Mention methods for reducing dimensionality.
AI questions like this can be easy and difficult at the same time as you may know the answers but not on the tip of your tongue. Hence, a quick refresher can help a lot. Reducing dimensionality refers to the reduction of the number of random variables. This can be achieved by different techniques including principal component analysis, low variance filter, missing values ratio, high correlation filter, random forest, and others.
Mention some advantages of neural networks.
Below are some advantages of neural networks:
Neural networks are an important part of artificial intelligence and hence, you should expect some AI interview questions to be centered around them.
TensorFlow is an essential open-source library for Machine Learning. As a toolkit for complex algorithms, TensorFlow offers speed and flexibility and is not too advanced. It is customizable and thus, helps users create experimental learning architectures and work on the same to produce desired results.
Explain cost function.
This is a popular AI interview question. A cost function is a scalar function that helps identify how wrong an AI model is with regard to its ability to determine the relationship between X and Y. In other words, it tells us the neural network’s error factor. The neural network works better when the cost function is lower. For instance, it takes the output predicted by the neural network and the actual output and then computes how incorrect the model was in its prediction. So, the cost function will give a lower number if the predictions don’t differ too much from the actual values and vice-versa.
Mention hyper-parameters of ANN.
The hyper-parameters of ANN are as follows:
Dropout is a method used to prevent the overfitting of a neural network. It refers to dropping out some neural network units. The process is similar to the process of natural reproduction, where distinct genes combine to produce offspring while the other genes are dropped out instead of strengthening their co-adaptation.
Explain vanishing gradient.
As more layers are added and the distance from the final layer increases, backpropagation is not as helpful in sending information to the lower layers. As a result, the information is sent back, and the gradients start disappearing and becoming small in relation to network weights.
Mention the steps of the gradient descent algorithm.
The gradient descent algorithm helps in optimization and in finding coefficients of parameters that help minimize the cost function. The steps that help achieve this are as follows:
Step 1: Give weights (x,y) random values and then compute the error, also called SSE Step 2: Compute the gradient or the change in SSE when you change the value of the weights (x,y) by a small amount. This step helps us identify the direction in which we must move x and y to minimize SSE. Step 3: Adjust the weights with the gradients for achieving optimal values for the minimal SSE. Step 4: Change the weights for predicting and calculating the new error. Step 5: Repeat steps 2 and 3 till the time making more adjustments stops producing significant error reduction.
Ensure that you go through AI questions that involve multiple steps and be sure to enlist all steps when answering.
Explain intermediate tensors. Do sessions have a lifetime?
In the Session.run() call, the intermediate tensors are not inputs or outputs, instead, they occur in the path between the inputs and the outputs in the direction of the outputs from the inputs. They become free either before or at the end of the call.
Sessions have their own resources in the form of classes such as tf.Variable, tf.QueueBase, and tf.ReaderBase. These resources use a copious amount of memory. However, the sessions and their related memory become free when the session comes to a close. This is done by using tf.Session.close.
Can you solve logical inference in propositional logic?
Yes, we can solve logical inference in propositional logic. We need to use the following concepts to do so:
You have now gone through some of the key Artificial Intelligence interview questions, however, this is not the only type of questions you will face in the AI interview. Your AI interview will comprise questions based on soft skills to assess whether you will fit with the team culture at the organization or not. Recruiters must ensure that they conduct this part of the interview to get the best candidates for their organization.
If you think you have it in you to answer all types of questions in your AI interview, you can apply to the AI engineer positions at Turing. If you want to build a team of the best AI engineers, leave a message on Turing’s website, and someone will get in touch with you.
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