Artificial Intelligence Interview Questions

Artificial Intelligence Interview Questions & Answers Artificial Intelligence is a newly emerging term in IT field. AI is a computer system able to perform tasks that requires human intelligence. Many of these artificial intelligence systems are powered by machine learning, some of them are powered by deep learning and some of them are powered by very boring things like rules. This workbook contains extensive set of Artificial Intelligence / Deep Learning questions and answers that can help clear your understanding on the topic and also help in responding to questions posed to an interviewee. With the difficulty level of questions ranging from low to high, we intend to cater to the requirements of masses. This guide will benefit: • A beginner who has never faced any Machine Learning / Artificial Intelligence interview • Anyone who wants a brief on Artificial Intelligence • Professional who want answers with examples and explanation • One who don't know what "They" really want to hear…. How should you read this book? You have to first do a slow reading of all the questions in this book. Once you go through them in the first pass, mark the questions that you could not answer by yourself. Then, in second pass go through only the difficult questions. After going through this book 2-3 times, you will be well prepared to face a technical interview for a Machine Learning position. What are the sample questions in this book? What is reinforcement learning? How are weights initialized in a Network? Why do we actually need convolutions? Can't we use fully-connected layers for that? Explain LSTM hyperparameter tuning? Explain Multi-layer Perceptron(MLP)? Name problems with sigmoid as an activation function? What regularization techniques for neural nets do you know? What are the different layers of Autoencoders? Explain briefly. 'People who bought this also bought…' recommendations seen on Amazon are a result of which algorithm? Can overfitting happen in a neural network? If yes, how to deal with overfitting in a neural network?