Build Your First Neural Network in Python

Preparing Your Dataset

Step 1: Load the Dataset

Load the dataset that will be used to train and test the model.

Step 2: Split the Data

Split the dataset into:

• Training set – Used to train the model.
• Testing set – Used to evaluate the model's performance.

Step 3: Normalize the Data

Normalize numerical values so they fall within a consistent range, making it easier for the model to learn.

Step 4: Encode Labels

Convert labels into a format that the model can understand.

Step 5: Verify the Dataset

Ensure the dataset is clean and properly prepared before training the model.  

Building the Model

Step 1: Create a Sequential Model

Use the Sequential API to build the neural network by stacking layers one after another.

Step 2: Add the Input Layer

Create an input layer that matches the number of features in your dataset.

Step 3: Add Hidden Layers

Add one or two hidden layers, choosing:

• The number of neurons.
• An activation function, such as ReLU.

Step 4: Add the Output Layer

Create an output layer based on your task.

For example:

• Binary classification: One neuron with a Sigmoid activation function.
• Multi-class classification: Multiple neurons with a Softmax activation function.

Step 5: Review the Model

Check that the layers, number of neurons, and activation functions are correctly configured before training the model.  

Training and Evaluating Results

Step 1: Compile the Model

Compile the model by selecting:

• A loss function
• An optimizer

Step 2: Train the Model

Train the model using the training dataset over multiple passes, called epochs.

Step 3: Evaluate the Model

After training is complete, test the model using the testing dataset, which the model has never seen before.

Step 4: Measure Performance

Evaluate the model's performance to determine how well it has learned, rather than simply memorizing the training data.