Statistical hypothesis testing is a method used to decide if there is enough evidence in a sample of data to support a specific claim about a population. It involves comparing observed results with what would be expected under a certain assumption, called the null hypothesis. If the results are unlikely under this assumption, the hypothesis…
Category: Model Training & Tuning
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,…
Neural Network Backpropagation
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.
Deep Belief Networks
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
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)…
AI Model Calibration
AI model calibration is the process of adjusting a model so that its confidence scores match the actual likelihood of its predictions being correct. When a model is well-calibrated, if it predicts something with 80 percent confidence, it should be right about 80 percent of the time. Calibration helps make AI systems more trustworthy and…
Neural Network Generalization
Neural network generalisation refers to the ability of a neural network to perform well on new, unseen data after being trained on a specific set of examples. It shows how well the network has learned patterns and rules, rather than simply memorising the training data. Good generalisation means the model can make accurate predictions in…
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…
Dynamic Inference Paths
Dynamic inference paths refer to the ability of a system, often an artificial intelligence or machine learning model, to choose different routes or strategies for making decisions based on the specific input it receives. Instead of always following a fixed set of steps, the system adapts its reasoning process in real time to best address…
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…