What is a neural network in Python?

If you’ve ever used a voice app like Alexa or Siri, you’ve interacted with a neural network. (And of course, you already have your own neural network – in your brain.)

In machine learning, an artificial neural network (ANN) is an information processing system modeled after our brains. ANNs are the bread and butter of the popular method of advanced machine learning known as deep learning. ANNs are more simply called neural networks, and more recently, deep neural networks.

By learning how to harness neural networks, you can apply them to various interesting use-cases, such as natural-language processing (NLP) and computer vision.

Today we’ll talk about neural networks and how you can start working with them in Python.

An intro to neural networks#

A neural network represents an artificial intelligence technique that enables computers to analyze data, drawing inspiration from the human brain’s functioning. This approach, which is part of the broader machine learning domain known as deep learning, employs a network of nodes or neurons arranged in layers, mimicking the brain’s structure.

Neural networks are complex structures of machine and deep learning algorithms. A neural network is composed of several information processing units.

The information processing units in ANNs share a name with those in our brains: neurons. However, neurons in an ANN are also called nodes, artificial neurons, or perceptrons.

A perceptron is a machine learning algorithm used for binary classification of data. On its own, a single perceptron constitutes a single-layer neural network, which is the most basic type of neural network.

As a binary classifier, a perceptron can learn linear boundaries between classes, provided it’s given a set of linearly separable training data.

To illustrate, a perceptron uses a linear model to separate a given set of input data into two classes:

This is great, but most real-world data isn’t related through a linear relationship.

To perform more complex tasks, neural networks must be able to learn non-linear representations from real-world data. To do this, we need multiple perceptrons to be interconnected in multi-layer neural networks, or multi-layer perceptrons.

Multi-layer neural networks form the basis for deep learning.

When we talk about neural networks, we’re usually referring to multi-layer neural networks.

There are three types of layers in a multi-layer neural network:

• Input layer: Receives a data set of input data, passes inputs to hidden layer
• Hidden layer(s): Contains the main computational units; all computations are performed in these layers
• Output layers: Also contains computational units and returns output data. (There is a debate as to whether the output layer should be considered a hidden layer or not.)

The following figure illustrates a neural network with a single hidden layer:

These hidden layers are where the computation happens. Without hidden layers, a neural network will simply return output data identical to the input data.

Hidden layers are what truly enable deep learning.

Common types of neural networks#

While there are various types of neural networks, the most common are:

• Convolutional neural networks: These are commonly used to analyze images, and are the masterminds behind image and facial recognition.

• Recurrent neural networks (RNNs): These learn from sequential training data. They power speech-recognition apps. One type of RNN is the long short-term memory (LSTM) network, which is the type of neural network behind Google Translate.

• ​​Multilayer Perceptrons (MLPs): They are a foundational type of feedforward artificial neural network (ANN). As the simplest form of deep neural networks, MLPs consist of multiple fully connected layers. They offer a solution to the high computational demands of contemporary deep learning models by employing layers that function as nonlinear transformations of weighted sums from their preceding layers.

Neural networks can use different machine learning paradigms, including supervised learning, unsupervised learning, and reinforcement learning.

How neural networks work#

A neural network can receive unstructured data sets, classify data points, recognize patterns, and develop an internal representation through which it makes predictions about similar data sets.

Like humans, a neural network learns to perfect its craft over time. It goes through several iterations of computations and adjustments until it makes predictions to a reasonable accuracy.

Some of the key computational components in neural networks include:

• Activation functions: Each perceptron has an activation function that standardizes its output and prevents different units from collapsing. A common activation function is the sigmoid activation function. Other activation functions are the rectified linear unit (ReLU), leaky ReLU, and tanh.
• Weight: A value assigned to connections between perceptrons, estimated by the learning algorithm.

A neural network’s training process looks like this:

• Receives input data: Input data is received through the input layer and passed on to hidden layer(s)
• Generates outputs: The neural network usually does its initial computations by using random numbers as weight assignments
• Compares outputs: The error between the generated output and required output is represented through a loss function.
• Optimizes: An optimization algorithm is used to reduce the loss, an iterative process that repeats until the loss is minimized to a reasonably small value.

