Apple To Integrate Google’s Gemini AI Into iPhones and Samsung Galaxy Z Fold 6, Z Flip 6,

 

Scroll down to the bottom of the page to download the Only Question  PDF

GTU MOST IMP QUESTIONS FOR  AIML

CH 1 =  CH 1 MOST IMP QUESTIONS

CH 2  = CH 2 MOST IMP QUESTIONS

CH 4 =  CH 4 MOST IMP QUESTIONS

CH 5 =  CH 5 MOST IMP QUESTIONS

1. Difference Between Biological Neurons and Artificial Neurons

Biological Neurons:

• Biological neurons are the basic building blocks of the brain and nervous system, transmitting information through electrochemical processes.
• Each neuron has:
  • Dendrites: Branch-like structures that receive signals from other neurons.
  • Soma (Cell Body): Processes incoming signals to determine if they meet a certain threshold to "fire" an output.
  • Axon: Transmits the signal to other neurons once an action potential is reached.
  • Synapses: Junctions where axons of one neuron connect to dendrites of another, transmitting signals chemically (using neurotransmitters).
• Neurons fire only if they receive enough input (a process called an action potential), allowing complex brain activities like cognition, memory, and sensory processing.

Artificial Neurons:

• Artificial neurons (or perceptrons) are computational models that mimic the functionality of biological neurons but in a simplified way.
• Each artificial neuron has:
  • Inputs: Represent the data features or signals received from other neurons.
  • Weights: Coefficients that assign importance to each input.
  • Summation Function: Aggregates the weighted inputs.
  • Activation Function: Determines whether the neuron "fires" or activates, based on the weighted sum. It introduces non-linearity to enable the network to solve complex patterns.

Key Differences:

Feature	Biological Neurons	Artificial Neurons
Structure	Dendrites, soma, axon, synapses	Inputs, weights, summation, activation function
Signal Transmission	Chemical and electrical signals	Mathematical functions
Learning Mechanism	Synaptic plasticity, allowing the brain to adapt	Adjusts weights via algorithms like backpropagation
Complexity	Incredibly complex, with trillions of connections	Less complex, with only a few layers and neurons
Purpose	Cognition, perception, sensory processing	Data analysis, classification, and pattern recognition

---

2. What is an Activation Function, and Why Use Them in Neural Networks?

An activation function in a neural network decides whether a neuron should be activated (produce an output) based on the weighted sum of inputs. It introduces non-linearity to help networks learn complex patterns.

Why Use Activation Functions:

1. Non-Linearity: Most real-world problems require non-linear decision boundaries. Without activation functions, a neural network would function as a linear model, limiting its ability to learn complex relationships in data.
2. Decision Thresholding: They help control the information passing through the network, deciding when neurons should activate or remain inactive.
3. Enabling Deep Learning: Activation functions allow neural networks to have multiple layers, with each layer capturing different aspects of data complexity.

---

3. Activation Functions: ReLU, Sigmoid, and Hyperbolic Tangent (tanh)

1. ReLU (Rectified Linear Unit):

   • Definition: ReLU outputs the input if it is positive; otherwise, it outputs zero.
     • Formula: $f(x) = \max(0, x)$
   • Characteristics:
     • Only neurons with positive values "fire," creating sparsity in the network, which reduces computation.
   • Advantages: Solves the vanishing gradient problem; simple and computationally efficient.
   • Disadvantages: Has the dying ReLU problem where neurons can become inactive (output zero continuously) and stop learning.
   • Use Case: Hidden layers in Convolutional Neural Networks (CNNs) and deep networks.

2. Sigmoid Function:

   • Definition: Maps input values to a range between 0 and 1.
     • Formula: $f(x) = \frac{1}{1 + e^{-x}}$
   • Characteristics: Outputs between 0 and 1, suitable for binary outputs and probability-based interpretations.
   • Advantages: Great for probability-based tasks (e.g., binary classification).
   • Disadvantages: Causes vanishing gradient issues for large values, slowing down learning in deep networks.
   • Use Case: Often used in output layers for binary classification tasks.

3. Hyperbolic Tangent (tanh):

   • Definition: Maps input values to a range between -1 and 1.
     • Formula: $f(x) = \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$
   • Characteristics: Similar to sigmoid but centers output around zero, making it useful for networks where negative and positive outputs are significant.
   • Advantages: Reduces the risk of bias towards positive outputs; suitable for hidden layers.
   • Disadvantages: Prone to vanishing gradient problems for large values.
   • Use Case: Useful in hidden layers for RNNs and networks needing balanced output around zero.

