Conteúdo do Curso
Introduction to Neural Networks
Introduction to Neural Networks
Neural 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:
- 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;
- Optimization: The
sklearn
library is optimized for performance, which can reduce the training time of your model; - Extensive Documentation:
sklearn
provides extensive documentation with usage examples, which can greatly speed up the learning process; - Compatibility:
sklearn
integrates well with other popular Python libraries such asnumpy
,pandas
andmatplotlib
.
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:
- Set up parameters of the model:
100
epochs, two hidden layers of10
neurons each, learning rate is0.5
. - Train the model.
- Evaluate the model.
Obrigado pelo seu feedback!
Neural 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:
- 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;
- Optimization: The
sklearn
library is optimized for performance, which can reduce the training time of your model; - Extensive Documentation:
sklearn
provides extensive documentation with usage examples, which can greatly speed up the learning process; - Compatibility:
sklearn
integrates well with other popular Python libraries such asnumpy
,pandas
andmatplotlib
.
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:
- Set up parameters of the model:
100
epochs, two hidden layers of10
neurons each, learning rate is0.5
. - Train the model.
- Evaluate the model.
Obrigado pelo seu feedback!
Neural 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:
- 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;
- Optimization: The
sklearn
library is optimized for performance, which can reduce the training time of your model; - Extensive Documentation:
sklearn
provides extensive documentation with usage examples, which can greatly speed up the learning process; - Compatibility:
sklearn
integrates well with other popular Python libraries such asnumpy
,pandas
andmatplotlib
.
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:
- Set up parameters of the model:
100
epochs, two hidden layers of10
neurons each, learning rate is0.5
. - Train the model.
- Evaluate the model.
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:
- 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;
- Optimization: The
sklearn
library is optimized for performance, which can reduce the training time of your model; - Extensive Documentation:
sklearn
provides extensive documentation with usage examples, which can greatly speed up the learning process; - Compatibility:
sklearn
integrates well with other popular Python libraries such asnumpy
,pandas
andmatplotlib
.
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:
- Set up parameters of the model:
100
epochs, two hidden layers of10
neurons each, learning rate is0.5
. - Train the model.
- Evaluate the model.