Fine-Tuning

Deep Learning

A subset of machine learning using neural networks with multiple layers to learn hierarchical representations of data.

Neural Network

A computing system inspired by biological neural networks, consisting of interconnected nodes that process information in layers.

Convolutional Neural Network

A deep learning architecture designed for processing structured grid data like images, using convolutional filters to detect features.

Recurrent Neural Network

A neural network architecture where connections between nodes form directed cycles, enabling processing of sequential data.

Long Short-Term Memory

A recurrent neural network architecture designed to learn long-term dependencies by using gating mechanisms to control information flow.

Gated Recurrent Unit

A simplified variant of LSTM that combines the forget and input gates into a single update gate.

Transformer

A neural network architecture based entirely on attention mechanisms, eliminating recurrence and enabling parallel processing of sequences.

Attention Mechanism

A neural network component that learns to focus on relevant parts of the input when producing each element of the output.

Encoder-Decoder Architecture

A neural network design where an encoder processes input into a fixed representation and a decoder generates output from it.

Autoencoder

A neural network trained to encode input data into a compressed representation and then decode it back to reconstruct the original.

Variational Autoencoder

A generative model that learns a probabilistic latent space representation, enabling generation of new data samples.

Batch Normalisation

A technique that normalises layer inputs during training to stabilise and accelerate deep neural network learning.

A self-supervised learning approach that trains models by comparing similar and dissimilar pairs of data representations.

A learned dense vector representation of discrete data (like words or categories) in a continuous vector space.

An activation function that maps input values to a range between 0 and 1, useful for binary classification outputs.

A regularisation technique that penalises large model weights during training by adding a fraction of the weight magnitude to the loss function, preventing overfitting.

Training neural networks using both 16-bit and 32-bit floating-point arithmetic to speed up computation while maintaining accuracy.

A problem in deep networks where gradients become extremely small during backpropagation, preventing earlier layers from learning.

Training a model on a large general dataset before fine-tuning it on a specific downstream task.

A neural network shortcut that allows the output of one layer to bypass intermediate layers and be added to a later layer's output.