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203.5.13 Neural Networks Conclusion

Real world application and drawbacks of Neural Networks.

Real World Applications

In the previous section, we studied about Digit Recognizer

  • 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 examples

Drawbacks of Neural Networks

  • No real theory that explains how to choose the number of hidden layers.
  • Takes a 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.
  • It’s 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 the 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 sent to the next neuron.
  • The artificial neural network algorithm is built on the same logic.


  • Neural network is a vast subject. Many data scientists solely focus only on 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 backpropagation.
  • Neural networks particularly work well on some particular class of problems like image recognition.
  • The neural network algorithms are very calculation intensive. They require highly efficient computing machines. Large datasets take a 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.

The next post is about Neural Network Appendix.

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