One advanced technique used in time series forecasting with deep learning is the ________ neural network, known for its ability to remember sequences over time.
- Recurrent
- Convolutional
- Randomized
- Decision
The recurrent neural network (RNN) is well-known for its ability to remember sequences over time, making it a valuable tool for time series forecasting.
In DQNs, the target Q-values are updated less frequently than the predicted Q-values to ensure stability, using a concept known as ________ networks.
- Target
- Control
- Reactive
- Neural
In Deep Q Networks (DQNs), the target Q-values are updated less frequently to stabilize learning. This concept is known as 'target' networks, which involves having separate target Q-networks.
A retail store wants to recommend products to customers based on their purchase history. They want to find products that other customers with similar purchase histories have bought. Which algorithm is apt for this recommendation system?
- Apriori Algorithm
- Collaborative Filtering
- Linear Regression
- Principal Component Analysis (PCA)
Collaborative Filtering is ideal for recommending products based on user behavior and finding items preferred by users with similar purchase histories. It leverages user-item interaction patterns for recommendations.
Which machine learning algorithm works by recursively splitting the data set into subsets based on the value of features until it reaches a certain stopping criterion?
- Decision Trees
- K-Means Clustering
- Linear Regression
- Neural Networks
Decision Trees work by recursively splitting the dataset into subsets based on feature values. This process continues until a stopping criterion, such as the maximum depth of the tree, is met. Decision Trees are used for both classification and regression tasks.