In all previous posts we progressed to this part. We will breakdown the steps how a neural network starts and ends.

### The Neural Network Algorithm

**Step 1: Initialization of weights:**Randomly select some weights**Step 2 : Training & Activation:**Input the training values and perform the calculations forward.**Step 3 : Error Calculation:**Calculate the error at the outputs. Use the output error to calculate error fractions at each hidden layer**Step 4: Weight training:**Update the weights to reduce the error, recalculate and repeat the process of training & updating the weights for all the examples.**Step 5: Stopping criteria:**Stop the training and weights updating process when the minimum error criteria is met

**Randomly Initialize Weights**

**Training & Activation**

**Error Calculation at Output**

**Error Calculation at hidden layers**

**Calculate weight corrections **

**Update Weights**

**Stopping Criteria**

#### Once Again ..Neural network Algorithm

- Step 1: Initialization of weights: Randomly select some weights
- Step 2 : Training & Activation: Input the training values and perform the calculations forward.
- Step 3 : Error Calculation: Calculate the error at the outputs. Use the output error to calculate error fractions at each hidden layer
- Step 4: Weight training : Update the weights to reduce the error, recalculate and repeat the process of training & updating the weights for all the examples.
- Step 5: Stopping criteria: Stop the training and weights updating process when the minimum error criteria is met.