Learn what is true for neural networks with a detailed explanation of their components and functionality. Prepare effectively for your CNN certification exam with this concise guide.
Question
Select the correct option, which is true for neural networks?
A. it has set of nodes and connections
B. each node computes it’s weighted input
C. node could be in excited state or non-excited state
D. all of the mentioned
Answer
D. all of the mentioned
Explanation
Neural networks are computational models inspired by the structure and functioning of the human brain. They consist of interconnected nodes (neurons) organized in layers, which process and transmit information. Let’s break down why each option in the question is correct:
Option A: It has a set of nodes and connections
Neural networks are composed of nodes (neurons) connected by edges. These connections have associated weights that determine the strength of the signal between nodes. This structure mimics the neural pathways in the human brain, where neurons communicate through synapses.
Option B: Each node computes its weighted input
Each neuron in a neural network receives inputs, multiplies them by their respective weights, adds a bias term, and processes the result through an activation function. This computation allows neurons to decide whether to “fire” or remain inactive based on the input’s weighted sum.
Option C: Node could be in an excited state or non-excited state
Nodes (neurons) in neural networks can be “activated” or remain inactive based on their activation function’s output. For example:
- If using a ReLU activation function, the neuron outputs 0 if the input is negative (non-excited state) or passes the input as it is if positive (excited state).
- Similarly, Sigmoid activation outputs values between 0 and 1, representing varying degrees of excitation.
Option D: All of the mentioned
Since all the above statements are true, this is the correct answer.
Key Components of Neural Networks
- Nodes/Neurons: The fundamental units that process inputs and produce outputs.
- Connections: Links between neurons with adjustable weights.
- Activation Functions: Decide whether a node should activate based on its input.
- Layers: Organized into input, hidden, and output layers to process data hierarchically.
- Weights and Biases: Parameters adjusted during training to minimize prediction errors.
Real-World Applications
Neural networks power applications like image recognition (via CNNs), natural language processing (via RNNs), fraud detection, medical diagnosis, and more. Their ability to model complex patterns makes them indispensable in modern artificial intelligence.
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