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Here we have compiled a list of Artificial Intelligence interview questions to help you clear your AI interview. We have included AI programming languages and applications, Turing test, expert system, details of various search algorithms, game theory, fuzzy logic, inductive, deductive, and abductive Machine Learning, ML algorithm techniques, Naïve Bayes, Perceptron, KNN, LSTM, autoencoder, and much more related topics in this blog.
Tower of Hanoi is a mathematical puzzle that shows how recursion might be utilized as a device in building up an algorithm to take care of a specific problem. Using a decision tree and a breadth-first search (BFS) algorithm in AI, we can solve the Tower of Hanoi.
Dimensionality reduction is the process of reducing the number of random variables. We can reduce dimensionality using techniques such as missing values ratio, low variance filter, high correlation filter, random forest, principal component analysis, etc.
Bias error is used to measure how much on an average the predicted values vary from the actual values. In case a high-bias error occurs, we have an under-performing model.
Variance is used to measure how the predictions made on the same observation differ from each other. A high-variance model will overfit the dataset and perform badly on any observation.
Dropout is a simple way to prevent a neural network from overfitting. It is the dropping out of some of the units in a neural network. It is similar to the natural reproduction process, where nature produces offsprings by combining distinct genes (dropping out others) rather than strengthening the co-adapting of them.
An autoencoder is basically used to learn a compressed form of the given data. A few applications of an autoencoder are given below:
The intermediate tensors are tensors that are neither inputs nor outputs of the Session.run() call, but are in the path leading from the inputs to the outputs; they will be freed at or before the end of the call.
Sessions can own resources, few classes like tf.Variable, tf.QueueBase, and tf.ReaderBase, and they use a significant amount of memory. These resources (and the associated memory) are released when the session is closed, by calling tf.Session.close.
When we first run the tf.Variable.initializer operation for a variable in a session, it is started. It is destroyed when we run the tf.Session.close operation.
A hash table is a data structure that is used to produce an associative array which is mostly used for database indexing.
F1 score is the weighted average of precision and recall. It considers both false positive and false negative values into account. It is used to measure a model’s performance.
Ensemble learning is a computational technique in which classifiers or experts are strategically formed and combined. It is used to improve classification, prediction, function approximation, etc. of a model.
Click here on Artificial intelligence Interview questions to know all top Q&A to crack the interview.
