Category: Model Training & Tuning

Adaptive Dropout Methods

Adaptive dropout methods are techniques used in training neural networks to prevent overfitting by randomly turning off some neurons during each training cycle. Unlike standard dropout, adaptive dropout adjusts the dropout rate based on the importance or activity of each neuron, allowing the model to learn which parts of the network are most valuable for…

Neural Network Regularisation Techniques

Neural network regularisation techniques are methods used to prevent a model from becoming too closely fitted to its training data. When a neural network learns too many details from the examples it sees, it may not perform well on new, unseen data. Regularisation helps the model generalise better by discouraging it from relying too heavily…

Neural Network Quantisation Techniques

Neural network quantisation techniques are methods used to reduce the size and complexity of neural networks by representing their weights and activations with fewer bits. This makes the models use less memory and run faster on hardware with limited resources. Quantisation is especially valuable for deploying models on mobile devices, embedded systems, or any place…

Hybrid CNN-RNN Architectures

Hybrid CNN-RNN architectures combine two types of neural networks: convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNNs are good at recognising patterns and features in data like images, while RNNs are designed to handle sequences, such as text or audio. By joining them, these architectures can process both spatial and temporal information, making…

Model Lifecycle Management

Model lifecycle management refers to the process of overseeing a machine learning or artificial intelligence model from its initial design through to deployment, monitoring, maintenance, and eventual retirement. It covers all the steps needed to ensure the model continues to work as intended, including updates and retraining when new data becomes available. This approach helps…

Distributed Model Training Architectures

Distributed model training architectures are systems that split the process of teaching a machine learning model across multiple computers or devices. This approach helps handle large datasets and complex models by sharing the workload. It allows training to happen faster and more efficiently, especially for tasks that would take too long or use too much…

Automated Model Selection Frameworks

Automated model selection frameworks are software tools or systems that help choose the best machine learning model for a specific dataset or problem. They do this by testing different algorithms, tuning their settings, and comparing their performance automatically. This saves time and effort, especially for people who may not have deep expertise in machine learning.

Knowledge Transfer in Multi-Domain Learning

Knowledge transfer in multi-domain learning refers to using information or skills learned in one area to help learning or performance in another area. This approach allows a system, like a machine learning model, to apply what it has learned in one domain to new, different domains. It helps reduce the need for large amounts of…

Multi-Objective Optimisation in ML

Multi-objective optimisation in machine learning refers to solving problems that require balancing two or more goals at the same time. For example, a model may need to be both accurate and fast, or it may need to minimise cost while maximising quality. Instead of focusing on just one target, this approach finds solutions that offer…