Neural Network (NN)

A Neural Network (NN) is a computational model consisting of interconnected nodes that processes information by mimicking the biological structure of the human brain. As a cornerstone of modern Artificial Intelligence (AI), these networks are capable of learning from data to recognize patterns, classify information, and predict outcomes. While they are a subset of Machine Learning (ML), neural networks distinguish themselves by their ability to model complex, non-linear relationships, making them the engine behind breakthrough technologies like generative AI and autonomous systems. You can read more about the biological inspiration in this overview of neural networks by IBM.

Architecture of a Neural Network

The structure of a neural network is composed of layers of nodes, often referred to as artificial neurons. These layers facilitate the flow of data from input to output through a series of mathematical transformations.

• Input Layer: This is the entry point where the network receives raw training data, such as pixel values from an image or numerical features from a dataset.
• Hidden Layers: Situated between the input and output, these layers perform the bulk of the computation. Each neuron in a hidden layer applies model weights and biases to the input it receives. To decide whether a neuron should "fire" or pass a signal forward, an activation function—such as ReLU or Sigmoid—is applied.
• Output Layer: The final layer produces the network's prediction, such as a classification label (e.g., "cat" vs. "dog") or a continuous value.

To "learn," the network uses a process called model training. During training, the network compares its predictions to the actual correct answers using a loss function. An algorithm known as backpropagation then calculates the error gradient, and an optimization algorithm like stochastic gradient descent (SGD) or Adam adjusts the weights to minimize errors over multiple epochs.

Neural Networks vs. Deep Learning

It is common to confuse Neural Networks with Deep Learning (DL). The primary difference lies in complexity and depth. A "shallow" neural network might have only one or two hidden layers. In contrast, Deep Learning involves "deep" neural networks with many hidden layers, allowing the model to learn hierarchical features automatically. For a deeper dive into this distinction, explore this explanation of Deep Learning by MIT News. Practically, all Deep Learning models are Neural Networks, but not all Neural Networks qualify as Deep Learning.

Real-World Applications

Neural networks drive innovation across virtually every industry by automating tasks that previously required human intelligence.

1. Computer Vision: In the field of Computer Vision (CV), specialized networks called Convolutional Neural Networks (CNNs) are used to analyze visual data. For example, Ultralytics YOLO11 utilizes a deep CNN architecture for real-time object detection, enabling applications ranging from AI in agriculture for crop monitoring to safety features in autonomous vehicles.
2. Natural Language Processing: For tasks involving text and speech, architectures like Recurrent Neural Networks (RNNs) and Transformers are dominant. These networks power machine translation services and chatbots by understanding context and sequence. You can see how these models impact industries in this article on AI in healthcare, where they assist in transcribing medical notes and analyzing patient records.

Implementation Example

Modern frameworks make it easy to deploy neural networks. The following Python code demonstrates how to load a pre-trained neural network (specifically YOLO11) and run inference on an image using the ultralytics package.

from ultralytics import YOLO

# Load a pretrained YOLO11 neural network model
model = YOLO("yolo11n.pt")

# Run inference on an image to detect objects
# The model processes the image through its layers to predict bounding boxes
results = model("https://ultralytics.com/images/bus.jpg")

# Display the results
results[0].show()

Tools and Ecosystem

Building neural networks requires robust software libraries. PyTorch and TensorFlow are the two most popular open-source frameworks, offering extensive support for designing custom architectures and utilizing GPU acceleration for faster training. For those looking for a streamlined experience, the Ultralytics Platform (launching fully in 2026) provides a comprehensive environment for managing datasets, training models like YOLO11, and handling deployment. To understand the hardware that powers these networks, check out NVIDIA's guide to GPU computing.