5 Questions
What are the components and methods discussed in the lecture?
Recall, Optimization, Analysis of the different methods, Activation functions, Various ReLUs, Softmax, Error functions, Cross-entropy, Negative log-likelihood, Regularization, Batch normalization, Weight regularization
What is the Gradient Descent Method?
It is a method used for optimizing network parameters. It involves finding the gradient of the function and updating the parameters in the opposite direction of the gradient.
What are some advanced optimizers mentioned?
Momentum methods, Adaptive Gradient methods, ADAM
Do we have to reach the global minimum for optimal performance?
No, reaching the global minimum can lead to overfitting and loss of generalization capabilities.
What is overfitting?
Overfitting occurs when a model learns the training data too well and loses its ability to generalize to new, unseen data.
Test your knowledge on the components and methods of deep neural networks, including optimization, activation functions, error functions, and regularization. This quiz covers topics such as various ReLUs, softmax, cross-entropy, negative log-likelihood, batch normalization, and weight regularization. See how well you understand these concepts in neural networks.
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