Table of Contents
Get hands-on with neural networks today.An intro to neural networksCommon types of neural networksHow do you choose the right neural network architecture?How neural networks workData Preparation, Preprocessing, and Batching1. Load and split your dataset2. Normalization and standardization3. One-hot encoding for classification4. Batching and shuffling5. Data augmentation (for images)Why it mattersGetting started with neural networks in PythonModel Training, Callbacks, and Early Stopping1. Compile the model2. Define callbacks3. Train the model4. Evaluate and visualize resultsWhy it mattersLibraries and frameworks for neural networks in PythonGet hands-on with neural networks today.Neural networks in Python: Code exampleModel Evaluation, Saving, and Deployment1. Evaluation and metrics2. Saving and loading models3. Inference and deploymentWhy it mattersWrapping up and next steps
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What is a neural network in Python? 2026 guide

What is a neural network in Python? 2026 guide

Learn how neural networks in Python transform data into intelligent predictions. Explore architectures, training workflows, TensorFlow and PyTorch tools, model deployment, and practical techniques for building real-world AI applications.

11 mins read
Jun 05, 2026
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A neural network is an information processing system modeled after the human brain, composed of interconnected nodes (neurons) arranged in layers that learn patterns from data. In Python, you can build neural networks using frameworks like TensorFlow, Keras, and PyTorch to tackle tasks such as image classification, natural-language processing, and speech recognition.

Key takeaways

  • Multi-layer architecture: Neural networks consist of an input layer, one or more hidden layers where computation happens, and an output layer that returns predictions.

  • Common network types: Convolutional neural networks (CNNs) handle image recognition, recurrent neural networks (RNNs) process sequential data, and multilayer perceptrons (MLPs) serve as the foundational feedforward architecture.

  • Training loop: The network receives input, generates outputs using weighted connections, measures error with a loss function, and iteratively optimizes weights using algorithms like stochastic gradient descent.

  • Data preparation matters: Preprocessing steps such as normalization, one-hot encoding, batching, and data augmentation are critical for faster convergence and higher model accuracy.

  • Full lifecycle in Python: Beyond training, production workflows include callbacks for early stopping, model evaluation with metrics like precision and recall, saving trained models, and deploying via TensorFlow Lite or ONNX.

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.

Get hands-on with neural networks today.#

Try one of our 300+ courses and learning paths: Become a Deep Learning Professional.


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:

widget

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.

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:

widget

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

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.

How do you choose the right neural network architecture?#

One of the most common questions beginners ask after learning about neural networks is which architecture they should use for a particular problem. The answer depends largely on the type of data you are working with and the patterns you want the model to learn.

For structured tabular data, multilayer perceptrons (MLPs) are often the best starting point. They are relatively simple, computationally efficient, and work well for classification and regression tasks involving customer data, financial records, or business metrics. When working with images, convolutional neural networks (CNNs) are typically preferred because they can automatically learn spatial patterns such as edges, shapes, and textures. This makes them highly effective for image classification, object detection, and facial recognition systems.

Sequential data introduces different challenges. Tasks such as speech recognition, language translation, and time-series forecasting often require models that can understand relationships across time. Recurrent neural networks (RNNs) and their variants, such as LSTMs, were designed specifically for these scenarios. More recently, transformer-based architectures have become increasingly popular for natural language processing and generative AI applications because of their ability to capture long-range dependencies efficiently.

Understanding the strengths of each architecture helps you avoid unnecessary experimentation and select a model that aligns with both your data and your objectives.

Neural Network Type

Best For

Common Applications

MLP (Multilayer Perceptron)

Structured tabular data

Classification, regression, fraud detection

CNN

Images and spatial data

Image recognition, object detection, medical imaging

RNN

Sequential data

Speech recognition, time-series forecasting

LSTM

Long sequences

Language translation, speech processing

Transformer

Large-scale language and sequence tasks

Chatbots, LLMs, text generation, NLP

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.

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

Get hands-on with neural networks today.#

Try one of our 300+ courses and learning paths: Become a Deep Learning Professional.

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.

Python
# Import packages
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.utils import plot_model
# Build neural network model
def build_model():
    
    # instantiate a Sequential class and linearly stack the layers of your model
    seq_model = tf.keras.models.Sequential([tf.keras.layers.Flatten(input_shape=(28, 28)),
                                            tf.keras.layers.Dense(128, activation=tf.nn.relu),
                                            tf.keras.layers.Dense(10, activation=tf.nn.softmax)])
    return seq_model
# Instantiate the model and plot it
model = build_model_with_functional()
plot_model(model)
#Load and prepare data
mnist = tf.keras.datasets.fashion_mnist
(training_images, training_labels), (test_images, test_labels) = mnist.load_data()
training_images = training_images / 255.0
test_images = test_images / 255.0
# Set optimization algorithm
model.compile(optimizer=tf.optimizers.Adam(),
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
# Train and evaluate the model
model.fit(training_images, training_labels, epochs=5)
model.evaluate(test_images, test_labels)

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!

Frequently Asked Questions

What is a neural network in Python?

A neural network is a system that makes predictions. This is done through a systematic learning process, which includes:

  1. Ingesting input data
  2. Formulating a prediction
  3. Evaluating the precision of the prediction in comparison to the expected result.
  4. Refining its internal mechanisms to improve prediction accuracy in subsequent iterations.
Written By:
Erica Vartanian
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