Deep Learning & Reinforcement Learning BAI701

Module 1 Overview

Introduction to Deep Learning: Introduction, Shallow Learning, Deep Learning, Why to use Deep Learning, How Deep Learning Works, Deep Learning Challenges, How Learning Differs from Pure Optimization, Challenges in Neural Network Optimization.

Textbook 1: Ch 1.1 - 1.6, Textbook 2: 8.1,8.2

Module 2 Overview

Basics of Supervised Deep Learning: Introduction, Convolution Neural Network, Evolution of Convolution Neural Network, Architecture of CNN, Convolution Operation

Textbook 1: Ch 2.1 - 2.5

Module 3 Overview

Training Supervised Deep Learning Networks: Training Convolution Neural Networks, Gradient Descent-Based Optimization Techniques, Challenges in Training Deep Networks.

Supervised Deep Learning Architectures: LetNet-5, AlexNet

Text Book - 1 : Ch 3.2,3.4,3.5, Ch 4.2,4.3

Module 4 Overview

Recurrent and Recursive Neural Networks: Unfolding Computational Graphs, Recurrent Neural Network, Bidirectional RNNs, Deep Recurrent Networks, Recursive Neural Networks, The Long Short-Term Memory. Gated RNNs.

Text Book - 2: 10.1-10.3, 10.5, 10.6, 10.10

Module 5 Overview

Deep Reinforcement Learning: Introduction, Stateless Algorithms: Multi-Armed Bandits, The Basic Framework of Reinforcement Learning, case studies.

Textbook - 3: Chapter 9: 9.1,9.2,9.3, 9.7

Deep Learning & Reinforcement Learning BAI701 is a VTU course covered through module-wise syllabus, notes, and PYQ-driven exam practice available on this page.

Credits for BAI701: 04.

Yes. You can access organized notes, PDFs, and PYQ material from the file explorer/resources section on this page.

Start with module summaries, solve recent PYQs unit-wise, and finish with complete paper practice under time constraints for SEE readiness.

Yes, this page is maintained with current scheme-oriented materials and practical exam-focused resource curation.

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