Data augmentation strategies are techniques used to increase the amount and variety of data available for training machine learning models. These methods involve creating new, slightly altered versions of existing data, such as flipping, rotating, cropping, or changing the colours in images. The goal is to help models learn better by exposing them to more…
Category: Artificial Intelligence
Teacher-Student Models
Teacher-Student Models are a technique in machine learning where a larger, more powerful model (the teacher) is used to train a smaller, simpler model (the student). The teacher model first learns a task using lots of data and computational resources. Then, the student model learns by imitating the teacher, allowing it to achieve similar performance…
Sparse Coding
Sparse coding is a technique used to represent data, such as images or sounds, using a small number of active components from a larger set. Instead of using every possible feature to describe something, sparse coding only uses the most important ones, making the representation more efficient. This approach helps computers process information faster and…
Knowledge-Augmented Models
Knowledge-augmented models are artificial intelligence systems that combine their own trained abilities with external sources of information, such as databases, documents or online resources. This approach helps the models provide more accurate, up-to-date and contextually relevant answers, especially when the information is too vast or changes frequently. By connecting to reliable knowledge sources, these models…
Cross-Modal Learning
Cross-modal learning is a process where information from different senses or types of data, such as images, sounds, and text, is combined to improve understanding or performance. This approach helps machines or people connect and interpret signals from various sources in a more meaningful way. By using multiple modes of data, cross-modal learning can make…
Self-Attention Mechanisms
Self-attention mechanisms are a method used in artificial intelligence to help a model focus on different parts of an input sequence when making decisions. Instead of treating each word or element as equally important, the mechanism learns which parts of the sequence are most relevant to each other. This allows for better understanding of context…
Neural Style Transfer
Neural Style Transfer is a technique in artificial intelligence that blends the artistic style of one image with the content of another. It uses deep learning to analyse and separate the elements that make up the ‘style’ and ‘content’ of images. The result is a new image that looks like the original photo painted in…
AutoML
AutoML, short for Automated Machine Learning, refers to tools and techniques that automate parts of the machine learning process. It helps users build, train, and tune machine learning models without requiring deep expertise in coding or data science. AutoML systems can handle tasks like selecting the best algorithms, optimising parameters, and evaluating model performance. This…
Ensemble Learning
Ensemble learning is a technique in machine learning where multiple models, often called learners, are combined to solve a problem and improve performance. Instead of relying on a single model, the predictions from several models are merged to get a more accurate and reliable result. This approach helps to reduce errors and increase the robustness…
Gradient Boosting Machines
Gradient Boosting Machines are a type of machine learning model that combines many simple decision trees to create a more accurate and powerful prediction system. Each tree tries to correct the mistakes made by the previous ones, gradually improving the model’s performance. This method is widely used for tasks like predicting numbers or sorting items…