What is a neural network?
These systems form the backbone of deep learning and have been responsible for breakthroughs in areas like image recognition, language processing, and game playing AI.
7 min read
7 min read
Neural networks are computer programs designed to recognise patterns and make predictions. They are inspired by the way the human brain processes information. Just as the brain is made up of interconnected cells called neurons, neural networks are made up of layers of “artificial neurons” that work together to learn complex patterns.
In a very simplified way, you can think of a neural network as a complex web of yes-or-no switches (called neurons) that connect and pass along signals to one another, creating a pathway that leads to an answer or prediction. This interconnected structure enables the network to learn and improve over time, even with minimal human guidance.
Imagine a neural network as a three-layered structure. With every new example it processes, the network adjusts itself a little bit to get closer to the right answer, just like a person refining their knowledge through practice. After seeing thousands or even millions of examples, it eventually learns the patterns well enough to make accurate predictions on new, unseen data. Imagine you’re training a neural network to recognise handwritten digits (0 through 9) from images. Each image is essentially a grid of pixels, with each pixel representing either a dark or light point on the screen.
A neural network would be nothing without activation functions, which help it decide when to pass information to the next layer. Think of activation functions like decision-making gates, they allow the network to learn and handle more complex patterns. A common activation function is called ReLU (Rectified Linear Unit), which turns any negative value into zero. This simple change helps the network focus on patterns that are more relevant while ignoring unimportant noise. Other popular functions include sigmoid (useful for binary classification tasks) and softmax (for multi-class classification).
When we talk about deep learning, we’re referring to neural networks with multiple hidden layers. These deeper networks are incredibly powerful because they allow the model to learn complex features and relationships in data, especially with tasks like image recognition, natural language processing, and speech recognition. For example, in image recognition, each hidden layer might focus on detecting specific features. The first layer might pick up on edges and basic shapes. The next layer might identify more complex shapes, like eyes or noses in a face. The final layers might recognise combinations of features that make up the entire face. This stacking of layers enables the network to tackle very intricate tasks by breaking them down into simpler parts and combining them layer by layer.
Neural networks are responsible for many AI applications we use every day;
Neural networks are key to AI’s ability to handle complex, real-world tasks. By layering artificial neuron and adjusting weights, these networks can learn intricate patterns in data, powering everything from self-driving cars to virtual assistants. The next time you see a face-recognition feature or talk to a virtual assistant, you’re witnessing the results of a neural network in action. With each image, phrase, or data point they process, these networks become smarter, helping AI push closer to human-like levels of perception and understanding.
Despite their power, neural networks have some limitations. They need vast amounts of data to learn accurately. If you have a small dataset, they may struggle. Training requires a lot of computational power, which can be costly in energy and time-consuming. They also operate as “black boxes,” meaning it’s hard to understand exactly how they’re making decisions. This lack of transparency can be a problem in applications where explainability is crucial.
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