Model versioning systems are tools and methods used to keep track of different versions of machine learning models as they are developed and improved. They help teams manage changes, compare performance, and ensure that everyone is working with the correct model version. These systems store information about each model version, such as training data, code,…
Time series forecasting is a way to predict future values by looking at patterns and trends in data that is collected over time. This type of analysis is useful when data points are recorded in a sequence, such as daily temperatures or monthly sales figures. By analysing past behaviour, time series forecasting helps estimate what…
Statistical hypothesis testing is a method used to decide if there is enough evidence in a sample of data to support a specific claim about a population. It involves comparing observed results with what would be expected under a certain assumption, called the null hypothesis. If the results are unlikely under this assumption, the hypothesis…
Feature selection algorithms are techniques used in data analysis to pick out the most important pieces of information from a large set of data. These algorithms help identify which inputs, or features, are most useful for making accurate predictions or decisions. By removing unnecessary or less important features, these methods can make models faster, simpler,…
Neural network backpropagation is a method used to train artificial neural networks. It works by calculating how much each part of the network contributed to an error in the output. The process then adjusts the connections in the network to reduce future errors, helping the network learn from its mistakes.
Deep Belief Networks are a type of artificial neural network that learns to recognise patterns in data by stacking multiple layers of simpler networks. Each layer learns to represent the data in a more abstract way than the previous one, helping the network to understand complex features. These networks are trained in stages, allowing them…
Recurrent Neural Network (RNN) variants are different types of RNNs designed to improve how machines handle sequential data, such as text, audio, or time series. Standard RNNs can struggle to remember information from earlier in long sequences, leading to issues with learning and accuracy. Variants like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)…
AI model calibration is the process of adjusting a model so that its confidence scores match the actual likelihood of its predictions being correct. When a model is well-calibrated, if it predicts something with 80 percent confidence, it should be right about 80 percent of the time. Calibration helps make AI systems more trustworthy and…
Neural network generalisation refers to the ability of a neural network to perform well on new, unseen data after being trained on a specific set of examples. It shows how well the network has learned patterns and rules, rather than simply memorising the training data. Good generalisation means the model can make accurate predictions in…
Neural network compression refers to techniques used to make large artificial neural networks smaller and more efficient without significantly reducing their performance. This process helps reduce the memory, storage, and computing power required to run these models. By compressing neural networks, it becomes possible to use them on devices with limited resources, such as smartphones…