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Introduction

Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains.[1]

The original goal of the ANN approach was to solve problems in the same way that a human brain would.

However, over time, attention moved to performing specific tasks, leading to deviations from biology. ANNs have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games, medical diagnosis and even in activities that have traditionally been considered as reserved to humans, like painting.[2]

Artificial neural networks (ANN)  are computing systems which learn to

perform tasks by considering examples, generally without being

programmed with task-specific rules.

Example: ANN might learn to identify images that contain cats by

analyzing example images that have been manually labeled as

"cat" or "no cat" and using the results to identify cats in other images.

They automatically generate identifying characteristics from the examples

that they process.

Timeline

1975

1943

Timeline

ButI in 1975, Marvin Minsky and Seymour Papert found multiple problems with the Perceptron model, which were later solved by Paul Werbos ,using Back Propagation

In 1943 ,Warren McCulloch and Walter Pitts opened the subject by writing a paper on how neurons might work. In order to describe how neurons in the brain might work, they modeled a simple neural network using electrical circuits.

1958

 in 1958, Frank Rosenblatt created the “Perceptron” model, which was the first of its kind to perform pattern recognition,

Timeline

1992

Timeline

In 1992, max-pooling was introduced to help with least shift invariance and tolerance to deformation to aid in 3D object recognition

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How ANN Does work?

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An ANN is based on a collection of connected nodes called

artificial neurons, which loosely model the neurons  in a biological

brain. The nodes are organized into multiple layers (input, hidden,

and output).

The connections (links) are used to transmit a signal (piece of

information) from a node to others. Each link has a weight, which

determines the strength of one node's influence on another.

The artificial neuron processes the received signal through a

nonlinear transformation called activation function and passes it

to other neurons.

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