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# 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.

## 204.7.6 Practice : Random Forest

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