Artificial Neural Network – Part 1


Deep Learning (Artificial Neural Network): Neural networks are set of algorithms, inspired from the functioning of human brain. In a very layman language, we can say neural network has a series of algorithms that attempts to identify the hidden trends within data by using a process that mimics the functioning of neural networks within human brain. Neural networks have the ability to evolve as per new information available so the network produces the best possible result without the need to redesign the output criteria.

Breaking Down “Neural Network”: In Finance Neural network can be used in pricing, portfolio management, credit risk, Forecasting and even can be used in trading.

Working of Neural Network: An Artificial Neural Network mimics the human brain. Neural Networks are typically organized in layers and layers are interconnected through nodes called “Activation Function”. Each Node is called perceptron which resembles a multiple linear regression. The perceptron feeds the signal generated by a multiple linear regression into an activation function that may be nonlinear.



Below is the list of concepts we would go through in the following articles to get better understanding of Neural Network.

  1. The Neurons
  2. The Activation Function
  3. How does Neural Network work?
  4. How Neural Network self-evolve?
  5. Gradient Descent
  6. Stochastic Gradient Descent
  7. Back-Propagation(Generalized Delta Rule)

Conclusion: The computing world has a lot to gain from neural networks. Their ability to learn by example makes them very flexible and powerful. Furthermore there is no need to devise an algorithm in order to perform a specific task; i.e. there is no need to understand the internal mechanisms of that task. They are also very well suited for real time systems because of their fast response and computational times which are due to their parallel architecture.

Next Article: Artificial Neural Network Part -2


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