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d its internal (free) parameters? (CLO 2.2) his relationship between supervised learning and reinforcement 1

d its internal (free) parameters? (CLO 2.2) his relationship between supervised learning and reinforcement 1

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2. A neuron 'k' receives input from other five neurons, as 2.65, 3.85, 4.2 and 0.1 The corresponding synaptic weights are -1.5, 0.2, 1.3 and 0.5. Assume the bias as -1. (CLO 1.1) Draw the computational model of neuron 'k' . Calculate the linear output of the neuron. Apply sigmoid function as activation function
Transcribed Image Text:2. A neuron 'k' receives input from other five neurons, as 2.65, 3.85, 4.2 and 0.1 The corresponding synaptic weights are -1.5, 0.2, 1.3 and 0.5. Assume the bias as -1. (CLO 1.1) Draw the computational model of neuron 'k' . Calculate the linear output of the neuron. Apply sigmoid function as activation function
4. How to assign credit assignment problem with two sub problems for a neural network's output to its internal (free) parameters? (CLO 2.2) 5. Discuss this relationship between supervised learning and reinforcement learning. (CLO 2.2)
Transcribed Image Text:4. How to assign credit assignment problem with two sub problems for a neural network's output to its internal (free) parameters? (CLO 2.2) 5. Discuss this relationship between supervised learning and reinforcement learning. (CLO 2.2)
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