Our goal when training neural networks is to reduce the error or loss, which means that the network’s generated outputs will ideally match the required outputs. There are several types of loss functions, a common one being the cross-entropy loss function, which is typical in classification tasks.

To reduce the loss, we update the weights. At this stage, we don’t use random numbers as our weight assignments. Instead, we use optimization algorithms to determine the changes we need to make.

There are many optimization algorithms used to train neural networks. A popular one is the gradient descent algorithm. Gradient descent is an iterative optimization algorithm.

A commonly used variant of gradient descent is stochastic gradient descent, which is well suited for working with large data sets.

Data Preparation, Preprocessing, and Batching#

Before building any neural network, data preparation is the most important step.
Good preprocessing ensures faster convergence, higher accuracy, and more reliable results.

1. Load and split your dataset#

Start by dividing your data into training, validation, and test sets — typically 70/15/15 or similar ratios.

from sklearn.model_selection import train_test_split

X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3)
X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5)

You can also perform manual splits for custom datasets.

2. Normalization and standardization#

Scale features or pixel values to a consistent range to stabilize training.

X_train = X_train.astype('float32') / 255.0
X_test  = X_test.astype('float32') / 255.0

This is crucial for image and numerical data to ensure gradients don’t explode or vanish.

3. One-hot encoding for classification#

Convert integer labels into categorical vectors when working with classification problems.

y_train = tf.keras.utils.to_categorical(y_train, num_classes)

4. Batching and shuffling#

Use tf.data.Dataset to efficiently pipeline data into your training loop.

ds = tf.data.Dataset.from_tensor_slices((X_train, y_train))
ds = ds.shuffle(buffer_size=1024).batch(32).prefetch(tf.data.AUTOTUNE)

Batching improves GPU utilization, while shuffling avoids learning order bias.

5. Data augmentation (for images)#

Augment training data to improve generalization and reduce overfitting.

data_gen = tf.keras.preprocessing.image.ImageDataGenerator(
    rotation_range=10,
    horizontal_flip=True,
    width_shift_range=0.1,
    height_shift_range=0.1
)
data_gen.fit(X_train)

Why it matters#

A well-prepared dataset leads to better accuracy, faster convergence, and fewer training issues.
In deep learning, data quality and preprocessing often matter as much as the model itself.

Getting started with neural networks in Python#

Creating neural networks (NN) is one of the many amazing things you can do with the Python programming language.

On your way to mastering neural networks, you’ll need a few ingredients:

• Basic Python proficiency
• Gain an understanding of deep learning with Python through the Keras, TensorFlow, and PyTorch frameworks
• Basic familiarity with linear algebra, probability, and calculus

Here are the steps you need to follow to create a neural network in Python:

1. Import the essential libraries into your Python script.
2. Proceed to load and get the data ready for processing.
3. Construct the neural network model.
4. Assemble the model for training.
5. Initiate the training process for the model.
6. Conduct an assessment of the model’s performance.

Model Training, Callbacks, and Early Stopping#

After defining your model architecture, the next step is training — controlling how your model learns over time.
Keras and TensorFlow make this process both flexible and production-ready through callbacks and built-in monitoring.

1. Compile the model#

Specify the optimizer, loss function, and evaluation metrics before training.

model.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

2. Define callbacks#

Callbacks let you control training dynamically — pausing, saving, or adjusting parameters automatically.

callbacks = [
    tf.keras.callbacks.EarlyStopping(
        monitor='val_loss',
        patience=3,
        restore_best_weights=True
    ),
    tf.keras.callbacks.ReduceLROnPlateau(
        monitor='val_loss',
        factor=0.2,
        patience=2
    )
]

• EarlyStopping stops training when validation loss no longer improves, preventing overfitting.
• ReduceLROnPlateau lowers the learning rate when performance plateaus, allowing finer convergence.
• You can also include ModelCheckpoint to save weights mid-training.

