While early artificial neural networks drew heavily from biological nervous systems, modern deep learning is a broadly interdisciplinary field. Just as aeronautics innovation eventually moved beyond the direct study of birds, contemporary deep learning draws inspiration in equal or greater measure from mathematics, linguistics, psychology, statistics, and computer science, rather than relying exclusively on neuroscience.

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Artificial Neural Networks are built to resemble neurons in the human brain. The idea behind neural networks is to understand the pattern behind terms through examples rather than through task-specific rules.

Artificial Neural Networks

Dive into Deep Learning

Deep learning is a particular kind of machine learning that achieves great power and flexibility by representing the world as a nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representationss computed in terms of less abstract ones.


Deep Learning (in Machine learning)

Neural networks are made up of nodes (each representing a Logistic regression) with layers. They are trained to map an input vector X to an output vector Y based on the training data. The hidden layers of a network contain neurons, connections, and activation functions that occur between the input and output. An activation function is applied at each layer to correctly connect the input and output. Different types of networks may use diverse types of activation functions to correctly identify the nature of how the input and output are related.
In the figure out of the 4 neurons, the Logistic regression representation of two of them are shown.

Artificial Neural Networks Formulation

The goal of a neural network model is to approximate some function $f^*$. For example, for a classifier, $y = f^*(x)$ maps an input $x$ to a category $y$. A neural network model defines a mapping $y = f (x; \theta)$ and learns the values for the parameter $\theta$ that result in the best function approximation.

Artificial Neural Networks Objective

The McCulloch-Pitts model is an early, foundational computational model of artificial neurons developed by cyberneticists and neurophysiologists Warren McCulloch and Walter Pitts. This model predates modern deep learning but provided a natural starting point by abstracting the structure of biological neural systems into interconnected mathematical units.

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