Dimensionality reduction techniques are methods used to simplify large sets of data by reducing the number of variables or features while keeping the essential information. This helps make data easier to understand, visualise, and process, especially when dealing with complex or high-dimensional datasets. By removing less important features, these techniques can improve the performance and…
Category: Model Optimisation Techniques
Feature Selection Algorithms
Feature selection algorithms are techniques used in data analysis to pick out the most important pieces of information from a large set of data. These algorithms help identify which inputs, or features, are most useful for making accurate predictions or decisions. By removing unnecessary or less important features, these methods can make models faster, simpler,…
Low-Rank Factorization
Low-Rank Factorisation is a mathematical technique used to simplify complex data sets or matrices by breaking them into smaller, more manageable parts. It expresses a large matrix as the product of two or more smaller matrices with lower rank, meaning they have fewer independent rows or columns. This method is often used to reduce the…
Sparse Activation Maps
Sparse activation maps are patterns in neural networks where only a small number of neurons or units are active at any given time. This means that for a given input, most of the activations are zero or close to zero, and only a few are significantly active. Sparse activation helps make models more efficient by…
Neural Network Compression
Neural network compression refers to techniques used to make large artificial neural networks smaller and more efficient without significantly reducing their performance. This process helps reduce the memory, storage, and computing power required to run these models. By compressing neural networks, it becomes possible to use them on devices with limited resources, such as smartphones…
Efficient Attention Mechanisms
Efficient attention mechanisms are methods used in artificial intelligence to make the attention process faster and use less computer memory. Traditional attention methods can become slow or require too much memory when handling long sequences of data, such as long texts or audio. Efficient attention techniques solve this by simplifying calculations or using clever tricks,…
Weight Sharing Techniques
Weight sharing techniques are methods used in machine learning models where the same set of parameters, or weights, is reused across different parts of the model. This approach reduces the total number of parameters, making models smaller and more efficient. Weight sharing is especially common in convolutional neural networks and models designed for tasks like…
Model Distillation Frameworks
Model distillation frameworks are tools or libraries that help make large, complex machine learning models smaller and more efficient by transferring their knowledge to simpler models. This process keeps much of the original model’s accuracy while reducing the size and computational needs. These frameworks automate and simplify the steps needed to train, evaluate, and deploy…
Inference Latency Reduction
Inference latency reduction refers to techniques and strategies used to decrease the time it takes for a computer model, such as artificial intelligence or machine learning systems, to produce results after receiving input. This is important because lower latency means faster responses, which is especially valuable in applications where real-time or near-instant feedback is needed….
Neural Network Quantization
Neural network quantisation is a technique that reduces the amount of memory and computing power needed by a neural network. It works by representing the numbers used in the network, such as weights and activations, with lower-precision values instead of the usual 32-bit floating-point numbers. This makes the neural network smaller and faster, while often…