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22 results for “training”
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.
Claude (Language Model)
A family of large language models developed by Anthropic, designed with a focus on safety, helpfulness, and Constitutional AI training methods for enterprise and consumer use.
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.
Data Labelling
Data labelling is the process of attaching meaningful tags, classes, or annotations to raw data so that supervised machine learning models can learn to predict those labels on unseen examples.
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.
Feature Store
A centralised data platform for storing, serving, and managing machine learning features so that they can be reused consistently across training and online inference.
Few-Shot Learning
Few-shot learning is a machine learning paradigm in which a model learns to perform new tasks or recognise new classes from only a small number of labelled training examples, often just one to five samples per class.
Fine-Tuning
The process of further training a pre-trained machine learning model on a smaller, task-specific dataset to adapt its weights for a particular domain, task, or desired behaviour.
Generative AI
Generative AI refers to artificial intelligence systems capable of producing new content — text, images, audio, video, or code — by learning the underlying distribution of training data.
Google Vertex AI
Google Vertex AI is a unified machine learning platform on Google Cloud that consolidates data preparation, model training, deployment, and monitoring for both custom-built models and Google's foundation models including Gemini.
Gradient Descent
Gradient descent is an iterative optimisation algorithm that minimises a loss function by repeatedly updating model parameters in the direction of the steepest descent, as defined by the negative gradient.
HRD Corp AI Training in Malaysia
HRD Corp, the Human Resource Development Corporation of Malaysia, funds employer-sponsored training in artificial intelligence, data science, and digital skills through a statutory levy collected from registered Malaysian employers.
Inference (Machine Learning)
Inference is the phase in which a trained machine learning model is used to generate predictions or outputs from new input data, distinct from the earlier training phase.
Parameter-Efficient Fine-Tuning
A family of techniques that adapts a pretrained language or vision model to a downstream task by training only a small fraction of its parameters, dramatically reducing compute, memory, and storage requirements compared to full fine-tuning.
PDPA AI Compliance
PDPA AI compliance refers to the application of Malaysia's Personal Data Protection Act 2010 to artificial intelligence systems, governing how personal data may be collected, processed, and used in AI training, inference, and deployment.
Regularisation (Machine Learning)
Regularisation is a collection of techniques in machine learning that constrain models during training to reduce overfitting and improve generalisation to unseen data.
Scale AI
An American data labelling, evaluation, and AI infrastructure company that supplies training data and evaluation services to leading AI laboratories, autonomous vehicle developers, and government agencies.
Support Vector Machine
A support vector machine (SVM) is a supervised machine learning algorithm that finds the optimal hyperplane separating data points of different classes by maximising the margin between the boundary and the nearest training examples.
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.
Transfer Learning
Transfer learning is a machine learning technique in which a model pre-trained on one task or dataset is adapted for a different but related task, enabling high performance with significantly less data and compute than training from scratch.
Zero-Shot Learning
Zero-shot learning is a machine learning paradigm in which a model makes accurate predictions on categories it has never seen during training by leveraging semantic descriptions or attribute representations.