IT ENGLISH: Research Project Topics - Deep Learning

What is deep learning?

• A subset of machine learning that focuses on training artificial neural networks with multiple layers

• A subset of machine learning that focuses on training artificial neural networks with single layers

• A subset of machine learning that focuses on training decision trees

• A subset of machine learning that focuses on training support vector machines

What is the purpose of deep learning?

• To recognize patterns and make predictions from large amounts of data

• To recognize patterns and make predictions from small amounts of data

• To recognize patterns and make predictions from any amount of data

• To recognize patterns and make predictions without any data

Which field has deep learning revolutionized?

• Computer vision

• Data analysis

• Robotics

• Genetics

What is one of the applications of deep learning in natural language processing?

• Language translation

• Image recognition

• Speech synthesis

• Data visualization

What is one of the applications of deep learning in computer vision?

• Object detection

• Speech recognition

• Sentiment analysis

• Stock market prediction

What is one of the applications of deep learning in speech recognition?

• Voice assistants

• Weather forecasting

• Social media analysis

• Customer segmentation

What are the components of artificial neural networks?

• Input layer, hidden layers, output layer

• Input layer, output layer

• Hidden layers, output layer

• Input layer, hidden layers

What do activation functions introduce to the model?

• Non-linearity

• Linearity

• Randomness

• Determinism

What are the challenges in deep learning?

• Lack of labeled data and computational resources

• Excessive labeled data and computational resources

• Lack of unlabeled data and computational resources

• Excessive unlabeled data and computational resources

What is one of the best practices in deep learning?

• Staying up-to-date with the latest research and advancements

• Ignoring the latest research and advancements

• Using outdated models and techniques

• Not considering ethical considerations