Neural networks

A brain-inspired system

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.

Input layer.

This is the layer where the data enters the network. Each piece of information (such as a pixel in an image or a word in a sentence) is fed into its own input “neuron.” In our example each pixel’s colour value is fed into its own neuron in the input layer.

Hidden layers

Between the input and output, there are one or more hidden layers. These layers do the heavy lifting of recognising patterns by transforming data and finding relationships. In our example the hidden layers analyse the patterns within the image, like the curve of a “3” or the loops of an “8.” Over time, these hidden neurons learn which combinations of pixels likely represent each digit.

Output layer.

Finally, the output layer provides the result or prediction, whether it’s identifying a cat in a photo, predicting the price of a house, or generating a sentence. In our example the output layer produces a prediction of which digit the network “thinks” it sees, like “3” or “8.” Through weight adjustments, the network gradually improves at recognising these digits. Eventually, it can identify even messy handwriting with impressive accuracy.

Turning neurons on and off

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).
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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.

Real life examples

Neural networks are responsible for many AI applications we use every day;

Image recognition

Neural networks are behind facial recognition software, object detection in photos, and even medical image analysis that detects tumors.

Natural language processing

Neural networks help models like GPT and other language processors understand and generate human language. They are used in chatbots, translation services, and text summarisation.

Voice recognition

Neural networks enable systems like Siri, Alexa, and Google Assistant to understand spoken language and respond accurately.

Game AI

From playing chess to conquering complex video games, neural networks help AI systems analyse game states and develop strategies.

Pushing the boundaries of AI

Neural networks represent a monumental step in 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.