Model pruning is a technique used in machine learning where unnecessary or less important parts of a neural network are removed. This helps reduce the size and complexity of the model without significantly affecting its accuracy. By cutting out these parts, models can run faster and require less memory, making them easier to use on…
Gradient clipping is a technique used in training machine learning models to prevent the gradients from becoming too large during backpropagation. Large gradients can cause unstable training and make the model’s learning process unreliable. By setting a maximum threshold, any gradients exceeding this value are scaled down, helping to keep the learning process steady and…
Loss landscape analysis is the study of how the values of a machine learning model’s loss function change as its parameters are adjusted. It helps researchers and engineers understand how easy or difficult it is to train a model by visualising or measuring the shape of the loss surface. A smoother or flatter loss landscape…
Quantisation-Aware Training is a method used to prepare machine learning models for running efficiently on devices with limited computing power, such as smartphones or embedded systems. It teaches the model to handle the reduced precision of numbers, which happens when large models are made smaller by using fewer bits to represent data. This approach helps…
Batch normalisation is a technique used in training deep neural networks to make learning faster and more stable. It works by adjusting and scaling the activations of each layer so they have a consistent mean and variance. This helps prevent problems where some parts of the network learn faster or slower than others, making the…
Activation functions are mathematical formulas used in neural networks to decide whether a neuron should be activated or not. They help the network learn complex patterns by introducing non-linearity, allowing it to solve more complicated problems than a simple linear system could handle. Without activation functions, neural networks would not be able to model tasks…
Residual connections are a technique used in deep neural networks where the input to a layer is added to its output. This helps the network learn more effectively, especially as it becomes deeper. By allowing information to skip layers, residual connections make it easier for the network to avoid problems like vanishing gradients, which can…
Gradient accumulation is a technique used in training neural networks where gradients from several smaller batches are summed before updating the model’s weights. This allows the effective batch size to be larger than what would normally fit in memory. It is especially useful when hardware limitations prevent the use of large batch sizes during training.
Sparse attention models are a type of artificial intelligence model designed to focus only on the most relevant parts of the data, rather than processing everything equally. Traditional attention models look at every possible part of the input, which can be slow and require a lot of memory, especially with long texts or large datasets….
Parameter-efficient fine-tuning is a machine learning technique that adapts large pre-trained models to new tasks or data by modifying only a small portion of their internal parameters. Instead of retraining the entire model, this approach updates selected components, which makes the process faster and less resource-intensive. This method is especially useful when working with very…