Q: How do neural networks learn from data? Please describe the main process or methodology used?

A: Neural networks learn from data through a process called “training” which involves using algorithms such as backpropagation. During training, the network adjusts its weights in response to the errors it makes in order to improve its predictions over time.

Q: What is the primary difference between supervised and unsupervised learning in AI?

A: In supervised learning, the AI is trained on a labeled dataset, which means each training example is paired with an output label. In unsupervised learning, the AI is given data without explicit instructions on what to do with it, and it tries to find patterns and relationships within the data.

Q: Can you explain what “overfitting” is in the context of machine learning and why it is a problem?

A: Overfitting occurs when a machine learning model performs very well on the training data but poorly on new, unseen data. This happens because the model has learned the details and noise in the training data to the extent that it negatively impacts the model’s performance on new data. It essentially “memorizes” the training data rather than generalizing from it.

Q: What is the purpose of a validation set in machine learning?

A: The validation set is a separate subset of data from the training set, used to evaluate the model’s performance during the training process. It helps in tuning the model’s hyperparameters and preventing overfitting by providing an unbiased evaluation of the model on unseen data before testing it on the final test set

Q: What is “natural language processing” (NLP) in AI?

A: Natural Language Processing (NLP) is a field of AI that focuses on the interaction between computers and humans through natural language. An example of NLP is using a virtual assistant like Siri or Alexa.

Q: What is a “convolutional neural network” (CNN) and in which type of tasks is it typically used?

A: A Convolutional Neural Network (CNN) is a class of deep learning algorithm particularly well-suited for analyzing visual data. CNNs are commonly used in image and video recognition

Q: Explain what reinforcement learning is

A: Reinforcement learning involves training algorithms by rewarding them for desired behaviors and punishing them for undesired ones. A practical example of reinforcement learning is training a robot to navigate a maze. The robot receives positive rewards for actions that bring it closer to the exit and vice vera.



Comments are closed