{"id":3948,"date":"2021-07-07T12:45:32","date_gmt":"2021-07-07T12:45:32","guid":{"rendered":"https:\/\/bvcoend.ac.in\/?page_id=3948"},"modified":"2026-04-02T04:09:19","modified_gmt":"2026-04-02T04:09:19","slug":"digital-image-processing-lab","status":"publish","type":"page","link":"https:\/\/bvcoend.ac.in\/index.php\/digital-image-processing-lab\/","title":{"rendered":"Reinforcement Learning and Deep Learning Lab"},"content":{"rendered":"[vc_row][vc_column width=”1\/4″][vc_wp_custommenu title=”Imp Links” nav_menu=”73″][\/vc_column][vc_column width=”3\/4″][vc_column_text]Introduction<\/strong><\/span><\/p>\n

The Reinforcement Learning and Deep Learning (RLDL) Lab focuses on providing hands-on experience with RL and DL algorithms through practical implementations. The syllabus covers Markov Decision Processes (MDPs), Q-learning, Policy Gradient Methods, and Deep Q-Networks (DQNs). Students will learn to apply RL algorithms for decision-making in dynamic environments and use DL techniques for classification, regression, and pattern recognition. The lab emphasizes industry-relevant applications, including autonomous navigation and predictive modeling, using libraries such as TensorFlow, and PyTorch.<\/span><\/p>\n

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Course Objectives <\/strong><\/span><\/p>\n\n\n\n\n\n\n
COB-1<\/span><\/td>\nTo introduce the foundation of Reinforcement learning foundation and Q Network algorithm)<\/span><\/td>\n<\/tr>\n
COB-2<\/span><\/td>\nTo understand policy optimization ,recent advanced techniques and applications of Reinforcement learning<\/span><\/td>\n<\/tr>\n
COB-3<\/span><\/td>\nTo introduce the concept of deep learning and neural network<\/span><\/td>\n<\/tr>\n
COB-4<\/span><\/td>\nTo understand the concept of NLP and computer vision in deep learning<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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Course Outcomes <\/strong><\/span><\/p>\n\n\n\n\n\n\n\n
CO<\/strong><\/span><\/td>\nStatement<\/strong><\/span><\/td>\nBloom\u2019s Level<\/strong><\/span><\/td>\n<\/tr>\n
ML-409P.1<\/span><\/td>\nLearn how to define RL tasks and the core principals behind the RL, including policies, value functions, deriving Bellman equations and understand and work with approximate solution (deep Q Network based algorithms)<\/span><\/td>\nRemember<\/span><\/p>\n

Understand<\/span><\/td>\n<\/tr>\n

ML-409P.2<\/span><\/td>\nLearn the policy gradient methods from vanilla to more complex cases and learn application and advanced techniques in Reinforcement Learning.<\/span><\/td>\nUnderstand<\/span><\/p>\n

Analyze<\/span><\/td>\n<\/tr>\n

ML-409P.3<\/span><\/td>\nApply neural networks and create different models for problem solving.<\/span><\/td>\nApply<\/span><\/p>\n

Create<\/span><\/td>\n<\/tr>\n

ML-409P.4<\/span><\/td>\nAble to Analyze images and evaluate the applications of NLP in deep learning.<\/span><\/td>\nAnalyze<\/span><\/p>\n

Evaluate<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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CO-PO-PSO Mapping <\/strong><\/span><\/p>\n\n\n\n\n\n\n\n
CO<\/strong><\/span><\/td>\nPO1<\/strong><\/span><\/td>\nPO2<\/strong><\/span><\/td>\nPO3<\/strong><\/span><\/td>\nPO4<\/strong><\/span><\/td>\nPO5<\/strong><\/span><\/td>\nPO6<\/strong><\/span><\/td>\nPO7<\/strong><\/span><\/td>\nPO8<\/strong><\/span><\/td>\nPO9<\/strong><\/span><\/td>\nPO10<\/strong><\/span><\/td>\nPO11<\/strong><\/span><\/td>\nPO12<\/strong><\/span><\/td>\nPSO1<\/strong><\/span><\/td>\nPSO2<\/strong><\/span><\/td>\n<\/tr>\n
ML-409P.1<\/span><\/td>\n3<\/span><\/td>\n2<\/span><\/td>\n3<\/span><\/td>\n3<\/span><\/td>\n3<\/span><\/td>\n2<\/span><\/td>\n2<\/span><\/td>\n–<\/span><\/td>\n–<\/span><\/td>\n–<\/span><\/td>\n–<\/span><\/td>\n2<\/span><\/td>\n1<\/span><\/td>\n1<\/span><\/td>\n<\/tr>\n
ML-409P.2<\/span><\/td>\n3<\/span><\/td>\n2<\/span><\/td>\n3<\/span><\/td>\n3<\/span><\/td>\n3<\/span><\/td>\n2<\/span><\/td>\n2<\/span><\/td>\n–<\/span><\/td>\n–<\/span><\/td>\n–<\/span><\/td>\n–<\/span><\/td>\n2<\/span><\/td>\n1<\/span><\/td>\n1<\/span><\/td>\n<\/tr>\n
ML-409P.3<\/span><\/td>\n3<\/span><\/td>\n2<\/span><\/td>\n3<\/span><\/td>\n3<\/span><\/td>\n3<\/span><\/td>\n2<\/span><\/td>\n2<\/span><\/td>\n–<\/span><\/td>\n–<\/span><\/td>\n–<\/span><\/td>\n–<\/span><\/td>\n2<\/span><\/td>\n1<\/span><\/td>\n1<\/span><\/td>\n<\/tr>\n
ML-409P.4<\/span><\/td>\n3<\/span><\/td>\n3<\/span><\/td>\n3<\/span><\/td>\n3<\/span><\/td>\n3<\/span><\/td>\n2<\/span><\/td>\n2<\/span><\/td>\n–<\/span><\/td>\n–<\/span><\/td>\n–<\/span><\/td>\n–<\/span><\/td>\n2<\/span><\/td>\n1<\/span><\/td>\n1<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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Facilities<\/strong><\/span><\/p>\n\n\n\n\n\n\n
Operating System \/Software <\/strong><\/span><\/td>\n<\/tr>\n
Sr. No.<\/span><\/td>\nName<\/strong><\/span><\/td>\nVersion<\/strong><\/span><\/td>\n<\/tr>\n
1.<\/span><\/td>\nWindows<\/span><\/td>\n10 PRO<\/span><\/td>\n<\/tr>\n
2.<\/span><\/td>\nAnaconda (Open Source)<\/span><\/td>\n5.3<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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Hardware <\/strong><\/span><\/td>\n<\/tr>\n
Sr. No.<\/strong><\/span><\/td>\nEquipment Name<\/strong><\/span><\/td>\nSpecification<\/strong><\/span><\/td>\nQuantity<\/strong><\/span><\/td>\n<\/tr>\n
1.\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0<\/span><\/td>\nComputer<\/span><\/td>\nIntel Core i9 Processor, 13th<\/sup> Generation, 32GB RAM<\/span><\/td>\n24<\/span><\/td>\n<\/tr>\n
2.\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0<\/span><\/td>\nPrinter<\/span><\/td>\nHP LASER Jet M1005MFP<\/span><\/td>\n01<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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Staff<\/strong><\/span><\/p>\n

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