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Building Artificial Neural Network using R - Machine Learning Tutorial - Great Learning

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Collective intelligence Collective action Self-organized criticality Herd mentality Phase transition Agent-based modelling Synchronization Ant colony optimization Particle swarm optimization Swarm behaviour. Evolutionary computation Genetic algorithms Genetic programming Artificial life Machine learning Evolutionary developmental biology Artificial intelligence Evolutionary robotics. Reaction—diffusion systems Partial differential equations Dissipative structures Percolation Cellular automata Spatial ecology Self-replication. Rational choice theory Bounded rationality. Artificial neural networks ANNs , usually simply called neural networks NNs , are computing systems vaguely inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons , which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron that receives a signal then processes it and can signal neurons connected to it. The "signal" at a connection is a real number , and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called edges. Business Artificial Neural Network and Data

Artificial Intelligence or AI is a field in computer science that focuses on solving problems by applying learning techniques and some math. In some ways, AI and the field, in general, focuses on building programs that try and imitate the way your own brain works.

Artificial Neural Network Essay

There are so many ways we as humans or ev e n other animals learn. When Buster was a pup I wanted to teach Artificiao to roll over, but I had two main read more. The first is that Buster doesn't know English.

The second is that he doesn't exactly know what it means to roll over. But I have two keys ways to teach buster to roll over. The second is that Buster loves chicken and little doggie treats. By using these techniques I can teach a dog, who knows nothing about the English language, to effectively understand certain English words.

If understanding AI ever scared you, don’t worry it’s not so bad!

In AI, researchers and computer scientists can do here same sort of thing using machine learning techniques like reinforcement learning.

This learning technique works exactly like teaching Buster to roll over. I can tell it the rules and the way the pieces move but it won't get the point of the game. We give the computer rewards when it takes a piece and we punish the computer by making it lose rewards when it loses a piece.

Over time, the computer will learn to maximize rewards. The consequence of maximizing these rewards will be that the computer will learn to play chess extremely well. But what's another way that we learn?

Well, there are actually lots of ways to learn but one that you probably know Netaork is repetition. What if you wanted to learn to shoot a basketball. The best way is to just practice shooting at the hoop and over time your body will learn to make small adjustments in how hard you throw the ball, how you push the ball of your fingertips, and many other micro-adjustments that all add up to shooting really shot that over time become more consistent. Our brains are amazing at learning in this way and in AI we try and emulate this behavior, something even babies can do seamlessly.

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But computers are just really good calculators. They can do simple math really fast. But how they learn as we do? Abstracting the learning process so that even a dumb machine can learn. I want you to fill in the last word in the following sentence:.

Bruce Ironhardt

Did you think keys — or maybe pants? There are millions of words to choose from but your brain was smart enough to narrow down the possibilities of which words I was going to say pretty substantially.

So how did your brain do it?]