Category: Deep Learning

Autoencoder Architectures

Autoencoder architectures are a type of artificial neural network designed to learn efficient ways of compressing and reconstructing data. They consist of two main parts: an encoder that reduces the input data to a smaller representation, and a decoder that tries to reconstruct the original input from this smaller version. These networks are trained so…

Recurrent Neural Network Variants

Recurrent Neural Network (RNN) variants are different types of RNNs designed to improve how machines handle sequential data, such as text, audio, or time series. Standard RNNs can struggle to remember information from earlier in long sequences, leading to issues with learning and accuracy. Variants like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)…

Transferable Representations

Transferable representations are ways of encoding information so that what is learned in one context can be reused in different, but related, tasks. In machine learning, this often means creating features or patterns from data that help a model perform well on new, unseen tasks without starting from scratch. This approach saves time and resources…

Neural Network Generalization

Neural network generalisation refers to the ability of a neural network to perform well on new, unseen data after being trained on a specific set of examples. It shows how well the network has learned patterns and rules, rather than simply memorising the training data. Good generalisation means the model can make accurate predictions in…