---

4. Single-Layer Feed-Forward Network

A single-layer feed-forward network has only one layer of neurons, with each neuron in this layer connected directly to an output layer.

Working Mechanism:

• Each input is multiplied by a weight and summed up.
• The weighted sum is then passed through an activation function, producing the final output.

Application of Single-Layer Feed-Forward Networks:

• Simple Classification and Regression Tasks: Like predicting binary outcomes or simple linear regression models.

Advantages:

1. Simplicity: Easy to understand and implement with minimal computational resources.
2. Low Training Time: Fewer parameters make training faster.

Disadvantages:

1. Limited Complexity: Cannot capture non-linear patterns, making it inadequate for real-world applications that require complex pattern recognition.
2. Underfitting Risk: Often underfits data due to limited learning capacity.

---

5. Multi-Layer Feed-Forward Network

A multi-layer feed-forward network has multiple hidden layers between the input and output layers, allowing it to capture more complex data relationships.

Working Mechanism:

• Data flows forward from input through hidden layers to the output layer, with each layer capturing progressively more abstract features.
• Layers in the network can capture increasingly complex patterns, making it suitable for applications requiring deeper understanding.

Applications: Image and speech recognition, natural language processing, and complex prediction models.

Advantages:

1. Non-Linear Learning: Can model complex, non-linear patterns and dependencies.
2. Improved Accuracy: Able to achieve better performance on intricate tasks.

Disadvantages:

1. Computationally Intensive: More layers mean more parameters and higher computational demands.
2. Risk of Overfitting: Overfitting can occur, especially in deep networks without regularization.

---

6. Recurrent Networks (RNNs)

Definition: RNNs are networks with connections that create cycles, allowing information from previous inputs to persist in memory, which is essential for sequence prediction.

Applications: Time-series forecasting, language modeling, machine translation, and speech recognition.

Advantages:

1. Sequential Learning: Can capture dependencies across sequences, making it suitable for time-dependent tasks.
2. Memory Retention: Retains information over short-term dependencies.

Disadvantages:

1. Vanishing/Exploding Gradient Problem: When handling long sequences, RNNs may face vanishing or exploding gradient issues, making it difficult to learn dependencies over extended periods.
2. Training Complexity: Longer training time due to recurrent connections.

---

7. Learning Process in ANN

The learning process in ANNs involves the following steps:

1. Initialization: Assign initial random values to the weights.
2. Forward Propagation: Pass input data through the network layers, calculating weighted sums and applying activation functions.
3. Loss Calculation: Compute the difference between predicted outputs and actual target values.
4. Backpropagation and Optimization: Adjust weights using the error gradients to minimize the loss function, allowing the network to improve over iterations.

---

8. Backpropagation

Definition: Backpropagation is an optimization algorithm that updates the weights of each neuron by propagating errors backward through the network. It’s essential for training deep networks, allowing each layer to adjust its weights to minimize the overall error.

Key Components:

• Gradient Calculation: Finds the gradient of the loss function with respect to each weight.
• Weight Adjustment: Weights are updated by moving in the opposite direction of the gradient to minimize the loss function.

---

9. Architecture of Recurrent Neural Network (RNN)

In an RNN:

• Inputs are processed sequentially, with each step remembering the previous inputs.
• Hidden State maintains memory across sequences, capturing dependencies across time.
• Output Layer gives the final prediction based on current input and hidden state.

10. Comparison Between ANN and BNN

Feature	Artificial Neural Network (ANN)	Biological Neural Network (BNN)
Structure	Simplified neuron model with weighted inputs	Complex network with synaptic connections
Learning	Backpropagation for weight adjustment	Synaptic plasticity for adaptive learning
Signal	Mathematical values	Electrochemical signals
Purpose	Solving computational and data processing tasks	Supports cognition, perception, and memory

---

11. Information Flow Through the Network

Data flows through an ANN in layers. Each layer processes inputs by calculating weighted sums, applying an activation function, and passing outputs to the next layer, ultimately reaching the output layer for the final prediction.

---

12. Components of Neurons

In a neural network, a neuron has:

• Inputs (Features): Data values received from previous neurons.
• Weights: Assigned importance of each input in contributing to the output.
• Summation Function: Adds the weighted inputs to create a net input.
• Activation Function: Determines if the neuron should activate or remain inactive, based on the net input.

ONLY QUESTIONS PDF  

👇