8 Unique Machine Learning Interview Questions on Backpropagation

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Introduction

Not interested in background? Skip to the questions here.

The days of Machine Learning taking over the world are well within their stride, when, at every step, an opportunity brings itself forth for everyone, everywhere. A world where AI is blooming and being powered by ML at its core means that once you decide you join in the hunt to master this craft, companies will come after you to get you!

However, things are not as simple as they may seem, and concepts, albeit at times basic, lead to your selection. Henceforth, let us discuss one such concept in ‘Back Propagation,’ the root of many ML problems.

Article Overview

• What is backpropagation?
• How does backpropagation work?
• Why do we need backpropagation?
• Backpropagation ML Interview Questions
• Wrap Up

What is Backpropagation in the First Place?

So, backpropagation is an algorithm in Deep Learning, which itself is a subset of Machine Learning. It does the fabulous and most important job of reducing the error by minimizing the cost function. To minimize this cost function, we need the optimum values of weights that we give to each feature, and we also need the derivates of different nodes in a multilayered neural network – backpropagation helps us do exactly that!

Backpropagation utilizes the chain rule to calculate the derivate (gradient) values at each layer (all nodes in that layer) from the last layer to the first layer. The gradient descent algorithm then updates the weights in the network after every epoch (iteration) that brings the error down by reducing the cost function.

How does Backpropagation Work?

Backpropagation makes use of some steps to achieve its goal:

1. Inputs are fed into a neural network. 
2. Inputs are then modeled using actual weights. The weights are usually randomly selected.
3. The output for every neuron from the input layer to the hidden layers to the output layer is then calculated.
4. Backtracking is done from the output layer to the hidden layers to adjust the weights such that the error is decreased (cost function is minimized).

The steps are repeated until the desired output accuracy is achieved.

Why do We Need Backpropagation?

It reduces error and increases accuracy. Enough said, don’t you think?

Now, let us move on to the questions related to backpropagation that can be asked in YOUR interview.

Backpropagation ML Interview Questions

What is backpropagation?

Backpropagation is very much a reason why neural network training works. It is a method of optimizing the weights of a neural network based on the cost function obtained in the previous epoch (iteration). Proper tuning of these weights allows us to reduce error and make the model reliable by increasing its accuracy.

Why do we need backpropagation?

The reasons why we need backpropagation are several. However, the most significant ones are:

• It is very applicable in deep neural networks, which work on error-prone projects such as speech and image recognition, where noise is highly prevalent.
• It is also able to function with multiple inputs using chain rules and power rules.
• It is used to calculate the derivative/gradient of the loss function with respect to all the weights in the network.
• Backpropagation minimizes the cost/loss function by updating the weights with the gradient descent/optimization method.
• Produces learning by modifying the weights of the connected nodes during the process of training 
• It is also iterative, recursive, and more efficient.

What are the advantages of backpropagation?

• The advantages of backpropagation are as follows:
• It’s fast, simple, and easy to program
• It has no parameters to tune apart from the number of inputs
• It is a flexible method as it does not require prior knowledge about the network
• It is a standard method that generally works well
• It does not need any special mention of the features of the function to be learned.

What are the disadvantages of backpropagation?

The disadvantages of backpropagation are as stated below:

• The function or performance of the backpropagation network on a particular issue depends on the data input
• These types of networks are susceptible to noisy data, and that can be pretty uncalled for
• The matrix-based approach is used instead of a mini-batch, hence taking longer and be less efficient in comparison

What are the types of backpropagation? Also, how are they different?

There are two types of backpropagation:

Static Back Propagation Neural Network:

Where the static output is generated due to the mapping of static input, and this is used to resolve static classification problems like optical character recognition.

Recurrent Backpropagation Neural Network:

Which is directed forward or conducted until a specific determined/threshold value is reached. After this particular value, the error is evaluated and propagated backward

They are different since the mapping is static and fast in ‘Static Back Propagation’ whilst in ‘Recurrent Back-Propagation,’ this mapping is slower and non-static.

How does backpropagation work?

Backpropagation works as established below:

• The inputs reach that reach the output neuron follow preconnected path
• Then, the real weights are employed to model the input. Usually, these weights values are alotted randomly.
• After this, the output for every neuron is calculated through forward propagation by going through the input layer, hidden layer, and output layer.
• Finally, the error is calculated at the outputs using the equation for backwardpropagation again through output and hidden layers.
• Then, the weights are adjusted to reduce the error.
• This process is repeated as specified or till where error curve converges to a sufficient amount.

What are the applications of backpropagation?

The applications of backpropagation include:

• A neural network that is trained to enunciate each letter of a word and a sentence
• Optimization in the field of speech recognition
• The field of character and face recognition

What is the purpose of backpropagation?

Its purpose is to create a training mechanism for neural networks to ensure that the network is trained to map the inputs to their appropriate outputs in a way where the error is the least.

Wrap Up

To epitomize everything that we have just come across above, it is evident that without backpropagation, the entire backbone of ML/DL and hence AI itself would collapse since the use of this algorithm is essential and plethoric at the same time. It would not be an understatement to say that ‘A deep learning algorithm cannot move forward without backward propagation.’ The so exceedingly relevant applications of the world nowadays include image processing, where you have object detection, recognition, tracking, motion sensing, signal processing, and text to speech recognition, all of which need backpropagation. It doesn’t just end here, backpropagation is even required to understand your thoughts using EEG technology, and this is a massive breakthrough in the field of neuroscience. So you see, backpropagation is absolutely amazing!