Search Results
10 results for “optimisation”
AI in Malaysian Manufacturing
Artificial intelligence adoption in Malaysian manufacturing covers predictive maintenance, computer vision quality control, demand forecasting, and supply-chain optimisation across the electronics, automotive, and food sectors.
Batch Normalisation
Batch normalisation is a deep learning technique that normalises the activations of each layer within a mini-batch to accelerate training and improve model stability.
Gaussian Process
A non-parametric Bayesian model that defines a distribution over functions, widely used in regression, optimisation, and uncertainty quantification.
Gradient Boosting
A machine learning ensemble technique that builds predictive models sequentially, where each new model corrects the errors of its predecessors using gradient descent optimisation.
Gradient Descent
Gradient descent is an iterative optimisation algorithm that minimises a loss function by repeatedly updating model parameters in the direction of the steepest descent, as defined by the negative gradient.
Hyperparameter Tuning
The process of selecting optimal configuration values for a machine learning model's external parameters using methods such as grid search, random search, and Bayesian optimisation.
Model Pruning
A model compression technique that removes redundant or low-importance parameters from a neural network to reduce size, memory footprint, and inference latency while preserving accuracy.
Neural Architecture Search
Neural architecture search is the automated design of neural network architectures using search algorithms, reinforcement learning, or gradient-based methods to discover models that meet target accuracy, latency, and size constraints.
OpenVINO
OpenVINO is an open-source toolkit developed by Intel for optimising and deploying deep learning inference across Intel hardware, including CPUs, GPUs, Neural Processing Units, and FPGAs, with broad support for major AI frameworks and model formats.
Quantisation
Quantisation is a model compression technique that reduces the numerical precision of a neural network's weights and activations from high-bit floating-point formats to lower-bit representations, decreasing memory usage and accelerating inference with minimal accuracy loss.