What is a neural network?

A neural network is a machine-learning algorithm inspired by the human brain. It processes complex data by passing information through layers of interconnected nodes (neurons). Each neuron adjusts its connections during training to improve accuracy, making neural networks highly effective for tasks like image recognition, language processing, and pattern detection.

Types of neural networks

Neural networks come in different types, each designed for specific tasks. Their architecture and training methods determine how they process data, making them suitable for applications like image recognition, language processing, and content generation. Here are some common types:

Feedforward Neural Networks (FNNs)

Process data in a single, one-way direction from input to output without looping back. These networks are ideal for simple classification and regression tasks such as predicting housing prices or detecting spam emails.

Recurrent Neural Networks (RNNs)

Handle sequential data by retaining memory of past inputs, making them useful for tasks like time-series analysis, speech recognition, and natural language processing. Variants like LSTM (Long Short-Term Memory) networks address the issue of retaining information over longer sequences.

Convolutional Neural Networks (CNNs)

Specialised in processing visual data, recognising spatial hierarchies and patterns like edges, textures, and objects in images. Widely used in applications such as facial recognition, medical imaging, and autonomous driving.

Generative Adversarial Networks (GANs)

Consists of two networks (a generator and a discriminator) competing to produce realistic images, videos, and audio. Commonly used for deepfake technology, image enhancement, and artistic content generation.

Key components and concepts of neural networks

Neural networks are built upon several key components that work together to process data and make predictions:

Neurons

A neural network's basic computational units (also called nodes). Each neuron receives inputs, processes them using a mathematical function, and transmits the output to the next layer of neurons.

Layers

Groups of neurons organised into three main types:

• Input Layer: Receives raw data and passes it to the next layer.
• Hidden Layers: Intermediate layers where data transformation occurs through weighted connections and activation functions. Neural networks can have multiple hidden layers in deep learning models.
• Output Layer: Produces the final result, such as a prediction or classification.

Weights and biases

Parameters that control the strength and influence of connections between neurons. During training, these values are adjusted to minimise errors and improve prediction accuracy. Weights determine the importance of an input, while biases allow the activation function to shift its output.

Activation functions

Mathematical operations applied to the output of neurons to introduce non-linearity, allowing the network to learn complex patterns. Common activation functions include:

• ReLU (Rectified Linear Unit): Activates neurons only if the input is positive, widely used in deep learning due to its simplicity and effectiveness.
• Sigmoid: Squashes input values into a range between 0 and 1, useful for binary classification tasks.
• Tanh: Similar to sigmoid but scales the output between -1 and 1, often used in RNNs for handling sequential data.

What are neural networks used for?

Neural networks power a wide range of real-world applications by mimicking how the human brain processes information. From image recognition and language processing to financial modelling and medical diagnostics, these AI-driven systems analyse complex data patterns to improve decision-making and automation. Key areas where neural networks are making an impact include:

• Image recognition: Used in self-driving cars (for pedestrian and object detection), social media (for facial recognition), and medical diagnostics (for identifying tumors in X-rays and MRIs).
• Natural Language Processing (NLP): Powers tools like Google Translate, virtual assistants (e.g., Alexa, Siri), and chatbots for machine translation, text summarisation, sentiment analysis, and speech recognition.
• Financial modelling: Helps analyse historical data for investment decisions, risk management, fraud detection, credit scoring, and portfolio optimisation.
• Medical diagnosis: Assists in disease detection, predicting patient outcomes, and identifying early warning signs by analysing patient records and diagnostic imagery.
• Robotics: Enables robots to perform complex tasks like assembly, precision welding, surgical procedures, and autonomous navigation in industrial and healthcare settings.
• Self-driving vehicles: Assists in recognising pedestrians, traffic lights, and other vehicles to ensure safe driving, lane-keeping, and collision avoidance.
• Fraud detection: Identifies fraudulent transactions by detecting unusual patterns in financial data, especially in real-time payment systems.
• Spam filtering: Detects spam emails by analysing content patterns and metadata, improving the accuracy of email categorisation and phishing detection.

Neural networks vs. deep learning

Neural networks are AI models with a few layers that process structured data and handle simpler tasks like basic classification and prediction. They rely on manual feature extraction, meaning human intervention is needed to define important data patterns. While effective for smaller datasets, they struggle with complex, unstructured data like images and text.

Deep learning builds on neural networks by adding multiple layers, allowing it to process vast amounts of data and uncover intricate patterns. It excels at handling unstructured data, making it essential for applications like self-driving cars, language models, and medical imaging. Unlike traditional neural networks, deep learning models learn patterns automatically, reducing the need for manual data processing and making them more powerful for solving complex problems.