Understanding Machine Learning and Deep Learning

What enables machines to make decisions based on past data?

• Statistical analysis

• Predictive modeling

• Data mining

• Machine learning

How does deep learning differ from traditional machine learning?

• Deep learning requires more manual feature engineering than traditional machine learning.

• Traditional machine learning uses deep neural networks for automatic feature extraction.

• Deep learning uses deep neural networks for automatic feature extraction, while traditional machine learning relies on manual feature engineering and simpler models.

• Deep learning is only applicable to structured data, while traditional machine learning can handle unstructured data.

What is required for training a machine learning model?

• A dataset, algorithm, computational resources, and evaluation method.

• Just a theoretical model without any data.

• Only a programming language and a user interface.

• A single data point and a random guess.

Does deep learning require manual feature identification?

• Manual feature identification is essential for deep learning success.

• Deep learning only works with pre-defined features.

• Yes, deep learning requires manual feature identification.

• No, deep learning does not require manual feature identification.

What approach does deep learning take to learn features?

• Deep learning learns features through hierarchical representation using neural networks.

• Deep learning learns features through linear regression models.

• Deep learning relies on manual feature extraction techniques.

• Deep learning uses decision trees to learn features.

How is a problem typically solved in traditional machine learning?

• A problem is solved by randomly guessing the solution.

• A problem is solved by training multiple models simultaneously without evaluation.

• A problem is solved by defining it, collecting data, preprocessing, selecting an algorithm, training the model, evaluating it, and testing on unseen data.

• A problem is solved by only collecting data without any preprocessing.

What is a characteristic of testing in deep learning systems?

• Focus on model complexity over performance

• Evaluation on unseen data for generalization.

• Ignoring overfitting during evaluation

• Testing only on training data

Why might testing take longer in traditional machine learning?

• Testing requires less data in traditional machine learning.

• Testing takes longer due to extensive preprocessing, feature selection, and model tuning.

• Testing is faster due to automated feature extraction.

• Testing is simplified with fewer model parameters.

What is a key advantage of deep learning regarding feature extraction?

• It is less effective than traditional methods.

• It requires manual feature extraction.

• It limits the number of features used.

• It automates feature extraction.

How does the reasoning behind decisions differ in deep learning?

• Deep learning relies solely on human intuition for decisions.

• Deep learning uses fixed algorithms for decision-making.

• Traditional reasoning is based on learned patterns from data.

• Deep learning reasoning is based on learned patterns from data, while traditional reasoning relies on explicit rules.