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Artificial Intelligence | What is AI | Introduction to Artificial Intelligence | Edureka

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Overview of Neural Networks

If you’ve heard about Artificial Intelligence, Machine Learning, or Deep Learning recently, then you might have heard of a Neural Network.

The development of neural networks have been key to teaching computers to think and understand the world in the way that humans do.

This is abstracted as a graph of nodes (neurons) connected by weighted edges (synapses).

The human brain consists of 100 billion cells called neurons, connected together by synapses.

This process of forward propagation and backward propagation is conducted iteratively on every piece of data in a training data set.

The greater the size of the data set and the greater the variety of data set that there is, the more that the neural network will learn, and the better that the neural network will get at predicting outputs.

Simply put, a neural network is a connected graph with input neurons, output neurons, and weighted edges.

The inputs and outputs of a neural network are represented by input neurons and output neurons.

A neural network consists of connections, each connection transferring the output of a neuron to the input of another neuron.

Overview of Artificial Intelligence Buzz

If you’re in tech, you’ve been hearing a lot of buzz around Artificial Intelligence, Machine Learning, and even Deep Learning.

This particular field of Artificial Intelligence and Machine Learning is the one that has been solving a ton of interesting problems in recent years — from automated grocery store purchases to autonomous cars.

Back in 1956, researchers came together at Dartmouth with the explicit goal of programming computers to behave like humans.

To further explain the goals of Artificial Intelligence, researchers extended their primary goal to these six main goals.

These are just six of the major algorithms and techniques within Artificial Intelligence: 1) Machine Learning is the field of artificial intelligence that gives computers the ability to learn without being explicitly programmed.

An example of logical reasoning in artificial intelligence is an expert computer system that emulates the decision-making ability of a human expert.

5) Probabilistic Reasoning is to combine the capacity of probability theory to handle uncertainty with the capacity of deductive logic to exploit structure of formal argument.

This usually involves a system of differential equations that usually describe a physical system like a robot or an aircraft.