Input shape refers to the specific dimensions or structure of data that a computer model, such as a neural network, expects to receive. This includes the number of features, rows, columns, or channels in the data. Correctly matching the input shape is essential for the model to process the information accurately and avoid errors. It…
Output depth refers to the number of bits used to represent each individual value in digital output, such as in images, audio, or video. It determines how many distinct values or shades can be displayed or recorded. For example, higher output depth in an image means more subtle colour differences can be shown, resulting in…
Epoch reduction is a technique used in machine learning and artificial intelligence where the number of times a model passes through the entire training dataset, called epochs, is decreased. This approach is often used to speed up the training process or to prevent the model from overfitting, which can happen if the model learns the…
Loss decay is a technique used in machine learning where the influence of the loss function is gradually reduced during training. This helps the model make larger adjustments in the beginning and smaller, more precise tweaks as it improves. The approach can help prevent overfitting and guide the training process to a more stable final…
Weight freezing is a technique used in training neural networks where certain layers or parameters are kept unchanged during further training. This means that the values of these weights are not updated by the learning process. It is often used when reusing parts of a pre-trained model, helping to preserve learned features while allowing new…
Fine-tune sets are collections of data specifically chosen to train or adjust an existing artificial intelligence model, making it perform better on a certain task or with a particular type of input. These sets usually contain examples and correct answers, helping the AI learn more relevant patterns and responses. Fine-tuning allows a general model to…
Layer fusion is a technique used in machine learning and computer vision to combine multiple processing steps or layers into a single, more efficient operation. This process helps reduce the amount of computation and can speed up how quickly a model runs. It is especially useful when deploying models on devices with limited resources, such…
Latent injection is a technique used in artificial intelligence and machine learning where information is added or modified within the hidden, or ‘latent’, layers of a model. These layers represent internal features that the model has learned, which are not directly visible to users. By injecting new data or signals at this stage, developers can…
Sequence folding is a process that takes a long list or sequence of items and combines them into a single result by applying a specific operation step by step. The operation is usually something simple, like adding numbers together or joining words, but it can be more complex depending on the task. This method is…
Neural collapse is a phenomenon observed in deep neural networks during the final stages of training, particularly for classification tasks. It describes how the outputs or features for each class become highly clustered and the final layer weights align with these clusters. This leads to a simplified geometric structure where class features and decision boundaries…