Neural Network Demo
Demo 1: Neural Network
Demo
David Lee
Neural Network Maths
• With inputs x’s, how to we calculate output y?
• https://www.youtube.com/watch?v=6zw50rx-pYs (11mins)
• https://www.youtube.com/watch?v=UojVVG4PAG0 (27mins)
• A neural network is a series of algorithms that endeavors to recognize
underlying relationships in a set of data through a process that mimics
the way the human brain operates. Neural networks can adapt to
changing input; so the network generates the best possible result
without needing to redesign the output criteria.
Neural Network – Back Propagation
• https://www.youtube.com/watch?v=WZDMNM36PsM
• Https://www.youtube.com/watch?v=Ilg3gGewQ5U(13mins)
• https://www.youtube.com/watch?v=khUVIZ3MON8
• https://www.youtube.com/watch?v=q555kfIFUCM
• https://www.youtube.com/watch?v=WZDMNM36PsM (Let us watch
this)
Deep Neural Networks
• Pixel + Multi Layer + Error Minimization + Back Propagation
• https://www.youtube.com/watch?v=ILsA4nyG7I0
• https://www.youtube.com/watch?v=BR9h47Jtqyw
• A deep neural network is a neural network with a certain level of
complexity, a neural network with more than two layers.
• Deep neural networks use sophisticated mathematical modeling to
process data in complex ways.
Perceptron
and Neutron
• A perceptron is a neural network unit (an
artificial neuron) that does certain
computations to detect features or business
intelligence in the input data. Perceptron was
introduced by Frank Rosenblatt in 1957. He
proposed a Perceptron learning rule based on
the original MCP neuron.
• Within an artificial neural network, a neuron is a
mathematical function that model the
functioning of a biological neuron. Typically, a
neuron computes the weighted average of its
input, and this sum is passed through a
nonlinear function, often called activation
function, such as the sigmoid.