Search Results
8 results for “architecture”
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.
AutoML
AutoML (Automated Machine Learning) is the process of automating the selection, composition, and tuning of machine learning algorithms and pipelines, enabling practitioners to build effective models with reduced manual effort.
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.
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.
Multi-Agent Systems
Multi-agent systems in AI are architectures in which multiple autonomous AI agents, each with specialised capabilities, collaborate through communication and coordination to complete complex tasks that exceed the capability of any single agent.
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.
Speech Recognition
Speech recognition, or automatic speech recognition (ASR), is the technology that enables computers to identify and transcribe spoken language into text using acoustic models, language models, and deep learning architectures.
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.