Data science model retraining pipelines are automated processes that regularly update machine learning models with new data to maintain or improve their accuracy. These pipelines help ensure that models do not become outdated or biased as real-world data changes over time. They typically include steps such as data collection, cleaning, model training, validation and deployment,…
Category: Model Training & Tuning
Data Science Experiment Tracking
Data science experiment tracking is the process of recording and organising information about the experiments performed during data analysis and model development. This includes storing details such as code versions, data inputs, parameters, and results, so that experiments can be compared, reproduced, and improved over time. Effective experiment tracking helps teams collaborate, avoid mistakes, and…
Automated Feature Extraction
Automated feature extraction is the process where computer algorithms identify and select useful information or patterns from raw data without requiring manual intervention. This helps prepare the data for machine learning models by highlighting the most relevant characteristics, making it easier for the models to find relationships and make predictions. It saves time and reduces…
AI for Quantum Chemistry
AI for Quantum Chemistry refers to the use of artificial intelligence techniques to help solve problems in quantum chemistry, such as predicting molecular properties or simulating chemical reactions. Traditional quantum chemistry calculations can be very slow and require significant computing power. AI models can speed up these calculations by learning patterns from existing data and…
AI for Genomic Analysis
AI for genomic analysis refers to the use of artificial intelligence techniques to examine and interpret genetic information. By analysing DNA sequences, AI can help identify patterns, mutations, and relationships that might be difficult for humans to spot quickly. This technology speeds up research and supports more accurate findings in genetics and medicine.
Policy Regularisation Techniques
Policy regularisation techniques are methods used in machine learning and artificial intelligence to prevent an agent from developing extreme or unstable behaviours while it learns how to make decisions. These techniques add constraints or penalties to the learning process, encouraging the agent to prefer simpler, safer, or more consistent actions. The goal is to help…
RL with Human Feedback
Reinforcement Learning with Human Feedback (RLHF) is a method where artificial intelligence systems learn by receiving guidance from people instead of relying only on automatic rewards. This approach helps AI models understand what humans consider to be good or useful behaviour. By using feedback from real users or experts, the AI can improve its responses…
Adaptive Exploration Strategies
Adaptive exploration strategies are methods used by algorithms or systems to decide how to search or try new options based on what has already been learned. Instead of following a fixed pattern, these strategies adjust their behaviour depending on previous results, aiming to find better solutions more efficiently. This approach helps in situations where blindly…
Cross-Layer Parameter Sharing
Cross-layer parameter sharing is a technique in neural network design where the same set of parameters, such as weights, are reused across multiple layers of the model. Instead of each layer having its own unique parameters, some or all layers share these values, which helps reduce the total number of parameters in the network. This…
Neural Network Ensemble Pruning
Neural network ensemble pruning is a technique used to make collections of neural networks more efficient. When many models are combined to improve prediction accuracy, the group can become slow and resource-intensive. Pruning involves removing some networks from the ensemble, keeping only those that contribute most to performance. This helps keep the benefits of using…