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Understanding Backpropagation in Neural Networks
Backpropagation in Neural Networks Quiz
Question 1
What is the main purpose of backpropagation in a neural network?
Initializing weights
Updating weights using error gradients
Data preprocessing
Model prediction
Question 2
What type of algorithm is backpropagation?
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Clustering
Question 3
What is the role of the loss function in backpropagation?
Predict the output
Calculate the error
Update weights
Initialize weights
Question 4
Which TensorFlow function is used to compute gradients automatically?
tf.gradients()
tf.reduce_sum()
tf.Variable()
tf.random()
Question 5
What is the main difference between feed-forward and backpropagation networks?
Feed-forward networks are unsupervised
Feed-forward uses gradient descent
Backpropagation updates weights, feed-forward does not
Both perform weight updates
Question 6
What happens if the learning rate is too high in backpropagation?
Model learns faster
Model converges smoothly
Model improves accuracy
Model may overshoot optimal weights
Question 7
How does TensorFlow calculate gradients during backpropagation?
Numerical Differentiation
Automatic Differentiation
Random Sampling
Linear Regression
Question 8
Which of the following is a common optimizer used during backpropagation in TensorFlow?
SGD (Stochastic Gradient Descent)
KMeans
DBSCAN
PCA
There are 8 questions to complete.
