Positional encoding is a technique used in machine learning models, especially transformers, to give information about the order of data, like words in a sentence. Since transformers process all words at once, they need a way to know which word comes first, second, and so on. Positional encoding adds special values to each input so…
Capsule Networks are a type of artificial neural network designed to better capture spatial relationships and hierarchies in data, such as images. Unlike traditional neural networks, capsules group neurons together to represent different properties of an object, like its position and orientation. This structure helps the network understand the whole object and its parts, making…
Residual connections are a technique used in deep neural networks where the input to a layer is added to its output. This helps the network learn more effectively, especially as it becomes deeper. By allowing information to skip layers, residual connections make it easier for the network to avoid problems like vanishing gradients, which can…
A Mixture of Experts is a machine learning model that combines several specialised smaller models, called experts, to solve complex problems. Each expert focuses on a specific part of the problem, and a gating system decides which experts to use for each input. This approach helps the overall system make better decisions by using the…
The Neural Tangent Kernel (NTK) is a mathematical tool used to study and predict how very large neural networks learn. It simplifies the behaviour of neural networks by treating them like a type of kernel method, which is a well-understood class of machine learning models. Using the NTK, researchers can analyse training dynamics and generalisation…
LoRA Fine-Tuning is a method used to adjust large pre-trained artificial intelligence models, such as language models, with less computing power and memory. Instead of changing all the model’s weights, LoRA adds small, trainable layers that adapt the model for new tasks. This approach makes it faster and cheaper to customise models for specific needs…
Gradient accumulation is a technique used in training neural networks where gradients from several smaller batches are summed before updating the model’s weights. This allows the effective batch size to be larger than what would normally fit in memory. It is especially useful when hardware limitations prevent the use of large batch sizes during training.
Sparse attention models are a type of artificial intelligence model designed to focus only on the most relevant parts of the data, rather than processing everything equally. Traditional attention models look at every possible part of the input, which can be slow and require a lot of memory, especially with long texts or large datasets….
Parameter-efficient fine-tuning is a machine learning technique that adapts large pre-trained models to new tasks or data by modifying only a small portion of their internal parameters. Instead of retraining the entire model, this approach updates selected components, which makes the process faster and less resource-intensive. This method is especially useful when working with very…
Attention mechanisms are methods used in artificial intelligence that help models focus on the most relevant parts of input data, such as words in a sentence or regions in an image. They allow the model to weigh different pieces of information differently, depending on their importance for the task. This makes it easier for the…