3. Train the model#

Run the training loop and track progress on both training and validation datasets.

history = model.fit(
    train_dataset,
    epochs=20,
    validation_data=val_dataset,
    callbacks=callbacks
)

4. Evaluate and visualize results#

After training, visualize the loss and accuracy curves to identify trends:

• Diverging validation and training curves → overfitting
• Flat accuracy improvement → potential learning rate or data issues

import matplotlib.pyplot as plt

plt.plot(history.history['loss'], label='train_loss')
plt.plot(history.history['val_loss'], label='val_loss')
plt.legend()
plt.show()

Why it matters#

Callbacks make model training adaptive and efficient, helping you:

• Avoid wasted epochs
• Detect and handle plateaus
• Automatically restore the best weights

This approach streamlines experiments and improves model generalization.

Libraries and frameworks for neural networks in Python#

There are various libraries and modules you can use to start creating neural networks in Python:

• Keras: Deep learning framework focused on neural networks
• NumPy: Python library packed with high-level mathematical functions for multi-dimensional matrices and arrays
• pandas: Python library for data analysis and data manipulation
• scikit-learn: Python machine learning library for regression and classification
• Matplotlib: Python library for plotting and visualization
• TensorFlow: Machine learning and AI library focused on training neural networks

Neural networks in Python: Code example#

Let’s take a sneak peek at how we implement neural networks in Python.

The following Python code is a simple example of image classification using TensorFlow.

Image classification models learn to recognize images. They’re one of the common types of neural networks we discussed earlier: a convolutional neural network.

Model Evaluation, Saving, and Deployment#

After training, your next goals are to evaluate performance, save the model, and deploy it for real-world inference.
These steps turn your trained network into a usable asset.

1. Evaluation and metrics#

Use your test dataset to measure model accuracy and generalization.

loss, accuracy = model.evaluate(test_dataset)
print(f"Test accuracy: {accuracy:.4f}")

For classification problems, go beyond accuracy by checking precision, recall, F1-score, and the confusion matrix.

from sklearn.metrics import classification_report, confusion_matrix

y_pred = model.predict(test_dataset)
# Convert probabilities to class labels before evaluation
print(classification_report(y_true, y_pred_labels))
print(confusion_matrix(y_true, y_pred_labels))

These metrics give deeper insight into class imbalance or error types.

2. Saving and loading models#

Persist your trained model for later use or production deployment.

model.save('my_model.h5')
loaded = tf.keras.models.load_model('my_model.h5')

Alternatively, use TensorFlow’s SavedModel format for flexible serving:

model.save('exported_model')
loaded = tf.keras.models.load_model('exported_model')

Checkpointing during training (via ModelCheckpoint) also allows partial recovery if training is interrupted.

3. Inference and deployment#

To use the trained model for predictions:

pred = loaded.predict(new_data_batch)
predicted_class = np.argmax(pred, axis=1)

For production or mobile deployment:

• Convert to TensorFlow Lite (TFLite) for Android/iOS.
• Export to ONNX for interoperability with other frameworks.

Why it matters#

Model evaluation, saving, and deployment complete the ML lifecycle.
They let you:

• Quantify generalization quality
• Reuse and serve models across environments
• Optimize for lightweight inference on edge or cloud

With these steps, your training work becomes a deployable, reproducible system.

Wrapping up and next steps#

There’s more and more data to work with each day. Why not team up with a neural network in Python to do something big with that data?

To help you master neural networks, we’ve created the learning path: Become a Deep Learning Professional. This path covers deep learning fundamentals and includes severals hands-on tutorials and projects to help you master neural networks in Python.

Whether you are:

• A data scientist
• A self-taught innovator looking to change the world (or just someone’s day)
• A person who simply loves to learn (some call us nerds)

… you can harness neural networks to do some amazing things. We can’t wait to see what you do with your own NN.

Happy learning!