You are designing a deep learning model for a multi-class classification task with 10 classes. Which activation function and loss function combination would be the most suitable for the output layer?
- Sigmoid activation function with Mean Squared Error (MSE) loss
- Softmax activation function with Cross-Entropy loss
- ReLU activation function with Mean Absolute Error (MAE) loss
- Tanh activation function with Huber loss
For multi-class classification with 10 classes, the most suitable activation function for the output layer is Softmax, and the most suitable loss function is Cross-Entropy. Softmax provides class probabilities, and Cross-Entropy measures the dissimilarity between the predicted probabilities and the true class labels. This combination is widely used in classification tasks.
Loading...
Related Quiz
- An e-commerce platform is trying to predict the amount a user would spend in the next month based on their past purchases. Which type of learning and algorithm would be most suitable for this?
- For an organization that needs real-time data analytics with live dashboard updates, which visualization tool would be the most appropriate?
- Which technique considers the spread of data points around the median to identify outliers?
- The technique where spatial transformations are applied to input images to boost the performance and versatility of models is called _______ in computer vision.
- In transfer learning, a model trained on a large dataset is used as a starting point, and the knowledge gained is transferred to a new, _______ task.