Exploration-Exploitation Strategies are approaches used to balance trying new options with using known, rewarding ones. The aim is to find the best possible outcome by sometimes exploring unfamiliar choices and sometimes sticking with what already works. These strategies are often used in decision-making systems, such as recommendation engines or reinforcement learning, to improve long-term results.
Category: Model Optimisation Techniques
Cloud Workload Optimization
Cloud workload optimisation is the process of making sure that applications and tasks running in a cloud environment use resources efficiently. This includes managing how much computing power, storage, and network capacity each workload needs, so that costs are kept low and performance stays high. By monitoring and adjusting resources as needed, organisations avoid waste…
Statistical Model Validation
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…
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…
Deep Residual Learning
Deep Residual Learning is a technique used to train very deep neural networks by allowing the model to learn the difference between the input and the output, rather than the full transformation. This is done by adding shortcut connections that skip one or more layers, making it easier for the network to learn and avoid…
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…
Model Compression Pipelines
Model compression pipelines are a series of steps used to make machine learning models smaller and faster without losing much accuracy. These steps can include removing unnecessary parts of the model, reducing the precision of calculations, or combining similar parts. The goal is to make models easier to use on devices with limited memory or…
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…