Deep Learning Interview Questions

How are Machine Learning and Deep Learning different from each other?
Machine Learning is a subfield of artificial intelligence that focuses on the development of algorithms and statistical models that enable computers to learn and make predictions or decisions without being explicitly programmed. Deep Learning, on the other hand, is a subset of Machine Learning that uses neural networks with multiple layers to analyze complex patterns and relationships in data.
What is the definition of a perceptron?
A perceptron is a type of artificial neuron or the simplest form of a neural network. It is a model of a single neuron that can be used for binary classification problems, which means it can decide whether an input represented by a vector of numbers belongs to one class or another.
In what ways is Deep Learning superior to Machine Learning?
Deep Learning is better than Machine Learning in some aspects, such as handling complex, real-world problems and learning from large amounts of data. Deep Learning models can automatically learn and improve from data without the need for manual feature engineering. However, Deep Learning also has some drawbacks, such as longer training times, the need for larger datasets, and the difficulty of interpreting the results.
What are some of the common uses of Deep Learning?
Some of the most used applications of Deep Learning are:

Voice assistants: Deep Learning is used to power natural language processing and speech recognition systems that enable users to interact with devices using voice commands. Examples are Siri, Alexa, and Google Assistant.
Self-driving cars: Deep Learning is used to enable autonomous driving by processing sensor data and making decisions in real-time. Examples are Tesla Autopilot, Waymo, and Uber.
Image recognition: Deep Learning is used to identify and classify objects, faces, scenes, and emotions in images. Examples are Google Photos, Facebook, and Snapchat.
Natural language processing: Deep Learning is used to analyze and generate natural language texts, such as translating, summarizing, answering questions, and creating chatbots. Examples are Google Translate, BERT, and GPT-3.
Recommendation systems: Deep Learning is used to provide personalized recommendations to users based on their preferences, behavior, and context. Examples are Netflix, Amazon, and Spotify.
What does over fitting mean?
Over fitting is a term used in statistics that refers to a modeling error that occurs when a function corresponds too closely or exactly to a particular set of data, and May therefore fail to fit to additional data or predict future observations reliably. An over fitted model is a mathematical model that contains more parameters than can be justified by the data.
What are the functions of activation functions?
Activation functions are functions used in a neural network to compute the weighted sum of inputs and biases, which is in turn used to decide whether a neuron can be activated or not. It manipulates the presented data and produces an output for the neural network that contains the parameters in the data.
What is the purpose of using the Fourier transform in Deep Learning?
The Fourier transform is a mathematical technique that transforms a signal from the time domain to the frequency domain. It is used in Deep Learning to analyze the frequency components of the input data and extract useful features. For example, the Fourier transform can be used to enhance the performance of convolutional neural networks by reducing the computational complexity and improving the generalization ability.
What are the steps involved in training a perceptron in Deep Learning?
The steps involved in training a perceptron in Deep Learning are:
Initialize the weights and bias of the perceptron randomly.
Present a training sample to the perceptron and calculate the output using the activation function.
Compare the output with the target value and calculate the error.
Update the weights and bias of the perceptron using a learning algorithm, such as the perceptron learning algorithm or back propagation, based on the error.
Repeat steps 2 to 4 for all the training samples until the error is minimized or a maximum number of iterations is reached.
What is the role of the loss function?
The loss function is a mathematical function that quantifies the difference between the predicted outputs of a machine learning or deep learning model and the actual target values. It measures the model’s performance and guides the optimization process by providing feedback on how well it fits the data.
What are some of the Deep Learning frameworks or tools that you have used?
Some of the Deep Learning frameworks or tools that I have used are:

TensorFlow: Google’s open-source platform that allows you to build and deploy machine learning and deep learning models using various tools and community resources.
Keras: A high-level neural network API that runs on top of TensorFlow and other frameworks. It simplifies the creation and training of deep learning models.
PyTorch: An open-source framework that provides a flexible and dynamic way of building and training deep learning models using tensors and automatic differentiation.
SciKit-Learn: A popular framework that provides a range of tools for machine learning and data analysis, including some modules for deep learning.
Apache MXNet: An open-source framework that supports multiple programming languages and platforms. It allows you to define, train, and deploy deep neural networks with high scalability and performance.