Search Results
32 results for “deep learning”
Activation Function
A mathematical function applied to a neuron's output in a neural network that introduces non-linearity, enabling models to learn complex patterns beyond simple linear relationships.
AI Drug Discovery
AI drug discovery applies machine learning, deep learning, and generative modelling to accelerate the identification, design, and optimisation of therapeutic compounds across the pharmaceutical pipeline.
Artificial Intelligence
Artificial intelligence (AI) is the simulation of human intelligence processes by computer systems, encompassing learning, reasoning, problem-solving, perception, and language understanding.
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.
Batch Normalisation
Batch normalisation is a deep learning technique that normalises the activations of each layer within a mini-batch to accelerate training and improve model stability.
Computer Vision
Computer vision is the field of artificial intelligence that enables machines to interpret and act upon visual information from the world — including images, video, and depth data.
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.
CUDA
NVIDIA's parallel computing platform and programming model that lets developers use GPUs for general-purpose computation, underpinning most modern deep learning frameworks.
Data Augmentation
A set of techniques that expand a training dataset by creating modified copies of existing examples, helping deep learning models generalise better and reducing overfitting.
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.
DeepSpeed
DeepSpeed is an open-source deep learning optimisation library developed by Microsoft that enables efficient distributed training and inference of large-scale neural networks through memory and compute optimisations.
Dropout
A regularisation technique in deep learning that randomly deactivates neurons during training, preventing co-adaptation and improving generalisation. Introduced by Hinton and colleagues in 2012 and formalised in 2014.
ElevenLabs
ElevenLabs is an AI audio research and deployment company founded in 2022 that develops text-to-speech, voice cloning, dubbing, and conversational voice agent technologies based on proprietary deep learning models.
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.
GPU Cluster
A GPU cluster is a networked group of servers, each containing one or more graphics processing units, purpose-built to accelerate parallel computation workloads such as deep learning training and large-scale AI inference.
Graph Neural Network
A class of deep learning models designed to operate on graph-structured data, enabling nodes to aggregate and propagate information across their neighbourhoods through a message-passing mechanism.
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.
Layer Normalisation
Layer normalisation is a technique that normalises the inputs across the features of a single training example, stabilising and accelerating the training of deep neural networks, especially transformers.
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.
Neuro-symbolic AI
Neuro-symbolic AI is a hybrid artificial intelligence paradigm that combines neural network-based learning with symbolic reasoning, integrating the pattern recognition strengths of deep learning with the structured reasoning and interpretability of symbolic methods.
Object Detection
Object detection is a computer vision task that involves identifying the location and category of one or more objects within an image or video frame, producing bounding boxes and class labels for each detected instance.
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.
OpenVINO
OpenVINO is an open-source toolkit developed by Intel for optimising and deploying deep learning inference across Intel hardware, including CPUs, GPUs, Neural Processing Units, and FPGAs, with broad support for major AI frameworks and model formats.
Optical Character Recognition
A computer vision technology that converts images of typed, handwritten, or printed text into machine-readable digital text, increasingly powered by deep learning and transformer-based vision models.
PyTorch
PyTorch is an open-source machine learning framework, originally developed by Meta AI, that provides tensor computation with GPU acceleration and a dynamic computational graph for building and training deep neural networks.
Residual Network
A deep convolutional neural network architecture introduced by Microsoft Research in 2015 that uses skip connections to enable training of very deep networks, winning the ImageNet challenge with a top-5 error rate of 3.57%.
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.
TensorFlow
TensorFlow is an open-source machine learning platform developed by Google that supports the full lifecycle of building, training, and deploying models across servers, mobile devices, browsers, and edge hardware.
TensorFlow Lite
TensorFlow Lite is an open-source deep learning framework from Google for running optimised machine learning models on mobile phones, microcontrollers, and other edge devices.
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.
Vision Transformer
The Vision Transformer (ViT) is a deep learning model that applies the transformer architecture originally designed for NLP directly to sequences of image patches, achieving state-of-the-art results on visual recognition tasks.