3. Capítulo 3
toggle bottom row

Manual Hyperparameter Tuning

Tarefa

Until now, we have used the neural network only for the classification task. Now try to solve the regression problem using MLPRegression.

To do this, we will get the real world California Housing dataset and try to teach the model to predict median home value for households in tens of thousands of US dollars. Your goal is to have the Mean Absolute Error (MAE) be less than 0.4.

  1. Experiment with the hidden layers (use 1-2 hidden layers with 10-30 neurons for each).
  2. Experiment with the number of epochs (change number of epochs from 10 to 50).
  3. Experiment with the learning rate (change it in from 0.0001 to 1).

Make sure your MAE is less than 0.4 before submitting.

Tarefa

Until now, we have used the neural network only for the classification task. Now try to solve the regression problem using MLPRegression.

To do this, we will get the real world California Housing dataset and try to teach the model to predict median home value for households in tens of thousands of US dollars. Your goal is to have the Mean Absolute Error (MAE) be less than 0.4.

  1. Experiment with the hidden layers (use 1-2 hidden layers with 10-30 neurons for each).
  2. Experiment with the number of epochs (change number of epochs from 10 to 50).
  3. Experiment with the learning rate (change it in from 0.0001 to 1).

Make sure your MAE is less than 0.4 before submitting.

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

Tudo estava claro?

Tarefa

Until now, we have used the neural network only for the classification task. Now try to solve the regression problem using MLPRegression.

To do this, we will get the real world California Housing dataset and try to teach the model to predict median home value for households in tens of thousands of US dollars. Your goal is to have the Mean Absolute Error (MAE) be less than 0.4.

  1. Experiment with the hidden layers (use 1-2 hidden layers with 10-30 neurons for each).
  2. Experiment with the number of epochs (change number of epochs from 10 to 50).
  3. Experiment with the learning rate (change it in from 0.0001 to 1).

Make sure your MAE is less than 0.4 before submitting.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Seção 3. Capítulo 3
Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
We're sorry to hear that something went wrong. What happened?
some-alt