These are answers of Deep Learning and Reinforcement Learning Week 2 Quiz
Practice: Introduction to Neural Networks
Q1. Select the method or methods that best help you find the same results as using matrix linear algebra to solve the equation θ=(XTX)-1XTy Use stochastic gradient descent Use scikit-learn to build a linear regression model Train a neural network model All the above
Answer: All the above
Q2. (True/False) Neurons can be used as logic gates True False
Answer: True
Q3. (True/False) The feed-forward computation of a neural network can be thought of as matrix calculations and activation functions. True False
Answer: True
These are answers of Deep Learning and Reinforcement Learning Week 2 Quiz
Practice: Keras Library
Q1. Building a Neural Network with the Sequential API in Keras implies that each layer can connect only to subsequent layers. after the first layer must be fully-connected. can connect to only the previous and next layers. can be connected to at most two subsequent layers.
Answer: can connect to only the previous and next layers.
Q2. An epoch in estimating a Deep Learning model refers to subsamples that make up the training data. iterations required to achieve convergence. computational complexity of the estimating procedure. the number of times the entire input data set is used by the model.
Answer: the number of times the entire input data set is used by the model.
These are answers of Deep Learning and Reinforcement Learning Week 2 Quiz
Q3. An advantage of the Sigmoid activation function over the step activation function is: the ability to generate nonlinear outcomes. sharper changes as the loss becomes positive. improved backpropagation due to nonzero gradients. significantly different gradients for very high values.
Answer: improved backpropagation due to nonzero gradients.
These are answers of Deep Learning and Reinforcement Learning Week 2 Quiz
Final Quiz
Q1. The backpropagation algorithm updates which of the following? The losses only. The activations only. The parameters and activations. The parameters only.
Answer: The parameters only.
Q2. What of the following about the activation functions is true? They add non-linearity into the model, allowing the model to learn complex pattern. They tell us about how computationally expensive a neural network is. They evaluate how well the model has performed on the training data. They are optimization algorithms that update values of the model parameters.
Answer: They add non-linearity into the model, allowing the model to learn complex pattern.
These are answers of Deep Learning and Reinforcement Learning Week 2 Quiz
Q3. What is true regarding the backpropagation rule? The actual output is determined by computing the output of neurons in each hidden layer It prevents overfitting It can be used to update the hyperparameters of a neural network It is a feed forward neural network.
Answer: The actual output is determined by computing the output of neurons in each hidden layer
Q4. Which option correctly lists the steps to build a linear regression model using Keras? 1. Use `fit()` and specify the number of epochs to train the model for. 2. Create a Sequential model with the relevant layers. 3. Normalize the features with ` layers.Normalization()` and apply `adapt()`. 4. Compile using `model.compile()` with specified optimizer and loss. 3, 2, 4, 1 3, 2, 1, 4 3, 1, 2, 4 2, 4, 3, 1
