TOP DEEP LEARNING QUESTIONS FOR MACHINE LEARNING INTERVIEWS

The following are deep learning interview questions and answers. It is one of the fastest-growing fields of information technology. A set of skills enables a machine to predict an outcome from a multi-step set of inputs. Since its inception, deep learning is being used by many large tech companies around the world that can create many jobs in this field. Should you be one of those who want to start a career in deep learning? Next, you need to be aware of the interview questions you will be asked during the interview. Learn more about it in this article. Here are some of the most popular deep learning interview questions.

1. What is Deep Learning?

Deep learning involves training neural networks using large amounts of unstructured or structured data and using complex algorithms. It also supports complex tasks such as extracting hidden patterns and features, such as distinguishing between cats and dogs. This is one of the most frequently asked interview questions by beginners to test their answering skills.

2. What is a Neural Network?

Neural networks are inspired by the way neurons in our brains are activated and can mimic the way humans learn, but much simpler. There are three types of neural networks:  input layer,  hidden layer, and output layer.

3. Define Multi-Layer Perceptron?

Like neural networks, MLP has three layers. It has a single-layer perceptron-like structure with one or more hidden layers. A single layer perceptron can only classify linear subclasses with binary output (0,1), whereas MLP  can classify nonlinear classes.

4. Tell us about Data Normalization and Why is it Important?

The process of normalizing and transforming data is called "data normalization." This is a preprocessing step to deduplicate data. Usually, data comes in and you get the same information in different formats. In such cases, you need to readjust the values ​​to fit your specific space for better convergence.

5. What is a Boltzmann Machine?

The Boltzmann machine is one of the most basic models representing a simplified version of a multilayer perceptron. The model has a hidden layer consisting of a visible input layer and a two-level neural network that makes a probabilistic decision about whether a neuron should be turned on or not. A node is connected between layers, but no two nodes at the same level are connected.

6. What does the Activation Function do in a Neural Network?

The activation function determines whether the neuron should fire or not. Takes the weighted sum of the input and the offset as input to the activation function. Step Function, ReLU, Sigmoid, Tanh, and Software are just a few examples of  action functions. This is one of the most frequently asked questions by experienced professionals to test their practical skills in an interview.

7. What is the Cost Function?

The cost function is also called loss or error. This is one of the deep learning interview questions to test your answering skills. It measures  how well the model performs. It is used to calculate the error in the final product layer during backpropagation of the error. We push this error back through the neural network and use it during various training functions.

8. Explain about Gradient Descent?

Gradient descent is an optimal algorithm for reducing cost  or error functions. The main goal is to find the local global minimum of the function. This determines the direction the model should move to reduce errors. This is one of the interview questions to test your practical skills.

9. What is your Understanding of Backpropagation?

This is one of the most frequently asked deep learning interview questions. So, backpropagation is a technique to improve network performance, which reduces the number of errors by propagating errors in the opposite direction and updating weights.

10. Difference between Feedforward Neural Network and Recurrent Neural Network?

This is one of the questions the interviewee expects you to provide a detailed and descriptive answer to. Feedforward neural network signals only propagate in a specific direction from input to output. There is no feedback and the network only considers call input. On the other hand, repetitive signals in a neural network propagate in both directions, creating a network with loops. This is one of the most popular deep learning interview questions to test your differentiation knowledge  in an interview.