Exploring Artificial Intelligence Concepts

What is the primary goal of machine learning?

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

• To replace human intelligence entirely.

• To store data in a more efficient way.

• To automate all computer programming tasks.

Define supervised learning and provide an example.

• Supervised learning is only applicable to numerical data.

• Supervised learning is a type of machine learning where a model is trained on labeled data. An example is training a model to classify images of cats and dogs based on labeled examples.

• Unsupervised learning involves training on unlabelled data.

• An example of supervised learning is clustering customer data without labels.

What is overfitting in machine learning?

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

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

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

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

Explain the concept of a neural network.

• A neural network is a type of biological neuron found in the brain.

• A neural network is a simple algorithm that does not require data to learn.

• A neural network is a hardware device used for data storage.

• A neural network is a computational model that simulates the way human brains process information, consisting of layers of interconnected nodes that learn to map inputs to outputs.

What are the main components of a neural network?

• Convolutional layers, pooling layers, dropout layers

• Data preprocessing, model evaluation, training set

• Input nodes, output nodes, feedback loops

• Input layer, hidden layers, output layer, weights, biases, activation functions.

Describe the function of an activation function.

• To calculate the loss function during training.

• The function of an activation function is to introduce non-linearity and determine the output of a neuron in a neural network.

• To store the weights of a neural network.

• To linearize the output of a neuron.

What is natural language processing (NLP)?

• Natural Language Processing (NLP) is a branch of artificial intelligence that enables computers to understand, interpret, and respond to human language.

• A system for managing databases.

• A method for teaching computers to write poetry.

• A technique for improving computer graphics.

Give an example of a task that NLP can perform.

• Translating spoken language.

• Image recognition of photos.

• Generating random text.

• Sentiment analysis of text.

What is the purpose of tokenization in NLP?

• To translate text from one language to another.

• To convert text into numerical values for machine learning models.

• To summarize text by extracting key phrases.

• The purpose of tokenization in NLP is to break down text into smaller units for easier analysis and processing.

What are convolutional neural networks (CNNs) used for?

• Time series forecasting

• Speech recognition

• CNNs are used for image classification, object detection, and image segmentation.

• Text generation