Home / Python / Predictive Modeling & Machine Learning / 204.5.6 Neural Network Intuition

204.5.6 Neural Network Intuition

Before going further into neural network algorithm, we need to understand and break down how the algorithm is working.

Neural Network Intuition

Final Output

y=out(h)=g(Wjhj)hj=out(x)=g(w(jk)xk)

y=out(h)=g(Wjg(w(jk)xk))

  • So h is a non linear function of linear combination of inputs – A multiple logistic regression line
  • Y is a non linear function of linear combination of outputs of logistic regressions
  • Y is a non linear function of linear combination of non linear functions of linear combination of inputs
  • We find W to minimize ni=1[yig(Wjhj)]2
  • We find Wj and wjk to minimize ni=1[yig(Wjg(w(jk)xk))]2
  • Neural networks is all about finding the sets of weights Wj and wjk using Gradient Descent Method

The Neural Networks

  • The neural networks methodology is similar to the intermediate output method explained above.
  • But we will not manually subset the data to crate the different models.
  • The neural network technique automatically takes care of all the intermediate outputs using hidden layers
  • It works very well for the data with non-linear decision boundaries
  • The intermediate output layer in the network is known as hidden layer
  • In Simple terms, neural networks are multi layer nonlinear regression model.
  • If we have sufficient number of hidden layers, then we can estimate any complex non-linear function

Neural Network and Vocabulary

Why are they called hidden layers?

  • A hidden layer “hides” the desired output.
  • Instead of predicting the actual output using a single model, build multiple models to predict intermediate output
  • There is no standard way of deciding the number of hidden layers.

Algorithm for Finding Weights

  • Algorithm is all about finding the weights/coefficients
  • We randomly initialize some weights; Calculate the output by supplying training input; If there is an error the weights are adjusted to reduce this error.

About admin

Check Also

204.7.6 Practice : Random Forest

Let’s implement the concept of Random Forest into practice using Python. Practice : Random Forest …

Leave a Reply

Your email address will not be published. Required fields are marked *