BCS702 · Notes
Parallel Computing
BCS702 Parallel Computing Complete Notes PDF | Modules 1–5 | VTU 7th Sem 2022 Scheme | FREE Download
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Previous Year Question Paper2022 SchemePreview before buying
Branch
CSE
Semester
7
Subject
Deep Learning
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What's inside: Complete solved previous year question paper for BCS714A – Deep Learning, VTU 2022 Scheme, 7th Semester.
Exam: December 2025 / January 2026
This solved paper contains complete exam-oriented answers for all questions from Modules 1 to 5, including OR questions.
Topics Covered:
Module 1:
Biological and Machine Vision, Hubel and Wiesel Experiment, LeNet-5, Traditional Learning vs Deep Learning, One-Hot Representation, AlexNet and Word Vectors.
Module 2:
Regularization for Deep Learning, L1 and L2 Regularization, Ridge Regression, Data Augmentation, Batch and Minibatch Gradient Descent, Momentum and Adam Optimization.
Module 3:
Convolution, Cross-Correlation, Shared and Unshared Convolution, Tiled Convolution, ReLU and Max Pooling.
Module 4:
Recurrent Neural Networks, Unfolding Computational Graphs, Bidirectional RNN, Encoder-Decoder Architecture, Deep Recurrent Networks and Recursive Neural Networks.
Module 5:
Natural Language Preprocessing, Stop-Word Removal, Stemming, Word-Vector Visualization, Confusion Matrix and ROC-AUC.
Features:
• All questions answered
• OR questions also solved
• Simple English
• Point-wise exam-oriented answers
• Necessary diagrams
• Step-by-step numerical solutions
• Important formulas
• Easy to understand and write in VTU examination
• Suitable for quick revision before SEE
Prepared by Search Creators.
Exam: December 2025 / January 2026
This solved paper contains complete exam-oriented answers for all questions from Modules 1 to 5, including OR questions.
Topics Covered:
Module 1:
Biological and Machine Vision, Hubel and Wiesel Experiment, LeNet-5, Traditional Learning vs Deep Learning, One-Hot Representation, AlexNet and Word Vectors.
Module 2:
Regularization for Deep Learning, L1 and L2 Regularization, Ridge Regression, Data Augmentation, Batch and Minibatch Gradient Descent, Momentum and Adam Optimization.
Module 3:
Convolution, Cross-Correlation, Shared and Unshared Convolution, Tiled Convolution, ReLU and Max Pooling.
Module 4:
Recurrent Neural Networks, Unfolding Computational Graphs, Bidirectional RNN, Encoder-Decoder Architecture, Deep Recurrent Networks and Recursive Neural Networks.
Module 5:
Natural Language Preprocessing, Stop-Word Removal, Stemming, Word-Vector Visualization, Confusion Matrix and ROC-AUC.
Features:
• All questions answered
• OR questions also solved
• Simple English
• Point-wise exam-oriented answers
• Necessary diagrams
• Step-by-step numerical solutions
• Important formulas
• Easy to understand and write in VTU examination
• Suitable for quick revision before SEE
Prepared by Search Creators.
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