RTU Kota B.Tech 7th Semester Deep Learning and Applications Question Paper 2025 (CSE/AI)

RTU Deep Learning and its Applications 7th Semester 2025 Paper Review

The Deep Learning and its Applications course in the 7th semester at Rajasthan Technical University (RTU) represents the advanced frontier of the Artificial Intelligence curriculum. For CSE and AI students, the 2025 course focused on moving beyond standard neural networks to complex architectures capable of processing unstructured data like images, text, and sequences. Succeeding in this exam requires a deep understanding of gradient-based optimization, specialized network architectures, and the mathematical principles that prevent overfitting and vanishing gradients.

The 2025 question paper emphasized the practical application of deep learning models in real-world scenarios, such as computer vision and natural language processing. Examiners expected students to demonstrate proficiency in architecting models and diagnosing training behaviors.

Understanding the Exam Pattern

The RTU theory examination for this 7th-semester core subject is a three-hour paper worth 100 marks, organized into three parts:

• Part I (20 Marks): Ten compulsory questions, two marks each. These test foundational definitions. Expect questions on the "Vanishing Gradient Problem," the role of different activation functions (ReLU vs. Sigmoid), basic CNN layers, and the difference between supervised and self-supervised learning. Keep answers concise.
• Part II (48 Marks): Twelve questions provided; you must answer eight. Each is worth six marks. These are analytical. Prepare to explain the architecture of a CNN, the difference between RNNs and LSTMs, the mechanics of dropout layers, and how batch normalization improves model training.
• Part III (32 Marks): Four questions provided; you must answer two. Each is worth sixteen marks. These require detailed technical explanations or design-oriented answers. You may encounter questions on designing a GAN (Generative Adversarial Network), detailing the transformer architecture, explaining transfer learning, or describing how to fine-tune pre-trained models.

Core Topics Evaluated in the 2025 Curriculum

Focus your study time on these specific modules to maximize your score:

Deep Neural Network Foundations

Master the core concepts of optimization: Stochastic Gradient Descent (SGD), Adam optimizer, and RMSprop. You must be able to explain how weight initialization strategies and regularization techniques (L1/L2, Dropout, Early Stopping) prevent overfitting.

Convolutional Neural Networks (CNNs)

This is a high-yield area. Understand the function of each layer: Convolutional (feature extraction), Pooling (dimensionality reduction), and Fully Connected (classification). You should be able to calculate output dimensions for given input sizes, kernels, and strides.

Recurrent Neural Networks (RNNs) and Sequence Models

Learn why standard RNNs fail with long-term dependencies. Study the gated architecture of LSTMs and GRUs—be prepared to explain the purpose of the input, forget, and output gates.

Modern Architectures and Generative Models

This is crucial for 7th-semester depth. Understand:

• Transformers: The attention mechanism and how it differs from sequence-based processing.
• GANs: The adversarial training process between the Generator and the Discriminator.
• Transfer Learning: The strategic use of pre-trained models (e.g., ResNet, VGG, BERT) and how to adapt them to new, smaller datasets.

Answer Writing Strategy for High Marks

RTU evaluators prioritize mathematical precision and clean diagrams:

• Diagrams: Deep learning is highly visual. Draw architectures (like a CNN feature map or an LSTM cell) clearly. Label all dimensions and connections. A ruler is essential.
• Formatting: Use headings and bullet points. For Part III, start with a formal definition, follow with a well-labeled architecture diagram, and provide a practical use case.
• Precision: If the question involves an algorithm, write down the update rules or the mathematical function clearly. For example, explicitly write out the attention mechanism formula:
• $$Attention(Q, K, V) = softmax\left(\frac{QK^T}{\sqrt{d_k}}\right)V$$
• Comparative Tables: Whenever the paper asks to compare technologies—like "RNNs vs. LSTMs" or "CNNs vs. Transformers"—always use a table to delineate differences in complexity, data requirements, and use cases.

Time Management During the Exam

• Part I (20 minutes): Finish these first to secure foundation marks. Aim for one point per minute.
• Part II (70 minutes): Allocate roughly 8-9 minutes per question. If a question requires a small derivation or architecture sketch, prioritize it to earn maximum points.
• Part III (90 minutes): Dedicate 45 minutes to each of the two major questions. Use this time to write out detailed architectural flows and explain the rationale behind your model design choices.