Category: Deep Learning

Dynamic Layer Optimization

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

Dynamic Layer Optimization is a technique used in machine learning and neural networks to automatically adjust the structure or parameters of layers during training. Instead of keeping the number or type of layers fixed, the system evaluates performance and makes changes to improve results. This can help models become more efficient, accurate, or faster by…

Neural Network Sparsity

Post author By EfficiencyAI
Post date 1 June 2025
Categories In Artificial Intelligence, Deep Learning, Model Optimisation Techniques

Neural network sparsity refers to making a neural network use fewer connections or weights by setting some of them to zero. This reduces the amount of computation and memory needed for the network to function. Sparsity can help neural networks run faster and be more efficient, especially on devices with limited resources.

Graph Neural Network Pruning

Post author By EfficiencyAI
Post date 1 June 2025
Categories In Deep Learning, Graph-Based Learning, Model Optimisation Techniques

Graph neural network pruning is a technique used to make graph neural networks (GNNs) smaller and faster by removing unnecessary parts of the model. These parts can include nodes, edges, or parameters that do not contribute much to the final prediction. Pruning helps reduce memory use and computation time while keeping most of the model’s…

Neural Network Backpropagation

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

Neural network backpropagation is a method used to train artificial neural networks. It works by calculating how much each part of the network contributed to an error in the output. The process then adjusts the connections in the network to reduce future errors, helping the network learn from its mistakes.

Autoencoder Architectures

Post author By EfficiencyAI
Post date 1 June 2025
Categories In Artificial Intelligence, Deep Learning, Embeddings & Representations

Autoencoder architectures are a type of artificial neural network designed to learn efficient ways of compressing and reconstructing data. They consist of two main parts: an encoder that reduces the input data to a smaller representation, and a decoder that tries to reconstruct the original input from this smaller version. These networks are trained so…

Deep Belief Networks

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

Deep Belief Networks are a type of artificial neural network that learns to recognise patterns in data by stacking multiple layers of simpler networks. Each layer learns to represent the data in a more abstract way than the previous one, helping the network to understand complex features. These networks are trained in stages, allowing them…

Recurrent Neural Network Variants

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

Recurrent Neural Network (RNN) variants are different types of RNNs designed to improve how machines handle sequential data, such as text, audio, or time series. Standard RNNs can struggle to remember information from earlier in long sequences, leading to issues with learning and accuracy. Variants like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)…

Convolutional Layer Design

Post author By EfficiencyAI
Post date 1 June 2025
Categories In Artificial Intelligence, Computer Vision, Deep Learning

A convolutional layer is a main building block in many modern neural networks, especially those that process images. It works by scanning an input, like a photo, with small filters to detect features such as edges, colours, or textures. The design of a convolutional layer involves choosing the size of these filters, how many to…

Semantic Segmentation

Post author By EfficiencyAI
Post date 1 June 2025
Categories In Artificial Intelligence, Computer Vision, Deep Learning

Semantic segmentation is a process in computer vision where each pixel in an image is classified into a specific category, such as road, car, or tree. This technique helps computers understand the contents and layout of an image at a detailed level. It is used to separate and identify different objects or regions within an…

Transferable Representations

Post author By EfficiencyAI
Post date 1 June 2025
Categories In Artificial Intelligence, Deep Learning, Embeddings & Representations

Transferable representations are ways of encoding information so that what is learned in one context can be reused in different, but related, tasks. In machine learning, this often means creating features or patterns from data that help a model perform well on new, unseen tasks without starting from scratch. This approach saves time and resources…