Deep generative models are a type of artificial intelligence that can learn to create new data similar to the data they have been trained on. They use deep neural networks to understand patterns and structures in data such as images, text, or sound. Once trained, these models can generate new content that looks or sounds…
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…
Convolutional neural filters are small sets of weights used in convolutional neural networks to scan input data, such as images, and detect patterns like edges or textures. They move across the input in a sliding window fashion, producing feature maps that highlight specific visual features. By stacking multiple filters and layers, the network can learn…
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…
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 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 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…
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.
Graph neural network pruning is a technique used to make graph neural networks (GNNs) smaller and faster by removing unnecessary parts of the model. These parts can include nodes, edges, or parameters that do not contribute much to the final prediction. Pruning helps reduce memory use and computation time while keeping most of the model’s…
Neural network backpropagation is a method used to train artificial neural networks. It works by calculating how much each part of the network contributed to an error in the output. The process then adjusts the connections in the network to reduce future errors, helping the network learn from its mistakes.