Artificial Intelligence

What is a neural network?

• A neural network is a type of computer hardware used for data storage.

• A neural network is a type of plant species found in tropical regions.

• A neural network is a type of weather pattern observed in the Arctic.

• A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.

Explain the concept of backpropagation in machine learning.

• Backpropagation is only applicable to shallow neural networks.

• Backpropagation is a method used in training artificial neural networks by calculating the gradient of the loss function with respect to the weights of the network.

• Backpropagation involves updating the weights of the neural network based on the output error.

• Backpropagation is a method used in unsupervised learning algorithms.

How is computer vision used in AI applications?

• Computer vision is used in AI applications to predict stock market trends

• Computer vision is used in AI applications to interpret and understand the visual world through digital images or videos.

• Computer vision is used in AI applications to analyze audio data

• Computer vision is used in AI applications to generate text summaries

What are the main components of a machine learning algorithm?

• weights, biases, activation functions

• algorithm, data, features, labels

• data, model, objective function, optimization algorithm, evaluation metric

• hyperparameters, loss function, training data

Describe the difference between supervised and unsupervised learning.

• Supervised learning uses labeled data, while unsupervised learning uses unlabeled data.

• Supervised learning is used for image recognition, while unsupervised learning is used for text analysis.

• Supervised learning uses unlabeled data, while unsupervised learning uses labeled data.

• Supervised learning and unsupervised learning are the same concept.

How does convolutional neural network (CNN) differ from a regular neural network?

• CNNs do not use activation functions, unlike regular neural networks.

• CNNs use convolutional layers for spatial pattern detection, while regular neural networks do not.

• CNNs use recurrent layers for spatial pattern detection, while regular neural networks do not.

• CNNs have fewer layers compared to regular neural networks.

What are some common applications of AI in the real world?

• Weather forecasting, Social media analysis, Online shopping recommendations

• Virtual assistants, recommendation systems, autonomous vehicles, fraud detection, medical diagnosis

What is the role of activation functions in neural networks?

• Activation functions are only used for visualization purposes in neural networks

• Activation functions introduce non-linearity to the neural network, allowing it to learn complex patterns and relationships in the data.

• Activation functions are not necessary in neural networks as they hinder learning

• Activation functions help reduce the complexity of neural networks by making them linear

Explain the concept of overfitting in machine learning.

• Overfitting is beneficial as it ensures the model can generalize well to unseen data.

• Overfitting happens when a model is overly complex and captures noise in the training data rather than the underlying patterns.

• Overfitting is not a common issue in machine learning models.

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

Explain the concept of transfer learning in deep learning.

• Transfer learning is a technique used to transfer data between different neural networks.

• Transfer learning involves using pre-trained models on a different task and fine-tuning them on a new task to improve performance.

• Transfer learning is not applicable in deep learning models.

• Transfer learning is used to create neural networks from scratch.