Search Results
21 results for “neural network”
Artificial Intelligence
Artificial intelligence (AI) is the simulation of human intelligence processes by computer systems, encompassing learning, reasoning, problem-solving, perception, and language understanding.
Attention Mechanism
A neural network technique that enables models to dynamically weight the relevance of different parts of an input sequence when producing each output element, forming the core of transformer architectures.
Autoencoder
An autoencoder is a type of artificial neural network trained to reconstruct its input through a compressed internal representation, used for dimensionality reduction, feature learning, and anomaly detection.
Backpropagation
Backpropagation is the primary algorithm for training neural networks, computing gradients of a loss function with respect to each weight by applying the chain rule of calculus in reverse through the network layers.
Convolutional Neural Network
A convolutional neural network (CNN) is a type of deep neural network that uses convolutional layers to automatically learn spatial hierarchies of features from grid-structured data, most commonly images.
Deep Learning
Deep learning is a subfield of machine learning that uses multi-layered artificial neural networks to learn hierarchical representations from data, enabling state-of-the-art performance across vision, language, and speech tasks.
Encoder-Decoder Architecture
A neural network design pattern that compresses an input sequence into an internal representation using an encoder, and then generates an output sequence from that representation using a decoder, foundational to machine translation, summarisation, and many other sequence-to-sequence tasks.
Generative Adversarial Network
A generative adversarial network (GAN) is a class of machine learning framework in which two neural networks, a generator and a discriminator, compete against each other to produce synthetic data indistinguishable from real examples.
Knowledge Distillation
Knowledge distillation is a model compression technique in which a smaller student neural network is trained to replicate the behaviour of a larger, more capable teacher model, enabling deployment of efficient models that approximate teacher-level performance.
Long Short-Term Memory (LSTM)
Long Short-Term Memory is a recurrent neural network architecture designed to learn long-range dependencies in sequential data by using gating mechanisms to control information flow.
Machine Learning
Machine learning is a subfield of artificial intelligence in which systems improve their performance on tasks through experience — by automatically learning patterns from data rather than following explicitly programmed rules.
Model Pruning
A model compression technique that removes redundant or low-importance parameters from a neural network to reduce size, memory footprint, and inference latency while preserving accuracy.
Neural Architecture Search
Neural architecture search is the automated design of neural network architectures using search algorithms, reinforcement learning, or gradient-based methods to discover models that meet target accuracy, latency, and size constraints.
Neural Network
A neural network is a computational model inspired by biological brains, composed of interconnected layers of nodes that learn patterns from data through weighted connections.
ONNX (Open Neural Network Exchange)
An open standard format for representing machine learning models that enables interoperability between deep learning frameworks, runtimes, and hardware platforms.
Quantisation
Quantisation is a model compression technique that reduces the numerical precision of a neural network's weights and activations from high-bit floating-point formats to lower-bit representations, decreasing memory usage and accelerating inference with minimal accuracy loss.
Recurrent Neural Network
A recurrent neural network (RNN) is a class of neural network designed for sequential data, where connections between nodes form directed cycles allowing information to persist across time steps.
Tensor Processing Unit
A tensor processing unit (TPU) is a custom application-specific integrated circuit developed by Google for accelerating machine learning workloads, particularly neural network training and inference.
Text-to-Speech
Text-to-speech is the technology that converts written text into synthesised spoken audio using rule-based, concatenative, or neural network methods.
Transformer Architecture
A neural network architecture introduced in 2017 that uses self-attention mechanisms to process sequential data in parallel, forming the foundation of modern large language models and multimodal AI systems.
Variational Autoencoder
A variational autoencoder is a generative neural network that learns a probabilistic latent representation of data, enabling smooth sampling and reconstruction of new examples.