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…
Transformer decoders are a component of the transformer neural network architecture, designed to generate sequences one step at a time. They work by taking in previously generated data and context information to predict the next item in a sequence, such as the next word in a sentence. Transformer decoders are often used in tasks that…
Self-supervised learning is a type of machine learning where a system teaches itself by finding patterns in unlabelled data. Instead of relying on humans to label the data, the system creates its own tasks and learns from them. This approach allows computers to make use of large amounts of raw data, which are often easier…
Catastrophic forgetting is a problem in machine learning where a model trained on new data quickly loses its ability to recall or perform well on tasks it previously learned. This happens most often when a neural network is trained on one task, then retrained on a different task without access to the original data. As…
Curriculum Learning is a method in machine learning where a model is trained on easier examples first, then gradually introduced to more difficult ones. This approach is inspired by how humans often learn, starting with basic concepts before moving on to more complex ideas. The goal is to help the model learn more effectively and…