Top 10 Deep Learning Questions Asked in Big Tech Company Interviews

The demand for Deep learning is constantly growing. Here are some of the top interview questions on deep learning

The demand for Deep learning is constantly growing. It is a set of techniques that allows machines to anticipate outcomes from a layered set of inputs. Big tech companies are now on the lookout for skilled professionals who can use deep learning and machine learning techniques to build models that can mimic human behavior. Are you one of those who want to start a career in deep learning? Then you need to know about deep learning questions that are asked in big tech companies' interviews.

Here are some of the top interview questions on deep learning:

What is Deep Learning?

If you are up for making a career in Deep Learning, then you should be definitely aware of what deep learning is. Deep learning involves taking large volumes of unstructured or structured data and using complex algorithms to train neural networks. Deep learning is more like an amalgamation of machine learning and Artificial Intelligence (AI) that imitates the way humans gain knowledge. Even though old machine learning techniques are linear, these are characterized in the process of increasing complexity and abstraction.

What is a Neural Network?

Neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of AI, machine learning, and other. There are three types of neural networks: an input layer, a hidden layer, and an output layer.

Define Multi-Layer Perceptron?

As in neural networks, MLPs have three layers. It has the same structure as a single layer perceptron with one or many hidden layers. MLPs are useful in research for their ability to solve problems stochastically, which often allows approximate solutions for extremely complex problems like fitness approximation.

Tell us about Data Normalization and Why is it Important?

Data normalization has several applications. For instance, data normalization helps to get rid of any duplicate data. This reduces any possible redundancies which can adversely affect the data and enhance the capability of efficient data analysis. Data normalization also helps to group the data together.

What is a Boltzmann Machine?

Boltzmann Machine is one of the most basic deep learning models that represents a simplified version of the multi-layer perceptron. Boltzmann machines with unconstrained connectivity have not proven useful for practical problems in machine learning or inference, but if the connectivity is properly constrained, the learning can be made efficient enough to be useful for practical problems. It is one of the most important questions asked in a big tech company interview.

What does the Activation Function do in a Neural Network?

The activation function is the most important factor in a neural network which decides whether or not a neuron will be activated or not and transferred to the next layer. This simply means that it will decide whether the neuron's input to the network is relevant or not in the process of prediction. This is one of the top deep learning questions for the experienced asked to test their practical skills.

How would you simulate the approach AlphaGo took to beat Lee Sedol at Go?

AlphaGo beating Lee Sedol, the best human player at Go, in a best-of-five series was a truly seminal event in the history of machine learning and deep learning. The Nature paper clearly describes how this was accomplished with "Monte-Carlo tree search with deep neural networks that have been trained by supervised learning, from human expert games, and by reinforcement learning from games of self-play."

Explain about Gradient Descent?

Gradient Descent is an optimal algorithm to cut down the cost function or an error. The main aim is to find the local-global minima of a function. This determines the direction the model should take to reduce error. This is one of the deep learning interview questions asked to test your practical skills.

What is your Understanding of Backpropagation?

In machine learning, backpropagation is a widely used algorithm for training feedforward neural networks. Generalizations of backpropagation exist for other artificial neural networks, and functions in general. These classes of algorithms are all referred to generically, as "backpropagation".

Difference between Feedforward Neural networks and Recurrent Neural Network?

This is one of the deep learning questions, where the interviewee expects you to give a detailed and explained answer. Recurrent neural network's signals travel in both directions, creating a looped network. This is one of the top deep learning questions to check your knowledge for a big tech company interview.