Machine learning involves adaptive mechanisms that enable computers to learn from experience, learn by example, and learn by analogy. Learning capabilities can improve over time. The most popular approaches to machine learning are artificial neural networks and genetic algorithms.
A neural network can be defined as a model of reasoning based on the human brain. The brain consists of a densely interconnected set of nerve cells, or basic information-processing units, called neurons.
The human brain incorporates nearly 10 billion neurons and 60 trillion connections, synapses, between them. Faster than the fastest computers in existence.
Neuron
Each neuron has a very simple structure, but an army of such elements consists of tremendous processing power.
→ It consists of a cell body, soma
→ Number of fibers called dendrites
→ Single long fiber called axon.
In neural networks, both data and its processing are global rather than local.
Q7 2018 Discss the typical neural network architecture. Exaplain how it is different from conventional computing
An artificial neural networks have same structure and working mechanisms as of human neuron.
From Past Questions 2022 Q3 Describe the four differnt kinds of activation functions commonly used in the neural network as a transfer functions.