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…
Category: Model Optimisation Techniques
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…
Efficient Model Inference
Efficient model inference refers to the process of running machine learning models in a way that minimises resource use, such as time, memory, or computing power, while still producing accurate results. This is important for making predictions quickly, especially on devices with limited resources like smartphones or embedded systems. Techniques for efficient inference can include…
Neural Network Sparsity
Neural network sparsity refers to making a neural network use fewer connections or weights by setting some of them to zero. This reduces the amount of computation and memory needed for the network to function. Sparsity can help neural networks run faster and be more efficient, especially on devices with limited resources.
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…
Hash Function Optimization
Hash function optimisation is the process of improving how hash functions work to make them faster and more reliable. A hash function takes input data and transforms it into a fixed-size string of numbers or letters, known as a hash value. Optimising a hash function can help reduce the chances of two different inputs creating…