Statistical model validation is the process of checking whether a statistical model accurately represents the data it is intended to explain or predict. It involves assessing how well the model performs on new, unseen data, not just the data used to build it. Validation helps ensure that the model’s results are trustworthy and not just…
Category: Model Training & Tuning
Data Preprocessing Pipelines
Data preprocessing pipelines are step-by-step procedures used to clean and prepare raw data before it is analysed or used by machine learning models. These pipelines automate tasks such as removing errors, filling in missing values, transforming formats, and scaling data. By organising these steps into a pipeline, data scientists ensure consistency and efficiency, making it…
Data Sampling Strategies
Data sampling strategies are methods used to select a smaller group of data from a larger dataset. This smaller group, or sample, is chosen so that it represents the characteristics of the whole dataset as closely as possible. Proper sampling helps reduce the amount of data to process while still allowing accurate analysis and conclusions.
Neural Network Regularization
Neural network regularisation refers to a group of techniques used to prevent a neural network from overfitting to its training data. Overfitting happens when a model learns the training data too well, including its noise and outliers, which can cause it to perform poorly on new, unseen data. Regularisation methods help the model generalise better…
Recurrent Layer Optimization
Recurrent layer optimisation refers to improving the performance and efficiency of recurrent layers in neural networks, such as those found in Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs). This often involves adjusting the structure, parameters, or training methods to make these layers work faster, use less memory, or…
Transfer Learning Optimization
Transfer learning optimisation refers to the process of improving how a machine learning model adapts knowledge gained from one task or dataset to perform better on a new, related task. This involves fine-tuning the model’s parameters and selecting which parts of the pre-trained model to update for the new task. The goal is to reduce…
Neural Architecture Pruning
Neural architecture pruning is a method used to make artificial neural networks smaller and faster by removing unnecessary parts, such as weights or entire connections, without significantly affecting their performance. This process helps reduce the size of the model, making it more efficient for devices with limited computing power. Pruning is often applied after a…
Dynamic Layer Optimization
Dynamic Layer Optimization is a technique used in machine learning and neural networks to automatically adjust the structure or parameters of layers during training. Instead of keeping the number or type of layers fixed, the system evaluates performance and makes changes to improve results. This can help models become more efficient, accurate, or faster by…
Model Quantization Trade-offs
Model quantisation is a technique that reduces the size and computational requirements of machine learning models by using fewer bits to represent numbers. This can make models run faster and use less memory, especially on devices with limited resources. However, it may also lead to a small drop in accuracy, so there is a balance…
Continuous Model Training
Continuous model training is a process in which a machine learning model is regularly updated with new data to improve its performance over time. Instead of training a model once and leaving it unchanged, the model is retrained as fresh information becomes available. This helps the model stay relevant and accurate, especially when the data…