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14 results for “neural networks”
AI Video Generation
AI video generation refers to the automated creation of video content from text prompts, images, or other inputs using generative neural networks, enabling synthetic video production without cameras or traditional animation.
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.
Continual Learning
Continual learning is a machine learning paradigm in which models incrementally acquire knowledge from sequential tasks or data streams without forgetting previously learned information, addressing the stability-plasticity trade-off inherent in neural networks.
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.
Cross-Entropy Loss
Cross-entropy loss is the standard objective function for training classification models, measuring the divergence between a predicted probability distribution and the true distribution of labels.
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.
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.
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.
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.
Neural Scaling Laws
Neural scaling laws are empirical relationships describing how the performance of neural networks improves predictably as a function of model size, dataset size, and compute budget, enabling principled resource allocation for AI training.
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.
Softmax Function
The softmax function converts a vector of real-valued scores into a probability distribution, and is widely used as the output layer of neural network classifiers and in attention mechanisms.