Neural Networks

Neural Networks is one of the important topics in machine learning. Artificial neural networks are brain-inspired systems which are deliberated to reproduce the way that the human brain works.

Neural Network s consists of input and output layers, as well as (in most cases) a hidden layer consisting of units that modify the input into something that the output layer can use. They are excellent tools for finding patterns which are far too countless or complex for a human programmer to remove and teach the machine to recognize.

Neural networks allow deep learning in the computer system. As mentioned, neural networks are computer systems modelled after neural connections in the human brain. Neural networks learn by processing training examples. The best examples come in the form of large data sets. By processing the many inputs, the machine can produce a single output.

This process analyses data many times to find associations and give meaning to previously undefined data. Through different learning models, like positive reinforcement, the machine is taught it has successfully identified the object.

  • Track 1-1 Neural network application
  • Track 2-2 Mathematical Preliminaries
  • Track 3-3 Recurrent Neural Networks
  • Track 4-4 The Network Flow Problem
  • Track 5-5 Algorithms in the Theory of Numbers

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