Machine Learning

"Learning machines to imitate human intelligence"

Machine Learning (ML) Traditional programming uses algorithms to produce results from data:

Machine learning creates algorithms from data and results:

Neural Networks (NN)

Neural Networks are based on how the human brain works: Neurons are sending messages to each other. While the neurons are trying to solve a problem (over and over again), it is strengthening the connections that lead to success and diminishing the connections that lead to failure.

Perceptron defines the first step into Neural Networks. It represents a single neuron with only one input layer, and no hidden layers. Learn how to program a perceptron.

In its simplest form, a neural network is made up from: An input layer (yellow) A hidden layer (blue) An output layer (red)

Neural Network Model, input data (yellow) are processed against a hidden layer (blue) before producing the final output (red).

The yellow perceptrons are making simple decisions based on the input. Each single decision is sent to the perceptrons in the next layer.

The blue perceptrons are making decisions by weighing the results from the first layer. This layer make more complex decisions at a more abstract level than the first layer.

Deep Neural Networks are made up of several hidden layers of neural networks that perform complex operations on massive amounts of data. Each successive layer uses the preceding layer as input. For instance, optical reading uses low layers to identify edges, and higher layers to identify letters.

Deep Neural Network Model, input data (yellow) are processed against a hidden layer (blue) and modified against more hidden layers (green) to produce the final output (red).