Deep Learning Interview Questions 2023

 

Q1) What is deep learning?

Deep learning is a part of machine learning with an algorithm inspired by the structure and function of the brain, which is called an artificial neural network. In the mid-1960s, Alexey Grigorevich Ivakhnenko published the first general, while working on a deep learning network. Deep learning is suited over a range of fields such as computer vision, speech recognition, natural language processing, etc.

Q2)What is the difference between supervised and unsupervised deep learning?

Supervised learning refers to the learning method which trains machines through labeled data. This data is already categorized and tagged to the correct set of answers. When a machine is fed this data, it analyses the training set and produces the correct result. 

Unsupervised learning, on the other hand, does not require the data to be labelled. Machines self-learn from identifying patterns and model data according to probability densities.

Q3)What are data visualization libraries?

Data visualization libraries help in understanding complex ideas by using visual elements such as graphs, charts, maps, and more. The visualization tools help you to recognize patterns, trends, outliers, and more, making it possible to design your data according to the requirement. Popular data visualization libraries include D3, React-Vis, Chart.js, vx, and more.

Q4)How are Deep networks better than shallow networks?

Neural networks include hidden layers apart from input and output layers. Shallow neural networks use a single hidden layer between the input and output layers whereas Deep neural networks, use multiple layers. For a shallow network to fit into any function, it needs to have a lot of parameters. However, since deep networks have several layers, they can fit functions better even with a limited number of parameters. Today deep networks have become preferable owing to its ability to work on any kind of data modeling, whether it is for voice or image recognition.

Q5)What are the applications of deep learning?

There are various applications of deep learning: 

• Computer vision 
• Natural language processing and pattern recognition 
• Image recognition and processing
•  Machine translation 
• Sentiment analysis
•  Question Answering system 
• Object Classification and Detection 
• Automatic Handwriting Generation 
• Automatic Text Generation.

Q6) What are the advantages of neural networks?

Following are the advantages of neural networks: 

•  Neural networks are extremely adaptable, and they may be used for both classification and regression problems, as well as much more complex problems. Neural networks are also quite scalable. We can create as many layers as we wish, each with its own set of neurons. When there are a lot of data points, neural networks have been shown to generate the best outcomes. They are best used with non-linear data such as images, text, and so on. They can be applied to any data that can be transformed into a numerical value.
• Once the neural network mode has been trained, they deliver output very fast. Thus, they are time-effective.

Q7)What are the disadvantages of neural networks?

Following are the disadvantages of neural networks:-

• The "black box" aspect of neural networks is a well-known disadvantage. That is, we have no idea how or why our neural network produced a certain result. When we enter a dog image into a neural network and it predicts that it is a duck, we may find it challenging to understand what prompted it to make this prediction.
• It takes a long time to create a neural network model.
• Neural networks models are computationally expensive to build because a lot of computations need to be done at each layer.
• A neural network model requires significantly more data than a traditional machine learning model to train.

Q8)What is a perceptron?

A perceptron is similar to the actual neuron in the human brain. It receives inputs from various entities and applies functions to these inputs, which transform them to be the output. A perceptron is mainly used to perform binary classification where it sees an input, computes functions based on the weights of the input, and outputs the required transformation.