## Introduction

Deep Learning entails taking large amounts of structured or unstructured data and training neural networks with complex algorithms. It carries out complex operations to extract hidden features and patterns (for example, distinguishing the image of a dog from a cat). Deep Learning has seen growth in popularity over the years, with applications in virtually every business sector.

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 top 30 Deep Learning interview questions. So without wasting any time let's get started with some important Deep learning interview questions.

## Most Commonly Asked Top Deep Learning Interview Questions and Answers

**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.

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**16. What Is Overfitting and Underfitting, and How Can You Avoid It?**

Overfitting usually occurs when the model learns the details and noise in the training data to the point where it negatively impacts the model's execution on new data. Learning a target function is more likely to occur with nonlinear models that have more flexibility. For instance, suppose a model looks at cars and trucks but only recognizes trucks with a specific box shape. It may not detect a flatbed truck because it has only seen one type of truck in training. The model may work well on training data but not in practice.

Underfitting refers to a model that is not well-trained on data and cannot generalize to new data. This usually occurs when there is insufficient and incorrect data to train a model. Underfitting has a negative impact on both performance and accuracy.

To combat overfitting and underfitting, you can resample the data to estimate model accuracy (k-fold cross-validation) and evaluate the model with a validation dataset.

**17. How Do Weights in a Network initialized?**

We have two options: we can set the weights to zero or assign them randomly.

Setting all weights to 0 transforms your model into a linear one. All neurons and layers perform the same operation, resulting in the same output and rendering the deep net useless.

Randomly assigning all weights: The weights are assigned randomly here by initializing them very close to 0. It improves model accuracy because each neuron performs a different computation. This is the most popular method.

**18. What Are the Different CNN Layers?**

CNN is made up of four layers:

**Convolutional Layer:**This layer performs a convolutional operation on the data, generating several smaller picture windows.**The ReLU Layer:**It adds nonlinearity to the network by converting all negative pixels to zero. The result is a feature map that has been rectified.**Pooling Layer:**Pooling is a downsampling operation that reduces the feature map's dimensionality.-
**Fully Connected Layer:**It identifies and classifies the objects in the image.

**19. How Does an LSTM Network Function?**

Long-Short-Term Memory (LSTM) is a type of recurrent neural network that can learn long-term dependencies and remember information as its default behavior for long periods. An LSTM network consists of three steps:

**Step 1:** The network decides what to remember and what to forget.

**Step 2:** It updates cell state values selectively.

**Step 3:** The network determines which parts of the current state are output.

**20. What Is the Difference Between Vanishing and Exploding Gradients?**

When training an RNN, the slope can become too small or too large, making training difficult. A "Vanishing Gradient" problem occurs when the slope is too small. An "Exploding Gradient" occurs when the slope grows exponentially rather than decaying. Gradient issues result in lengthy training times, poor performance, and poor accuracy.

**21. What Is the Difference Between Deep Learning Epoch, Batch, and Iteration?**

**Epoch:** One iteration over the entire dataset (everything put into the training model).

**Batches**: When we cannot pass the entire dataset into the neural network at once, we divide it into several batches.

**Iteration:** For example, if we have 10,000 images as data and a batch size of 200, an epoch should run 50 iterations (10,000 divided by 50).

**22. Describe a Computational Graph.**

Tensorflow is built around the creation of a computational graph. It consists of a network of nodes, each representing a mathematical operation and the edges representing tensors. Because data flows in the form of a graph, it is also referred to as a "DataFlow Graph."

**23. Describe the Generative Adversarial Network.**

Assume there is a wine shop that buys wine from dealers and resells it later. However, some dealers sell phoney wine. In this case, the shopkeeper should be able to tell the difference between fake and authentic wine.

The forger will try various techniques to sell fake wine and ensure that specific techniques pass the shop owner's inspection. Wine experts will likely inform the shop owner that some of the wine is not original. The owner would need to improve his ability to tell whether a wine is fake or authentic.

The forger's goal is to create indistinguishable wines from the authentic ones, whereas the shop owner's goal is to accurately determine whether the wine is real or not.

Let's break down this example

- A noise vector is entering the forger who is creating fake wine.
- In this case, the forger serves as a Generator.
- The shopkeeper is a Discriminator.
- The Discriminator is given two inputs: one of fake wine and one of real authentic wine. The shopkeeper must determine whether it is genuine or counterfeit.
- As a result, the two primary components of the Generative Adversarial Network (GAN) are named:

Generator\sDiscriminator

The generator is a CNN that continuously generates images that resemble real images, whereas the discriminator attempts to distinguish between fake and real images. The ultimate goal is for the discriminator to learn to distinguish between real and fake images.

**24. What is an Auto-encoder?**

This Neural Network has three layers, with input neurons equaling output neurons. The network's external target is the same as its input. It restructures the input using dimensionality reduction. It operates by compressing the image input to a latent space representation and then reconstructing the output from it.

**25. What Is the Difference Between Bagging and Boosting?**

Bagging and boosting are ensemble techniques for training multiple models with the same learning algorithm before making a decision.

We use Bagging to divide a dataset into training and test data. Then we randomly choose data to place in the bags and train the model separately.

**26. What role does the Fourier transform play in Deep Learning tasks?**

The Fourier transform function analyses maintain and manage large datasets efficiently. It can generate real-time array data, which is useful for processing multiple signals.

**27. What exactly do you mean by transfer learning? Name a few popular transfer learning models.**

Transfer learning is transferring knowledge from one model to another without training it from the ground up. It uses critical parts of a previously trained model to solve new but similar machine learning problems.

Popular transfer learning models include:

- BERT
- VGG-16
- Inception V3
- XCeption
- GTP-3

**28. What is Deep Learning Data Augmentation?**

Data Augmentation is creating new data by increasing the size and quality of training datasets to build better models with them. Data augmentation techniques include numerical data augmentation, image augmentation, GAN-based augmentation, and text augmentation.

**29. Describe Adam's optimization algorithm.**

Adaptive Moment Estimation, also known as Adam optimization, is a stochastic gradient descent extension. This algorithm comes in handy when dealing with complex problems involving large amounts of data or parameters. It requires less memory and is more efficient.

The Adam optimization algorithm is a hybrid of two gradient descent methods -

Root Mean Square Propagation and Momentum

**30. What are some of the Deep Learning applications of Autoencoders?**

- Autoencoders convert black-and-white images into color images.
- Autoencoder aids in the extraction of features and hidden patterns in data.
- It is also used to reduce data dimensionality.
- It is also useful for removing noise from images.