How neural networks work
Neural networks are a type of machine learning algorithm that are modelled after the structure and function of the human brain. They consist of layers of interconnected nodes called artificial neurons that transmit signals from input data and slowly adjust the connections between the neurons based on the output error. The goal is to create a model that can make predictions or classify data based on patterns it recognises in the training data it receives.
The simplest neural network is called a perceptron and contains an input layer, a hidden layer, and an output layer. The input layer receives the data, the hidden layer performs computations and applies activation functions, and the output layer provides the predicted result. The connections between nodes have numeric weights associated with them that determine how each input impacts the output. The neural network can be trained by feeding it training datasets and running iterative optimisation algorithms called learning algorithms that gradually modify the weights to reduce the error and make more accurate predictions.
The learning process works by initialising small random weights and processing the first input from the training dataset. The output is compared to the known target value and an error amount is calculated. This error is then propagated backwards to determine how much each weight contributed to the error. The error amounts are used to adjust the weights slightly in the direction that would make the output closer to the target. Many iterations of forward and backward passes are required for the model to converge on optimised weight values that enable it to accurately map inputs to outputs for new data.
There are several key components that characterise how neural networks operate:
Architecture - The overall structure of the network including number and types of layers. Simple feedforward networks have an input layer, one or more hidden layers, and an output layer. More complex networks like convolutional neural networks and recurrent neural networks have specialised architectures.
Activation function - The mathematical formula applied to the summed weighted input of each node to produce the node's output. Common activation functions include sigmoid, tanh, and ReLU. They introduce non-linearity into the network.
Loss function - Measures how far the current predictions are from the actual target. Common loss functions include mean squared error for regression and cross-entropy loss for classification. The loss is used to generate the error signal for adjusting weights.
Optimiser - The learning algorithm that modifies the weights to minimise the loss. Common optimisation methods include stochastic gradient descent, RMSprop, Adam, and Adagrad.
Regularisation - Techniques used to prevent overfitting like L1/L2 regularisation that add a penalty equal to the sum of the weights to the loss function. Dropout randomly sets a fraction of node outputs to zero during training.
Weights and biases - The adjustable parameters optimised during training. Weights determine how much influence an input has on an output. Biases allow nodes to learn patterns more easily.
Epochs - One complete pass through the training dataset during training. Networks often require hundreds or thousands of epochs to train effectively.
Batch size - The number of training samples processed before the weights are updated. Stochastic gradient descent uses small batch sizes to smooth out fluctuations in the training data.
Learning rate - Controls the size of the weight updates after each batch. A large learning rate leads to rapid initial progress but potential instability while a small rate leads to slow steady progress.
Neural networks excel at tasks like computer vision, speech recognition, and natural language processing where there are complex high-dimensional inputs like images, audio samples, and text. The layered hierarchical feature extraction of deep neural networks allows them to learn relevant patterns from raw data without relying on hand-engineered feature extraction. They outperform traditional machine learning methods at problems with opacity, nonlinearity, dimensionality, and noise.
However, they also have significant drawbacks. They require massive training datasets and extensive computational resources. Their internal workings can be difficult to interpret and debug. Small changes in the training data can result in wildly different models. They can also overfit if not properly regularised and struggle to generalise. Security and privacy issues exist due to their reliance on large amounts of data.
Overall, neural networks offer a very flexible framework for learning complex functions. Their layered processing and use of nonlinear activation functions gives them the ability to model diverse patterns and data types. With advancements in network architectures, optimisation techniques, faster hardware, and big datasets, they have achieved state-of-the-art results across many challenging machine learning tasks. However, extensive tuning and testing is required to develop an optimal network for a given problem.