In short, an attention-based model "focuses" on each element of the input (a word in a sentence or a different position in an image, etc.). "Focusing" means projecting different levels of attention so that the input elements are treated differently and each element of the input is weighted differently to influence the result; a non-attention model treats each element "equally".

pseudocode.js is a JavaScript library that typesets pseudocode beautifully to HTML. When I was trying to render the pseudocode in my tech blog, I struggled a lot. I tried loads of methods, but none of them worked. Finally, this one worked out, so I'd like to document it for future reference.

Convolution is a mathematical operation on two functions (f and g) that creates a third function (f * g) that expresses how the shape of one is modified by the other in mathematics (specifically, functional analysis). If one of the functions participating in the fold is considered to be the indicator function of the interval, the fold can also be considered to be a 'sliding average' promotion. The idea of applying the convolutional operation to image data is not novel or specific to convolutional neural networks. A convolution is simply the application of a filter to an input that results in an activation. In computer vision, it's a common technique.

A contract for differences (CFD) is a financial contract that pays the difference between the open and closing trade settlement prices. CFDs are popular in FX and commodities goods since they allow investors to trade the direction of securities in the very short term.

The standardisation of inputs may be applied to input variables for the first hidden layer or to the activations from a hidden layer for deeper layers. In common, this normalisation technique is used on the inputs to the layer before or after the activation function in the previous layer. Using normalisation technique, in addition, can make the network more stable during training. In this articale, batch normalisation and layer normalisation will be compared.

The possibility of two events occurring at the same time is known as joint probability. To better know the concept, we should take a closer look at marginal distribution and conditional distribution into detail.