Adaptive feature selection algorithms are computer methods that automatically choose the most important pieces of data, or features, from a larger set to help a machine learning model make better decisions. These algorithms adjust their selection process as they learn more about the data, making them flexible and efficient. By focusing only on the most…
Category: Model Optimisation Techniques
Distributed Model Training Architectures
Distributed model training architectures are systems that split the process of teaching a machine learning model across multiple computers or devices. This approach helps handle large datasets and complex models by sharing the workload. It allows training to happen faster and more efficiently, especially for tasks that would take too long or use too much…
Adaptive Model Compression
Adaptive model compression is a set of techniques that make machine learning models smaller and faster by reducing their size and complexity based on the needs of each situation. Unlike fixed compression, adaptive methods adjust the amount of compression dynamically, often depending on the device, data, or available resources. This helps keep models efficient without…
Multi-Objective Optimisation in ML
Multi-objective optimisation in machine learning refers to solving problems that require balancing two or more goals at the same time. For example, a model may need to be both accurate and fast, or it may need to minimise cost while maximising quality. Instead of focusing on just one target, this approach finds solutions that offer…
Automated Hyperparameter Tuning Algorithms
Automated hyperparameter tuning algorithms are computer programs that help choose the best settings for machine learning models without human intervention. These algorithms test different combinations of parameters, such as learning rate or tree depth, to find the ones that make the model perform best. By automating this process, they save time and often find better…
Neural Network Sparsity Techniques
Neural network sparsity techniques are methods used to reduce the number of active connections or weights in a neural network. By removing or disabling unnecessary elements, these techniques make models smaller and more efficient without losing much accuracy. This helps save memory and speeds up computation, which is important for running models on devices with…
Active Sampling for Data Efficiency
Active sampling for data efficiency is a method used in machine learning and data science to select the most informative data points for training models. Instead of using all available data, the system chooses which examples to label or process, focusing on those that help improve the model most. This approach saves time and resources…
Early Stopping Criteria in ML
Early stopping criteria in machine learning are rules that determine when to stop training a model before it has finished all its training cycles. This is done to prevent the model from learning patterns that only exist in the training data, which can make it perform worse on new, unseen data. By monitoring the model’s…
Dynamic Loss Function Scheduling
Dynamic Loss Function Scheduling refers to the process of changing or adjusting the loss function used during the training of a machine learning model as training progresses. Instead of keeping the same loss function throughout, the system may switch between different losses or modify their weights to guide the model to better results. This approach…
Neural Network Weight Initialisation Techniques
Neural network weight initialisation techniques are methods used to set the starting values for the weights in a neural network before training begins. These starting values can greatly affect how well and how quickly a network learns. Good initialisation helps prevent problems like vanishing or exploding gradients, which can slow down or stop learning altogether.