In machine learning, which of the following inputs is required for model training and prediction?
In machine learning, historical data is crucial for model training and prediction. The model learns from this data, identifying patterns and relationships between features and target variables. While the training algorithm is necessary for defining how the model learns, the input required for the model is historical data, as it serves as the foundation for training the model to make future predictions.
Neural networks and training algorithms are parts of the model development process, but they are not the actual input for model training.
Which of the following are subfields of AI?
Artificial intelligence is a broad field that encompasses several subfields. Two key subfields are:
Expert systems, which are computer programs that mimic the decision-making abilities of a human expert by reasoning through bodies of knowledge. These systems are used in various domains such as healthcare, engineering, and finance.
Computer vision, which enables machines to interpret and understand visual data from the world. It includes tasks such as object detection, image recognition, and video analysis.
While options like backpropagation and smart finance are related to AI, they represent specific algorithms or application areas rather than broad subfields.
Which of the following statements about datasets are true?
In machine learning:
The testing set is a dataset used after training to evaluate the model's performance and generalization ability. Each sample in this set is called a test sample.
A dataset generally has multiple dimensions, with each dimension representing a feature or attribute of the data.
A typical machine learning process divides the data into a training set (to train the model), a validation set (to tune hyperparameters and avoid overfitting), and a test set (to evaluate the model's final performance).
The statement that the validation set and test set are the same is false because they serve different purposes: validation is for hyperparameter tuning, while testing is for final model evaluation.
"AI application fields include only computer vision and speech processing." Which of the following is true about this statement?
AI is not limited to just computer vision and speech processing. In addition to these fields, AI encompasses other important areas such as natural language processing (NLP), robotics, smart finance, autonomous driving, and more. Natural language processing focuses on understanding and generating human language, while other fields apply AI to various industries and applications such as healthcare, finance, and manufacturing. AI is a broad field with numerous application areas.
The derivative of the Rectified Linear Unit (ReLU) activation function in the positive interval is always:
The Rectified Linear Unit (ReLU) activation function is defined as f(x)=max(0,x)f(x) = \max(0, x)f(x)=max(0,x). In the positive interval, where x>0x > 0x>0, the derivative of ReLU is always 1. This makes ReLU popular for deep learning networks because it helps avoid the vanishing gradient problem during backpropagation, ensuring efficient gradient flow.
