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# 204.5.13 Neural Networks Conclusion

##### Real world application and drawbacks of Neural Networks.
Link to the previous post : https://statinfer.com/204-5-12-practice-digit-recognizer/

### Real World Applications

• Self driving car by taking the video as input
• Speech recognition
• Face recognition
• Cancer cell analysis
• Heart attack predictions
• Currency predictions and stock price predictions
• Credit card default and loan predictions
• Marketing and advertising by predicting the response probability
• Weather forecasting and rainfall prediction

#### Some exaples

• Face recognition :
• Autonomous car software

### Drawbacks of Neural Networks

• No real theory that explains how to choose the number of hidden layers.
• Takes lot of time when the input data is large, needs powerful computing machines.
• Difficult to interpret the results. Very hard to interpret and measure the impact of individual predictors.
• Its not easy to choose the right training sample size and learning rate.
• The local minimum issue. The gradient descent algorithm produces the optimal weights for the local minimum, the global minimum of the error function is not guaranteed.

### Why the name neural network?

• The neural network algorithm for solving complex learning problems is inspired by human brain.
• Our brains are a huge network of processing elements. It contains a network of billions of neurons.
• In our brain, a neuron receives input from other neurons. Inputs are combined and send to next neuron.
• The artificial neural network algorithm is built on the same logic.

## Conclusion

• Neural network is a vast subject. Many data scientists solely focus on only Neural network techniques.
• In this session we practiced the introductory concepts only. Neural Networks has much more advanced techniques. There are many algorithms other than back propagation.
• Neural networks particularly work well on some particular class of problems like image recognition.
• The neural networks algorithms are very calculation intensive. They require highly efficient computing machines. Large datasets take significant amount of runtime on R. We need to try different types of options and packages.
• Currently there is a lot of exciting research going on, around neural networks.
• After gaining sufficient knowledge in this basic session, you may want to explore reinforced learning, deep learning etc.

This series is not done yet. In the final post we will cover the advance part of neural network : the math behind the Weights, Delta Rule and Gradient Descent.

The next post is a appendix on neural network.

Link to the next post : https://statinfer.com/204-5-14-neural-network-appendix/

21st June 2017

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