Exploring AI Concepts

What is the primary goal of machine learning?

• To store data without analysis.

• To create static algorithms that never change.

• To replace human intelligence entirely.

• To enable computers to learn from data and make predictions or decisions.

Define supervised learning and give an example.

• Image recognition for identifying objects

• Clustering customer data into groups

• Reinforcement learning for game strategies

• An example of supervised learning is email classification, where emails are labeled as 'spam' or 'not spam'.

What is overfitting in machine learning?

• Overfitting happens when a model is trained on too much data, leading to confusion.

• Overfitting is when a model performs well on training data but poorly on new data due to excessive complexity.

• Overfitting occurs when a model is too simple and cannot capture the underlying patterns.

• Overfitting is when a model performs poorly on both training and new data due to lack of data.

Explain the concept of tokenization in NLP.

• Tokenization is the method of translating text into different languages.

• Tokenization refers to the analysis of the grammatical structure of sentences.

• Tokenization is the process of breaking down text into smaller units called tokens.

• Tokenization is the process of summarizing text into a single sentence.

What is the purpose of a confusion matrix?

• To calculate the mean of a dataset

• The purpose of a confusion matrix is to evaluate the performance of a classification model.

• To visualize the distribution of data points

• To determine the correlation between features

Describe the role of convolutional layers in computer vision.

• Convolutional layers extract and learn features from images, enabling effective analysis and understanding in computer vision tasks.

• Convolutional layers only enhance image brightness and contrast.

• Convolutional layers are used for data storage in computer vision.

• Convolutional layers are primarily responsible for image compression.

What is the difference between classification and regression?

• Classification requires more data than regression.

• Classification is used for time series; regression is for image analysis.

• Classification predicts numerical values; regression predicts categories.

• Classification predicts categories; regression predicts continuous values.

What is sentiment analysis in natural language processing?

• Sentiment analysis is the process of identifying grammatical errors in text.

• Sentiment analysis is a method for translating text into different languages.

• Sentiment analysis is the process of determining the emotional tone behind a body of text in natural language processing.

• Sentiment analysis involves summarizing the main ideas of a text.

Name a common dataset used for image classification tasks.

• CIFAR-10

• MNIST

• Fashion-MNIST

• ImageNet

What is transfer learning and how is it used in AI?

• Transfer learning is only applicable to image recognition tasks.

• Transfer learning is a method for training models from scratch.

• Transfer learning involves using multiple models simultaneously for a single task.

• Transfer learning is a technique in AI where a pre-trained model is adapted for a new, related task.