Introduction

Artificial neural networks, or neural nets for short, are a key component of many modern AI systems. Based loosely on the neurons in the human brain, neural nets are comprised of interconnected layers that transmit 'signals' from input data to produce desired outputs. While the human brain remains far more advanced, neural nets have powered major advances in computer vision, speech recognition, translation, and more.

In this guide, we will try to demystify the basics of neural net architecture and training. We'll try to use simple examples to explain the core mathematical concepts behind neural nets in an accessible way. By the end, you should have a basic grasp of how these AI models work their magic!

Neurons and Connections

The fundamental unit of a neural net is the artificial neuron, loosely modelled on real neurons in the brain. Each neuron receives inputs from multiple other neurons, represented by numbers. It multiplies each input by an associated weight value, sums them, and then passes the total through an activation function to determine the neuron's output signal.

By connecting neurons in layers and assigning weights to their connections, a neural net can identify statistical patterns between input data and target outputs. The connections and weights are key - neural nets learn by adjusting weights during training to minimise errors in predicting the right outputs from sample inputs.

Layers and Architecture

Neural nets are organised in connected layers of neurons. The first input layer receives data to make predictions from. An output layer produces the net's predictions. In between are hidden layers that enable processing of abstract features and patterns in the data. Deep neural nets have many hidden layers, enabling highly sophisticated analysis of complex data like images and speech.

Each neuron in one layer connects to every neuron in the next layer. Data flows from the input layer through progressively more advanced representations in the hidden layers, until the output layer pushes out predictions. This layered processing architecture gives neural nets immense flexibility and power.

Training Neural Nets

The true magic of neural nets is that they automatically learn representations of data without human intervention. They do this through an iterative training process that tweaks connection weights to minimise prediction errors.

Training requires labelled data - input examples paired with target outputs. For instance, pixel data from images labelled as 'dog' or 'cat'. The net makes predictions for each sample input, compares it to the label, calculates error, then backpropagates this error through the layers to adjust weights and reduce mistakes. Doing this repeatedly across many samples enables the net to learn which weight tweaks lower errors.

Over time, the neural net extracts meaningful patterns from the data. The trained model can then make accurate predictions for new unseen inputs - for example, recognising new photos with dogs and cats.

Key Concepts and Terms

Some key concepts and terms that are useful to understand neural nets:

• Weights and biases - Each connection between neurons has an associated weight, which amplifies or dampens the input signal. Biases offset the summed weighted input to a neuron. Tweaking weights and biases is how neural nets learn.

• Loss function - The mathematical formula used to quantify prediction errors during training. Minimising loss enables the net to adjust weights towards optimal values. Common loss functions include mean squared error for regression and cross-entropy loss for classification.

• Optimisers - Training algorithms that iteratively adjust weights to minimise loss, like stochastic gradient descent and Adam. They dictate the learning process.

• Activation functions - Mathematical operations within neurons to transform summed weighted inputs into output signals. Common activations include sigmoid, tanh, and ReLU.

Summary

Modern neural nets leverage these basic concepts at massive scale to achieve remarkable feats. While complex under the hood, their fundamental purpose remains intuitive - layered neurons trained to replicate patterns buried in data. We hope this guide provides a solid foundation to better understand these powerful AI models.