Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Data Preprocessing | Neural Network from Scratch
Introduction to Neural Networks
course content

Contenido del 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 NetworksChallenge: Manual Hyperparameter TuningChallenge: Automatic Hyperparameter TuningQuiz: ConclusionSummary

bookData Preprocessing

Wine Dataset

Now we will try to train our model on more realistic task. There is a wine dataset in scikit-learn library that we will use to predict wine class. We will use 3 input parameters for prediction.

Here you can see how this dataset look like:


              
12345678910

            
import pandas as pd  # Import pandas to create a DataFrame from loaded dataset
from sklearn.datasets import load_wine  # Import dataset loading function

wine_ds = load_wine()  # Load the dataset
X = pd.DataFrame(wine_ds.data, columns=wine_ds.feature_names)[['flavanoids', 'proline', 'total_phenols']]  # Extract input values from the dataset
y = pd.DataFrame(wine_ds.target, columns=['target'])  # Extract output values from the dataset

# Display the datasets
display(X.head())  # `X` is our input values, they are used to predict target value 
display(pd.DataFrame(y.value_counts()))  # `y` is a target value, that we want to predict; it has 3 target classes
copy

To train our model, we'll use three input parameters: flavanoids, proline, and total_phenols. For now, we have chosen these parameters as one of those with the highest correlation. This is done in order to reduce the size of the neural network required for successful training and reduce the time spent on the training process.

Data Preprocessing

Here's how we'll prepare the data for training:

  1. Data Scaling: Neural networks differ from decision trees or random forests in that they require data scaling for better performance. This step is crucial for reasons such as ensuring numerical stability, achieving faster convergence, and ensuring unit independence, etc. Always scale your data before passing it trough a neural network;
  2. One-Hot Encoding: Our target values comprise three classes, represented in a single column by the numbers 0, 1, and 2. For enhanced neural network performance, it's more effective to encode these classes into three distinct columns;
  3. Train-Test Data Split: Using the same dataset for both training and testing won't give us a realistic measure of the model's performance on new, unseen data.

Tarea

Prepare the wine dataset to work with our neural network:

  1. Extract input values from the dataset.
  2. Scale input values.
  3. Split data into train and test sets (40% of data will be used as test data).

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 2. Capítulo 5
toggle bottom row

bookData Preprocessing

Wine Dataset

Now we will try to train our model on more realistic task. There is a wine dataset in scikit-learn library that we will use to predict wine class. We will use 3 input parameters for prediction.

Here you can see how this dataset look like:


              
12345678910

            
import pandas as pd  # Import pandas to create a DataFrame from loaded dataset
from sklearn.datasets import load_wine  # Import dataset loading function

wine_ds = load_wine()  # Load the dataset
X = pd.DataFrame(wine_ds.data, columns=wine_ds.feature_names)[['flavanoids', 'proline', 'total_phenols']]  # Extract input values from the dataset
y = pd.DataFrame(wine_ds.target, columns=['target'])  # Extract output values from the dataset

# Display the datasets
display(X.head())  # `X` is our input values, they are used to predict target value 
display(pd.DataFrame(y.value_counts()))  # `y` is a target value, that we want to predict; it has 3 target classes
copy

To train our model, we'll use three input parameters: flavanoids, proline, and total_phenols. For now, we have chosen these parameters as one of those with the highest correlation. This is done in order to reduce the size of the neural network required for successful training and reduce the time spent on the training process.

Data Preprocessing

Here's how we'll prepare the data for training:

  1. Data Scaling: Neural networks differ from decision trees or random forests in that they require data scaling for better performance. This step is crucial for reasons such as ensuring numerical stability, achieving faster convergence, and ensuring unit independence, etc. Always scale your data before passing it trough a neural network;
  2. One-Hot Encoding: Our target values comprise three classes, represented in a single column by the numbers 0, 1, and 2. For enhanced neural network performance, it's more effective to encode these classes into three distinct columns;
  3. Train-Test Data Split: Using the same dataset for both training and testing won't give us a realistic measure of the model's performance on new, unseen data.

