Introduction
Deep Learning has revolutionized the world of artificial intelligence (AI) by enabling machines to mimic the human brain's ability to learn from vast amounts of data. From self-driving cars to voice assistants, deep learning algorithms are at the heart of many cutting-edge applications across industries. As demand for AI professionals continues to rise, companies are seeking candidates with a strong grasp of deep learning principles and techniques.

If you are preparing for a Deep Learning interview and want a quick guide of Deep learning interview questions before your interview, you have come to the right place.
This blog features the most asked Deep Learning interview questions. So without wasting any time let's get started with some important Deep learning interview questions.
Deep Learning Interview Questions for Freshers
1. What is Deep Learning?
It is a branch of machine learning. Deep Learning is a field that is based on self-learning and improvement through the examination of computer algorithms.
As opposed to machine learning, deep learning works with artificial neural networks designed to mimic how humans think and learn. Until recently, neural networks were limited in complexity due to computing power constraints. On the other hand, advances in Big Data analytics have enabled larger, more sophisticated neural networks, allowing computers to observe, learn, and react to complex situations faster than humans. Speech recognition, Image classification, and language translation have benefited deep learning. It can solve any pattern recognition problem without the need for human intervention.
2. What is a Neural Network?
Neural Networks mimic how humans learn by being inspired by how neurons in our brains fire, but they are much simpler.
The three most common network layers in Neural Networks are:
- Input Layer: The first layer is the input layer. This layer receives the data and forwards it to the rest of the network.
- Hidden layer: Hidden layer is the most important layer where feature extraction is performed, and adjustments are made to help the model train faster and function better.
-
Output Layer: Each sheet contains "nodes," which are neurons that perform various operations. Deep learning algorithms such as RNN, CNN, GAN, and others use neural networks.
3. What is a Multilayer Perceptron (MLP)?
MLPs, like Neural Networks, have three layers: an input layer, a hidden layer, and an output layer. It is built similarly to a single-layer perceptron with one or more hidden layers. MLP can classify nonlinear classes, while a single layer perceptron can only classify linear separable classes with binary output (0,1).
Each node in the other layers, except for the input layer, employs a nonlinear activation function. This means that the input layers, the data coming in, and the activation function are all based on adding all nodes and weights to produce the output. MLP employs a supervised learning technique known as "backpropagation." The neural network calculates the error using the cost function during backpropagation. This error is propagated backward from where it originated (adjusts the weights to train the model more accurately).
4. What Is Data Normalization and Why Is It Necessary?
"Data Normalization" refers to the process of standardizing and reforming data. It is a pre-processing step used to remove data redundancy. Data is frequently received, and you receive the same information in various formats. In these cases, rescale values to fit into a specific range to achieve better convergence.
5. What exactly is the Boltzmann Machine?
A Boltzmann Machine, similar to a simplified version of the Multi-Layer Perceptron, is one of the most basic Deep Learning models. This model consists of a visible input layer and a hidden layer. Essentially, it is a two-layer neural net that makes stochastic decisions about whether a neuron should be turned on or off. Nodes are linked across layers, but no nodes in the same layer are linked.
6. What is the activation function in a Neural Network?
At its most basic, an activation function determines whether or not a neuron should fire. It accepts the input's bias and weighted sum as input to any activation function. Activation functions include the step function, Sigmoid function, ReLU function, Tanh function, and Softmax function.
7. What Exactly Is the Cost Function?
The cost function, also known as "loss" or "error," is a metric to assess your model's performance. It is used to compute the output layer's error during backpropagation. We feed that error into the neural network and use it during the various training functions.
c=1/2(x-y)^2
8. What Is Gradient Descent?
Gradient Descent is the best algorithm for minimizing a cost function or an error. The goal is to find a function's local-global minima. This determines which path the model should take to reduce error.
9. What exactly do you mean by backpropagation?
This is one of the most commonly encountered deep learning interview questions. Backpropagation is a network performance improvement technique. To reduce the error, it backpropagates the error and updates the weights.
10. What Is the Difference Between a Recurrent Neural Network and a Feedforward Neural Network?
Signals in a Feedforward Neural Network travel in one direction from input to output. There are no feedback loops; the network only considers the current input. It is unable to remember previous inputs (e.g., CNN).
Signals in a Recurrent Neural Network travel in both directions, resulting in a looped network. It uses the current and previously received inputs to generate a layer's output and can remember past data due to its internal memory.
11. What Are the Uses for a Recurrent Neural Network (RNN)?
The RNN can analyze sentiment, mine text, and caption images. Recurrent Neural Networks can also be used to solve time series problems, such as predicting stock prices over a month or quarter.
12. What Are the Functions of Softmax and ReLU?
Softmax is an activation function that produces an output between 0 and 1. It divides each output so that the sum of the outputs equals one. Softmax is frequently used in output layers.
The most common activation function is ReLU (or Rectified Linear Unit). It returns X if X is positive and zeroes otherwise. ReLU is frequently used to represent hidden layers.
13. What Exactly Are Hyperparameters?
This is another common deep learning interview question. Once properly formatted data, you usually work with hyperparameters with neural networks. A hyperparameter is a parameter for which value is determined before the start of the learning process. It is used to determine how a network is trained and its structure (such as the learning rate, number of hidden units, epochs, etc.).
14. What Happens If You Set the Learning Rate Too Low or Too High?
When your learning rate is too low, the model's training will be very slow because we are only making minor changes to the weights. It will take several updates to reach the minimum point.
When the learning rate is too high, the loss function exhibits undesirable divergent behaviour due to drastic weight updates. It may fail to converge (model may produce good results) or even diverge (data is too chaotic for the network to train).
15. What Is the Difference Between Dropout and Batch Normalization?
Dropout is a technique for randomly removing hidden and visible network units to prevent data overfitting (typically dropping 20 percent of the nodes). It doubles the number of network convergence iterations required.
Batch normalization is a technique for improving neural network performance and stability by normalizing the inputs in each layer to have a mean output activation of zero and a standard deviation of one.
Must Read Ruby on Rails Interview Questions