Top 10 Deep Learning Questions Asked in Interviews with Big Tech Companies

The demand for deep learning continues to grow. Here are some of the top deep learning interview questions

The demand for deep learning continues to grow. It is a set of techniques that allows machines to predict outcomes from a set of layered inputs. Big tech companies are now looking for skilled professionals who can use deep learning and machine learning techniques to create 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 the deep learning questions that are asked in interviews with big tech companies.

Here are some of the top deep learning interview questions:

What is Deep Learning?

If you are ready for a career in deep learning, you absolutely need to know 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 mimics the way humans acquire knowledge. Even though older machine learning techniques are linear, they are characterized by a process of increasing complexity and abstraction.

What is a neural network?

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

Define Multilayer Perceptron?

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

Tell us about data normalization and why is it important?

Data normalization has several applications. For example, data normalization helps get rid of all duplicate data. This reduces possible redundancies that can negatively affect the data and improves the ability to effectively analyze the data. Data normalization also allows data to be grouped.

What is a Boltzmann machine?

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

What does the activation function do in a neural network?

Activation function is the most important factor in a neural network that decides whether or not a neuron will be activated or not and transferred to the next layer. It just means that it will decide whether the input of the neuron in the network is relevant or not in the prediction process. This is one of the main deep learning questions for experienced people who are asked to test their practical skills.

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

AlphaGo beating Lee Sedol, the top human Go player, in a best-of-five series was a truly landmark event in the history of machine learning and deep learning. The Nature article clearly describes how this was accomplished with “the search for Monte Carlo trees with deep neural networks that were trained by supervised learning, from human expert games, and by learning by reinforcement from games of self-play”.

Explain about Gradient Descent?

Gradient Descent is an optimal algorithm to reduce the cost function or an error. The main objective is to find the local-global minima of a function. This determines the direction the model should take to reduce the 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 to train feedback 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 Network and Recurrent Neural Network?

This is one of the deep learning questions, where the interviewee expects you to give a detailed and explained answer. Recurring signals from the neural network travel back and forth, creating a looping network. This is one of the best deep learning questions to check your knowledge for an interview with a big tech company.

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