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Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is the main purpose of forward propagation in a neural network?
π‘ Hint: Think about how inputs are processed.
Question 2
Easy
What operation is commonly used for computing the weighted sum in forward propagation?
π‘ Hint: Consider how we handle multiple inputs and weights.
Practice 4 more questions and get performance evaluation
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What is the primary role of forward propagation?
π‘ Hint: Think about what happens during the forward pass.
Question 2
True or False: Activation functions are not necessary for neural networks.
π‘ Hint: Think about the limitations of linear models.
Solve 2 more questions and get performance evaluation
Push your limits with challenges.
Question 1
Consider a neural network with two input nodes feeding into one hidden layer with three neurons. The input values are [1, 2] and the corresponding weights for each connection from input to hidden layer are given as [[0.1, 0.2], [0.3, 0.4]]. Compute the output of each hidden neuron if a ReLU activation function is used.
π‘ Hint: Calculate the weighted sums first, then apply the ReLU function.
Question 2
Given a batch of inputs [2, -1, 0], weights [[0.5, -0.2, 0.1], [-0.6, 0.4, 0.2]], and biases [0.3, 0.1], calculate the pre-activation output for each neuron in the layer after forward propagation.
π‘ Hint: Remember to add the corresponding bias after computing the weighted sums.
Challenge and get performance evaluation