Tarea

Prepare the wine dataset to work with our neural network:

  1. Extract input values from the dataset.
  2. Scale input values.
  3. Split data into train and test sets (40% of data will be used as test data).

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 2. Capítulo 5
toggle bottom row

bookData Preprocessing

Wine Dataset

Now we will try to train our model on more realistic task. There is a wine dataset in scikit-learn library that we will use to predict wine class. We will use 3 input parameters for prediction.

Here you can see how this dataset look like:


              
12345678910

            
import pandas as pd  # Import pandas to create a DataFrame from loaded dataset
from sklearn.datasets import load_wine  # Import dataset loading function

wine_ds = load_wine()  # Load the dataset
X = pd.DataFrame(wine_ds.data, columns=wine_ds.feature_names)[['flavanoids', 'proline', 'total_phenols']]  # Extract input values from the dataset
y = pd.DataFrame(wine_ds.target, columns=['target'])  # Extract output values from the dataset

# Display the datasets
display(X.head())  # `X` is our input values, they are used to predict target value 
display(pd.DataFrame(y.value_counts()))  # `y` is a target value, that we want to predict; it has 3 target classes
copy

To train our model, we'll use three input parameters: flavanoids, proline, and total_phenols. For now, we have chosen these parameters as one of those with the highest correlation. This is done in order to reduce the size of the neural network required for successful training and reduce the time spent on the training process.

Data Preprocessing

Here's how we'll prepare the data for training:

  1. Data Scaling: Neural networks differ from decision trees or random forests in that they require data scaling for better performance. This step is crucial for reasons such as ensuring numerical stability, achieving faster convergence, and ensuring unit independence, etc. Always scale your data before passing it trough a neural network;
  2. One-Hot Encoding: Our target values comprise three classes, represented in a single column by the numbers 0, 1, and 2. For enhanced neural network performance, it's more effective to encode these classes into three distinct columns;
  3. Train-Test Data Split: Using the same dataset for both training and testing won't give us a realistic measure of the model's performance on new, unseen data.

Tarea

Prepare the wine dataset to work with our neural network:

  1. Extract input values from the dataset.
  2. Scale input values.
  3. Split data into train and test sets (40% of data will be used as test data).

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Wine Dataset

Now we will try to train our model on more realistic task. There is a wine dataset in scikit-learn library that we will use to predict wine class. We will use 3 input parameters for prediction.

Here you can see how this dataset look like:


              
12345678910

            
import pandas as pd  # Import pandas to create a DataFrame from loaded dataset
from sklearn.datasets import load_wine  # Import dataset loading function

wine_ds = load_wine()  # Load the dataset
X = pd.DataFrame(wine_ds.data, columns=wine_ds.feature_names)[['flavanoids', 'proline', 'total_phenols']]  # Extract input values from the dataset
y = pd.DataFrame(wine_ds.target, columns=['target'])  # Extract output values from the dataset

# Display the datasets
display(X.head())  # `X` is our input values, they are used to predict target value 
display(pd.DataFrame(y.value_counts()))  # `y` is a target value, that we want to predict; it has 3 target classes
copy

To train our model, we'll use three input parameters: flavanoids, proline, and total_phenols. For now, we have chosen these parameters as one of those with the highest correlation. This is done in order to reduce the size of the neural network required for successful training and reduce the time spent on the training process.

Data Preprocessing

Here's how we'll prepare the data for training:

  1. Data Scaling: Neural networks differ from decision trees or random forests in that they require data scaling for better performance. This step is crucial for reasons such as ensuring numerical stability, achieving faster convergence, and ensuring unit independence, etc. Always scale your data before passing it trough a neural network;
  2. One-Hot Encoding: Our target values comprise three classes, represented in a single column by the numbers 0, 1, and 2. For enhanced neural network performance, it's more effective to encode these classes into three distinct columns;
  3. Train-Test Data Split: Using the same dataset for both training and testing won't give us a realistic measure of the model's performance on new, unseen data.

Tarea

Prepare the wine dataset to work with our neural network:

  1. Extract input values from the dataset.
  2. Scale input values.
  3. Split data into train and test sets (40% of data will be used as test data).

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 2. Capítulo 5
Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
some-alt