Category: Model Training & Tuning

Knowledge Sparsification

Post author By EfficiencyAI
Post date 1 June 2025
Categories In Artificial Intelligence, Model Optimisation Techniques, Model Training & Tuning

Knowledge sparsification is the process of reducing the amount of information or connections in a knowledge system while keeping its most important parts. This helps make large and complex knowledge bases easier to manage and use. By removing redundant or less useful data, knowledge sparsification improves efficiency and can make machine learning models faster and…

Self-Supervised Learning

Post author By EfficiencyAI
Post date 1 June 2025
Categories In Artificial Intelligence, Deep Learning, Model Training & Tuning

Self-supervised learning is a type of machine learning where a system teaches itself by finding patterns in unlabelled data. Instead of relying on humans to label the data, the system creates its own tasks and learns from them. This approach allows computers to make use of large amounts of raw data, which are often easier…

Catastrophic Forgetting

Post author By EfficiencyAI
Post date 1 June 2025
Categories In Artificial Intelligence, Deep Learning, Model Training & Tuning

Catastrophic forgetting is a problem in machine learning where a model trained on new data quickly loses its ability to recall or perform well on tasks it previously learned. This happens most often when a neural network is trained on one task, then retrained on a different task without access to the original data. As…

Curriculum Learning

Post author By EfficiencyAI
Post date 1 June 2025
Categories In Artificial Intelligence, Deep Learning, Model Training & Tuning

Curriculum Learning is a method in machine learning where a model is trained on easier examples first, then gradually introduced to more difficult ones. This approach is inspired by how humans often learn, starting with basic concepts before moving on to more complex ideas. The goal is to help the model learn more effectively and…

Decentralized AI Training

Post author By EfficiencyAI
Post date 31 May 2025
Categories In Artificial Intelligence, Decentralised Systems, Model Training & Tuning

Decentralized AI training is a method where artificial intelligence models are trained across multiple computers or devices, rather than relying on a single central server. Each participant works with its own local data and shares only necessary model updates, not the data itself. This approach can improve privacy, reduce bottlenecks, and make use of distributed…

Tensor Processing Units (TPUs)

Post author By EfficiencyAI
Post date 31 May 2025
Categories In AI Infrastructure, Artificial Intelligence, Model Training & Tuning

Tensor Processing Units (TPUs) are specialised computer chips designed by Google to accelerate machine learning tasks. They are optimised for handling large-scale mathematical operations, especially those involved in training and running deep learning models. TPUs are used in data centres and cloud environments to speed up artificial intelligence computations, making them much faster than traditional…

Kernel Methods in ML

Post author By EfficiencyAI
Post date 31 May 2025
Categories In Artificial Intelligence, Data Science, Model Training & Tuning

Kernel methods are a set of mathematical techniques used in machine learning to find patterns in data by comparing pairs of data points. They allow algorithms to work with data that is not easily separated or structured, by transforming it into a higher-dimensional space where patterns become more visible. This makes it possible to solve…

Gaussian Process Regression

Post author By EfficiencyAI
Post date 31 May 2025
Categories In Artificial Intelligence, Data Science, Model Training & Tuning

Gaussian Process Regression is a method in machine learning used to predict outcomes based on data. It models the relationship between inputs and outputs by considering all possible functions that fit the data, and then averaging them in a way that accounts for uncertainty. This approach can provide both predictions and a measure of how…

Expectation-Maximisation Algorithm

Post author By EfficiencyAI
Post date 31 May 2025
Categories In Artificial Intelligence, Data Science, Model Training & Tuning

The Expectation-Maximisation (EM) Algorithm is a method used to find the most likely parameters for statistical models when some data is missing or hidden. It works by alternating between estimating missing data based on current guesses and then updating those guesses to better fit the observed data. This process repeats until the solution stabilises and…

Knowledge Consolidation

Post author By EfficiencyAI
Post date 31 May 2025
Categories In Artificial Intelligence, Data Science, Model Training & Tuning

Knowledge consolidation is the process by which information learned or acquired is stabilised and stored in long-term memory. This process helps new knowledge become more permanent, making it easier to recall and use later. It often involves revisiting, reviewing, or practising information over time to strengthen understanding and retention.