Deep Learning Interview Questions and Answers
Question - 1 : - What is deep learning ?
Answer - 1 : -
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 deep learning network. Deep learning is suited over a range of fields such as computer vision, speech recognition, natural language processing, etc.
Question - 2 : - What is the difference between Machine Learning and Deep Learning?
Answer - 2 : -
Machine Learning forms a subset of Artificial Intelligence, where we use statistics and algorithms to train machines with data, thereby, helping them improve with experience.
Deep Learning is a part of Machine Learning, which involves mimicking the human brain in terms of structures called neurons, thereby, forming neural networks.
Question - 3 : - What is a perceptron?
Answer - 3 : -
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.
Question - 4 : - How is Deep Learning better than Machine Learning?
Answer - 4 : -
Machine Learning is powerful in a way that it is sufficient to solve most of the problems. However, Deep Learning gets an upper hand when it comes to working with data that has a large number of dimensions. With data that is large in size, a Deep Learning model can easily work with it as it is built to handle this.
Question - 5 : - What are some of the most used applications of Deep Learning?
Answer - 5 : -
Deep Learning is used in a variety of fields today. The most used ones are as follows:
- Sentiment Analysis
- Computer Vision
- Automatic Text Generation
- Object Detection
- Natural Language Processing
- Image Recognition
Question - 6 : - What is the meaning of overfitting?
Answer - 6 : -
Overfitting is a very common issue when working with Deep Learning. It is a scenario where the Deep Learning algorithm vigorously hunts through the data to obtain some valid information.
This makes the Deep Learning model pick up noise rather than useful data, causing very high variance and low bias. This makes the model less accurate, and this is an undesirable effect that can be prevented.
Question - 7 : - What are activation functions?
Answer - 7 : -
Activation functions are entities in Deep Learning that are used to translate inputs into a usable output parameter. It is a function that decides if a neuron needs activation or not by calculating the weighted sum on it with the bias.
Using an activation function makes the model output to be non-linear. There are many types of activation functions:
- ReLU
- Softmax
- Sigmoid
- Linear
- Tanh
Question - 8 : - Why is Fourier transform used in Deep Learning?
Answer - 8 : -
Fourier transform is an effective package used for analyzing and managing large amounts of data present in a database. It can take in real-time array data and process it quickly. This ensures that high efficiency is maintained and also makes the model more open to processing a variety of signals.
Question - 9 : - What are the steps involved in training a perception in Deep Learning?
Answer - 9 : -
There are five main steps that determine the learning of a perceptron:
- Initialize thresholds and weights
- Provide inputs
- Calculate outputs
- Update weights in each step
- Repeat steps 2 to 4
Question - 10 : - What is the use of the loss function?
Answer - 10 : -
The loss function is used as a measure of accuracy to see if a neural network has learned accurately from the training data or not. This is done by comparing the training dataset to the testing dataset.
The loss function is a primary measure of the performance of the neural network. In Deep Learning, a good performing network will have a low loss function at all times when training.