(SEM VII) THEORY EXAMINATION 2022-23 DEEP LEARNING

DEEP LEARNING (KCS-078)

B.Tech SEM VII – Complete Solved Question Paper (2022–23)
                                                                                                      ⏱ Time: 3 Hours | 📊 Marks: 100

SECTION A

Attempt all questions in brief (2 × 10 = 20 marks)

(a) Applications of Machine Learning

Machine Learning is used in image recognition, speech recognition, recommendation systems, fraud detection, medical diagnosis, self-driving cars, spam filtering, and predictive analytics.

(b) Boltzmann Machine

A Boltzmann Machine is a stochastic recurrent neural network that learns probability distributions over inputs. It consists of visible and hidden units connected symmetrically and is mainly used for feature learning and optimization problems.

(c) Can deep learning models be built using only linear regression? Explain

No. Linear regression can only model linear relationships. Deep learning requires non-linear transformations to learn complex patterns. Without non-linear activation functions, deep models collapse into a single linear transformation.

(d) Different layers of Convolutional Neural Network (CNN)

CNN consists of:                                                          Input layer

Convolutional layer                                                     Activation layer (ReLU)

Pooling layer                                                               Fully connected layer

Output layer

(e) Linear models

Linear models assume a linear relationship between input features and output. Examples include linear regression and logistic regression. They are simple, fast, but limited in modeling complex data.

(f) Importance of non-linearities in neural networks

Non-linearities allow neural networks to learn complex patterns and decision boundaries. Without non-linear activation functions, neural networks cannot solve non-linear problems.

(g) Limitations of perceptron

A perceptron can only solve linearly separable problems. It cannot solve non-linear problems such as XOR and lacks hidden layers.

(h) Why use convolutions instead of fully connected layers for images?

Convolutions reduce parameters, preserve spatial information, and exploit local patterns. Fully connected layers are computationally expensive and ignore spatial structure.

(i) Importance of GPUs in deep learning

GPUs enable parallel computation, faster matrix operations, and efficient training of large deep learning models compared to CPUs.

(j) Best algorithm for face detection

Convolutional Neural Networks (CNNs) are the best for face detection. Popular models include Haar-Cascade (traditional) and modern CNN-based models like MTCNN and YOLO.

 SECTION B

Attempt any THREE (10 × 3 = 30 marks)

(a) Difference between Deep and Shallow Networks

(b) Architecture of Convolutional Neural Network

A CNN consists of stacked convolutional layers followed by activation functions, pooling layers for dimensionality reduction, fully connected layers for classification, and an output layer.
( In exam, draw neat CNN block diagram)

(c) Why CNN is preferred over ANN for image classification

CNNs preserve spatial relationships, require fewer parameters, and automatically extract features, whereas ANNs treat images as flat vectors and are inefficient for large images.

(d) LSTM (Long Short-Term Memory) and applications

LSTM is a special type of RNN designed to handle long-term dependencies using memory cells and gates (input, forget, output).

Applications:                                                                Speech recognition

Machine translation                                                       Time-series forecasting

Text generation

(e) Image Captioning in Deep Learning

Image captioning combines CNNs for feature extraction and RNN/LSTM for sequence generation. It generates natural language descriptions for images, widely used in accessibility tools.

SECTION C

Q3 (Attempt any one)

(a) Gradient Descent vs Stochastic Gradient Descent

(b) GAN (Generative Adversarial Network) and its models

GAN consists of a Generator and Discriminator competing against each other.

Types of GANs:                                                 Vanilla GAN

DCGAN                                                               CycleGAN

Conditional GAN

Q4 (Attempt any one)

(a) Semi-Supervised Learning

Semi-supervised learning uses both labeled and unlabeled data. It reduces labeling cost and improves performance when labeled data is limited.

(b) Deep Learning: history, applications, and uses

Deep learning evolved from neural networks and gained popularity due to big data and GPUs. It is used in vision, speech, NLP, healthcare, and autonomous systems.

Q5 (Attempt any one)

(a) Backpropagation algorithm

Backpropagation updates weights by computing error gradients using the chain rule.

Steps:                                                                    Forward pass

Error calculation                                                     Backward pass

Weight update

(b) Short notes

i) Deep Reinforcement Learning:
Combines deep learning with reinforcement learning to learn policies.

ii) Autoencoder Architecture:
Used for dimensionality reduction and feature learning.

iii) VGG:
Deep CNN architecture with small filters and high accuracy.

iv) SOA (State of the Art):
Refers to best-performing models at a given time.

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Q6 (Attempt any one)

(a) PCA vs RNN

(b) Batch GD vs Stochastic GD

Batch GD uses full dataset per update, while SGD updates weights per sample, making SGD faster and scalable.

Q7 (Attempt any one)

(a) Privacy issues in facial recognition

Facial recognition can lead to surveillance, misuse of personal data, identity theft, and lack of consent when used by private companies.

(b) How AI and Neuroscience drive each other

Neuroscience inspires AI architectures, while AI helps analyze brain data. Together, they advance understanding of intelligence.