Neural Networks Interview Questions

There is considerable overlap between the fields of neural networks and statistics. Statistics is concerned with data analysis. In neural network terminology, statistical inference means learning to generalize from noisy data. Some neural networks are not concerned with data analysis (e.g., those intended to model biological systems) and therefore have little to do with statistics. Some neural networks do not learn (e.g., Hopfield nets) and therefore have little to do with statistics.

Some neural networks can learn successfully only from noise-free data (e.g., ART or the perceptron rule) and therefore would not be considered statistical methods. But most neural networks that can learn to generalize effectively from noisy data are similar or identical to statistical methods. For example:

Some neural networks can learn successfully only from noise-free data (e.g., ART or the perceptron rule) and therefore would not be considered statistical methods. But most neural networks that can learn to generalize effectively from noisy data are similar or identical to statistical methods. For example:

Neural networks require too much data to train. A classification network may require thousands of examples in a single class for it to identify it in unseen data. Due to this, sometimes it is not feasible to create ANN models for fringe applications.

Neural networks are not interpretable. The user needs to input data into the network and it outputs the required output, but the work that goes into processing the input and giving an output is not understandable to human beings.

The power required to train the neural network is extremely high compared to the amount of power that a human brain uses (around 20 Watts) to do almost the same things such as image classification.

Source : Towardsdatascience

Biological Neurons | Artificial Neurons |
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Major components: Axions, Dendrites, Synapse | Major Components: Nodes, Inputs, Outputs, Weights, Bias |

Information from other neurons, in the form of electrical impulses, enters the dendrites at connection points called synapses. The information flows from the dendrites to the cell where it is processed. The output signal, a train of impulses, is then sent down the axon to the synapse of other neurons. | The arrangements and connections of the neurons made up the network and have three layers. The first layer is called the input layer and is the only layer exposed to external signals. The input layer transmits signals to the neurons in the next layer, which is called a hidden layer. The hidden layer extracts relevant features or patterns from the received signals. Those features or patterns that are considered important are then directed to the output layer, which is the final layer of the network. |

A synapse is able to increase or decrease the strength of the connection. This is where information is stored. | The artificial signals can be changed by weights in a manner similar to the physical changes that occur in the synapses. |

Approx 10^{11} neurons. |
10^{2}– 10^{4} neurons with current technology |

Human Brain(Biological Neuron Network) | Computers(Artificial Neuron Network) |
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The human brain works asynchronously | Computers(ANN) work synchronously. |

Biological Neurons compute slowly (several ms per computation) | Artificial Neurons compute fast (<1 nanosecond per computation) |

The brain represents information in a distributed way because neurons are unreliable and could die any time. | In computer programs every bit has to function as intended otherwise these programs would crash. |

Our brain changes their connectivity over time to represents new information and requirements imposed on us. | The connectivity between the electronic components in a computer never change unless we replace its components. |

Biological neural networks have complicated topologies. | ANNs are often in a tree structure. |

Researchers are still to find out how the brain actually learns. | ANNs use Gradient Descent for learning. |

A** Multi Layer Perceptron (MLP)** contains one or more hidden layers (apart from one input and one output layer). While a single layer perceptron can only learn linear functions, a multi layer perceptron can also learn non – linear functions.

Figure 4 shows a multi layer perceptron with a single hidden layer. Note that all connections have weights associated with them, but only three weights **(w0, w1, w2)** are shown in the figure.

Given a set of **features X = (x1, x2, …)** and a target y, a Multi Layer Perceptron can learn the relationship between the features and the target, for either classification or regression.

Lets take an example to understand Multi Layer Perceptrons better. Suppose we have the following student-marks dataset:

The two input columns show the number of hours the student has studied and the mid term marks obtained by the student. The Final Result column can have two values **1 or 0** indicating whether the student passed in the final term. For example, we can see that if the student studied 35 hours and had obtained** 67 marks** in the mid term, he / she ended up passing the final term.

Now, suppose, we want to predict whether a student studying **25 hours** and having **70 marks** in the mid term will pass the final term.

This is a binary classification problem where a multi layer perceptron can learn from the given examples (training data) and make an informed prediction given a new data point. We will see below how a multi layer perceptron learns such relationships..

Source : ujjwalkarn

Source : ujjwalkarn