✅ Top AI Interview Questions with Answers: Part-2 🧠

11. What is a perceptron?
A perceptron is the simplest type of neural network unit. It takes inputs, multiplies them with weights, adds a bias, and passes the result through an activation function to produce output. It’s the building block of neural networks.
Formula: output = activation(w₁x₁ + w₂x₂ + ... + b)

12. What is gradient descent?
Gradient Descent is an optimization algorithm used to minimize the loss function in machine learning models. It updates model weights iteratively in the opposite direction of the gradient to reduce prediction error.
- Variants: Batch, Stochastic, Mini-batch
- Learning rate controls step size.

13. Explain backpropagation
Backpropagation is the algorithm used in training neural networks. It calculates the gradient of the loss function with respect to each weight by applying the chain rule, then updates weights using gradient descent.
It works in two passes:
1. Forward pass (prediction)
2. Backward pass (error correction)

14. What is a Convolutional Neural Network (CNN)? 📸
CNNs are deep learning models specifically designed for image and spatial data.
- Use convolutional layers to detect features (edges, shapes)
- Pooling layers reduce dimensions
- Fully connected layers make predictions
Used in: face recognition, image classification, object detection.

15. What is a Recurrent Neural Network (RNN)? 💬
RNNs are neural networks designed for sequential data like time series or text.
- They use memory (hidden state) to store previous inputs.
- Struggle with long-term dependencies
Variants like LSTM and GRU solve this issue.

16. What is transfer learning? 🔄
Transfer learning involves reusing a pre-trained model on a new but similar task.
Example: Use a model trained on ImageNet and fine-tune it for medical image classification.
Saves time and resources, especially with limited data.

17. Difference between parametric and non-parametric models
- Parametric models: Assume a fixed number of parameters (e.g., Linear Regression).
- Non-parametric models: Don't assume a specific form and grow with more data (e.g., KNN, Decision Trees).
Non-parametric = more flexible but needs more data.

18. What are the different types of AI (ANI, AGI, ASI)?
- ANI (Narrow AI): Performs one task (e.g., Siri, ChatGPT)
- AGI (General AI): Human-like reasoning across domains (still theoretical)
- ASI (Super AI): Exceeds human intelligence (future concept)

19. What is reinforcement learning? 🎮
Reinforcement Learning (RL) is a type of learning where an agent interacts with an environment and learns to make decisions by receiving rewards or penalties.
Used in: Game playing (Chess, Go), robotics, autonomous driving.

20. Explain Markov Decision Process (MDP)
MDP provides a mathematical framework for modeling RL problems.
It includes:
- States
- Actions
- Transition probabilities
- Rewards
The agent learns an optimal policy (what action to take in each state).

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