It is a subfield of artificial intelligence which is modeled after the brain. It is a computational network consisting of neurons interconnected to each other. This interconnected structure is used for making various predictions for both regressions as well as classification problems. The ANN consists of various layers - the input layer, the hidden layer, and the output layers. The hidden layers could be more than 1 in number. The hidden layer is the place where all the mathematics of the neural network takes place. The basic formulas of weights and biases are added here, along with the application of the activation functions. These activation functions are responsible for delivering the output in a structured and trimmed manner. It is majorly used for solving non-linear problems - handwriting recognition, traveling salesman problems, etc. ANNs involves complex mathematical calculations and are highly compute-intensive in nature.
Imagine you are walking on a walkway and you see a pillar (assume that you have never seen a pillar before). You walk into the pillar and hit it. Now, the next time you see a pillar you stay a few meters away from the pillar and continue walking on the side. This time your shoulder hits the pillar and you are hurt again. Again when you see the pillar you ensure that you don’t hit it but this time on your path you hit a letter-box (assuming that you have never seen a letter-box before). You walk into it and the complete process repeats again. This is how an artificial neural network works, it is given several examples and it tries to get the same answer. Whenever it is wrong, an error is calculated. So, next time for a similar example the value at the synapse (weighted values through which neurons are connected in the network) and neuron is propagated backward i.e. back propagation takes place. Thus, an ANN requires lots of examples and learning and they can be in millions or billions for real-world applications.
Recommended Reading: Types of Neural Networks
Why use ANNs?
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ANN’s have interconnection of non-linear neurons thus these machine learning algorithms can exploit non-linearity in a distributed manner.
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They can adapt free parameters to the changes in the surrounding environment.
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Learns from its mistakes and takes better decisions through backpropagation.
Advantages of Using ANNs
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Easy to understand for professionals who do not want to dig deep into math-related complex machine learning algorithms. If you are trying to sell a model to an organization which would you rather say Artificial Neural Networks (ANN) or Support Vector Machine (SVM). We guess the answer obviously is going to be ANN because you can easily explain to them that they just work like the neurons in your brain.
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They are easy to conceptualize.
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They have the ability to identify all probable interactions between predictor variables.
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They have the ability to subtly identify complex nonlinear relationships that exists between independent and dependent variables.
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It is relatively easy to add prior knowledge to the model.
Disadvantages of Using ANNs
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It is very difficult to reverse engineer ANN algorithms. If your ANN learns that the image of a dog is actually a cat then it is very difficult to determine “why”. All than can be done is continuously tweak or train the ANN further.
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ANN algorithms are not probabilistic meaning if the output of the algorithm is a continuous number it is difficult to translate it into a probability.
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They are not magic wands and cannot be applied to solve any kind of ML algorithm.
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ANNs in native implementation are not highly effective at practical problem-solving. However, this can be improved with the use of deep learning techniques.
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Multi-layered ANN algorithms are hard to train and require tuning a lot of parameters.