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This application has significant implications in speech recognition systems and optical character recognition (OCR). Music Generation: LSTM networks can generate musical sequences by learning patterns from existing musical data. This application enables the creation of new melodies, harmonies, and compositions.
The term "Artificial neural network" refers to a biologically inspired sub-field of artificial intelligence modeled after the brain. An Artificial neural network is usually a computational network based on biological neural networks that construct the structure of the human brain. Similar to a human brain has neurons interconnected to each ...
Backpropagation can be written as a function of the neural network. Backpropagation algorithms are a set of methods used to efficiently train artificial neural networks following a gradient descent approach which exploits the chain rule. The main features of Backpropagation are the iterative, recursive and efficient method through which it ...
Convolutional Neural Network. Convolutional Neural Networks are a special type of feed-forward artificial neural network in which the connectivity pattern between its neuron is inspired by the visual cortex. The visual cortex encompasses a small region of cells that are region sensitive to visual fields. In case some certain orientation edges ...
ResNet: Residual Network. ResNet (short for Residual Network) is a type of neural network architecture introduced in 2015 by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun from Microsoft Research. It was designed to solve the problem of vanishing gradients in deep neural networks, which hindered their performance on large-scale image ...
ReLU Function is the most commonly used activation function in the deep neural network. To gain a solid understanding of the feed-forward process, let's see this mathematically. 1) The first input is fed to the network, which is represented as matrix x1, x2, and one where one is the bias value. 2) Each input is multiplied by weight with respect ...
Principal Component Analysis is an unsupervised learning algorithm that is used for the dimensionality reduction in machine learning. It is a statistical process that converts the observations of correlated features into a set of linearly uncorrelated features with the help of orthogonal transformation. These new transformed features are called ...
