one of neural network
map source:https://github.com/microsoft/ai-edu
- Fundamental Principle
- inputs: characteristic value that presents attribute of sample.
- weights: weighted value for each input single. BUT not necessarily their value to 1.
- bias: deviation measuring learning algorithm for degree of deviation from the expectations of the forecast and actual results, that is, ability to portray fitting of the learning algorithm itself.
- activative function: simulation of characteristics of biological neurons, after receving inpput by a threshold value simulation of neuron activation and excitement and generate output, to introduce nonlinear neural networks, enhance the ability of neural networks.
- calculation process: result = (∑wixi + b) * A
- Regression or Fitting
- Back Propagation;
- Gradient Descent;
- Loss Function;
- FP:
- Initialization
- Forward pass
- Loss function
- Back propagation
- Gradient descent
- Goto 2, loop calculation, To fitting curve.
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