Artificial Intelligence Interview Questions and Answers
Question - 61 : - How is overfitting avoided in neural networks?
Answer - 61 : -
Overfitting is avoided in neural nets by making use of a regularization technique called ‘dropout.’
By making use of the concept of dropouts, random neurons are dropped when the neural network is being trained to use the model doesn’t overfit. If the dropout value is too low, it will have a minimal effect. If it is too high, the model will have difficulty in learning.
Question - 62 : - What is overfitting? How is overfitting fixed?
Answer - 62 : -
Overfitting is a situation that occurs in statistical modeling or Machine Learning where the algorithm starts to over-analyze data, thereby receiving a lot of noise rather than useful information. This causes low bias but high variance, which is not a favorable outcome.
Overfitting can be prevented by using the below-mentioned methods:
- Early stopping
- Ensemble models
- Cross-validation
- Feature removal
- Regularization
Question - 63 : - What are some of the algorithms used for hyperparameter optimization?
Answer - 63 : -
There are many algorithms that are used for hyperparameter optimization, and the following are the three main ones that are widely used:
- Bayesian optimization
- Grid search
- Random search
Question - 64 : - What are intermediate tensors? Do sessions have lifetime?
Answer - 64 : -
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.
Question - 65 : - What is the lifetime of a variable?
Answer - 65 : -
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.
Question - 66 : - Is it possible to solve logical inference in propositional logic?
Answer - 66 : -
Yes, logical inference can easily be solved in propositional logic by making use of three concepts:
- Logical equivalence
- Process satisfaction
- Validation checking
Question - 67 : - How does face verification work?
Answer - 67 : -
Face verification is used by a lot of popular firms these days. Facebook is famous for its usage of DeepFace for its face verification needs.
There are four main things you must consider when understanding how to face verification works:
Input: Scanning an image or a group of images
Process:
- Detection of facial features
- Feature comparison and alignment
- Key pattern representation
- Final image classification
Output: Face representation, which is a result of a multilayer neural network
Training data: Involves the usage of thousands of millions of images
The implementation of face verification in Python requires special libraries such as glob, NumPy, OpenCV(cv2), and face_recognisation. Among them, OpenCV is one of the most widely used libraries for computer vision and image processing.
OpenCV is a beginner-friendly, cross-platform python library that is mainly used for real-time image and video processing applications. WithOpenCV, you can create applications used for object detection, facial recognition, and object tracking. It can also be used to extract the facial features and identify unique patterns for face verification.