Hyperparameter tweaks refer to the process of adjusting the settings that control how a machine learning model learns from data. These settings, called hyperparameters, are not learned by the model itself but are set by the person training the model. Changing these values can significantly affect how well the model performs on a given task.
Weight freezing is a technique used in training neural networks where certain layers or parameters are kept unchanged during further training. This means that the values of these weights are not updated by the learning process. It is often used when reusing parts of a pre-trained model, helping to preserve learned features while allowing new…
Overfitting checks are methods used to ensure that a machine learning model is not just memorising the training data but can also make accurate predictions on new, unseen data. Overfitting happens when a model learns too much detail or noise from the training set, which reduces its ability to generalise. By performing checks, developers can…
A stability index is a measure used to assess how stable or consistent something is over time. It is often used in fields like engineering, finance, and data analysis to identify changes or shifts in a system, data set, or process. By calculating the stability index, organisations can monitor trends, detect problems early, and make…
Performance metrics are measurements used to assess how well a system, process, or individual is working. They help track progress, identify strengths and weaknesses, and guide improvements. Good metrics are clear, relevant, and easy to understand so that everyone involved can use them to make better decisions.
Prompt efficiency refers to how effectively and concisely a prompt communicates instructions to an AI system to get accurate and relevant results. It involves using clear language, avoiding unnecessary details, and structuring requests so the AI can understand and respond correctly. Efficient prompts save time and resources by reducing the need for repeated clarifications or…
A token window refers to the amount of text, measured in tokens, that an AI model can process at one time. Tokens are pieces of words or characters that the model uses to understand and generate language. The size of the token window limits how much information the model can consider for a single response…
Memory tracing is the process of monitoring and recording how a computer program uses memory over time. It helps developers track which parts of their code allocate, use, and free memory. This information is valuable for finding memory leaks, optimising performance, and ensuring efficient resource management.
Weighted sampling is a method for selecting items from a group where some items are given a higher chance of being chosen than others. Each item is assigned a weight, which indicates its importance or likelihood of selection. This approach is often used when some options are more relevant or common than others, so the…
Output buffering is a technique used by computer programs to temporarily store data in memory before sending it to its final destination, such as a screen or a file. This allows the program to collect and organise output efficiently, reducing the number of times it needs to access slow resources. Output buffering can improve performance…