Frequently asked Artificial intelligence interview questions – part 2

Deep learning systems rely on specialized data structures to handle and process various types of data efficiently: • Tensors: These are multi-dimensional arrays used to store data in frameworks like TensorFlow and PyTorch. Tensors generalize scalars, vectors, and matrices. • Matrices: Two-dimensional arrays commonly used in mathematical operations like multiplication or inversion, often applied in model calculations. • Vectors: One-dimensional arrays that often represent features, model weights, or intermediate outputs. • Arrays: Structured blocks of memory used to store homogeneous data, they can be 1D, 2D, or even higher-dimensional, serving as the basis for vectors and matrices.

The hidden layer in a neural network acts as a feature extractor. It processes input data and transforms it into patterns or features that help the network understand and solve tasks. These transformed features are passed to the output layer for final predictions. In essence, hidden layers enable the network to learn complex relationships in the data.

Neural networks offer several benefits: • They require minimal statistical knowledge to train. • They’re capable of modeling complex, non-linear relationships. • They perform well with large datasets and can uncover deep insights. • They can handle noisy or incomplete data by extracting relevant features. They adapt and learn continuously, making them suitable for dynamic environments.

Stemming is a rule-based method that trims words to their root form by chopping off suffixes, often without considering actual word meanings. Lemmatization, on the other hand, is more advanced—it uses vocabulary and grammatical rules to reduce a word to its base or dictionary form (lemma), ensuring that the result is a valid word. It’s generally more accurate but also computationally heavier.

Text summarization can be categorized into two primary types: • Extractive Summarization: This method selects and compiles the most relevant sentences or phrases directly from the original text. It does not generate new content but instead identifies and presents key existing portions. Abstractive Summarization: This approach goes beyond simply copying sentences. It understands the main idea of the content and rephrases it in new words, generating a concise and coherent summary that conveys the original meaning.

A corpus refers to a large, structured collection of text data used in NLP tasks. It serves as a foundational resource for developing language models, creating dictionaries, performing linguistic analysis, or training and evaluating NLP algorithms.

Binarization is the process of transforming data features into binary format (0s and 1s). This simplifies data and helps classification algorithms perform better. It’s especially useful in applications like shape or character recognition, where converting images or patterns into binary form can help isolate relevant objects from their backgrounds.

Advantages of decision trees: • Simple to understand and easy to interpret. • Require little data preprocessing. • Capable of handling both numerical and categorical data. Disadvantages: • Prone to overfitting, especially with complex datasets. • Can be unstable—small changes in data may lead to different trees. May struggle with capturing linear relationships compared to other algorithms.

In artificial intelligence, perception is the interpretation of sensory input by machines. The key types include: • Visual Perception: Enables tasks like face recognition, video analysis, 3D modeling, and medical image interpretation. • Auditory Perception: Involves processing sound for applications like voice recognition, speech synthesis, and smart assistant functionality. Tactile Perception: Allows machines to sense and respond to physical touch or pressure, enhancing interaction with physical environments (e.g., robotics).