Lab/Experiment(s) Hosting Request
Use this issue to get your lab and experiment repositories hosted on Virtual Labs. This issue also is used to host labs and their experiments that have been migrated to Phase 3 lab/exp format from Phase 2.
Please provide item 1 for all the experiments of the lab
- Hosting Unit:
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GitHub Handle: @ksrinivas-DEI
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Virtual Lab Phase: Ext 3
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Existing Hosted URL: NA
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Approved Proposal & Hosting Approval Mail: Dayalbagh Educational Institute Mail - Request for Hosting of the Deep Learning Virtual Lab,Approval mail.pdf
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Introduction: Deep Learning (DL) has emerged as one of the most influential areas of modern Artificial Intelligence, enabling major advances in computer vision, natural language processing, speech recognition, biomedical image analysis, autonomous systems, and intelligent decision-making. As deep learning is now widely included in undergraduate, postgraduate, and specialized AI curricula, there is a growing need for a structured laboratory platform that can support both conceptual understanding and practical experimentation.
The proposed Deep Learning Virtual Laboratory is designed to cover foundational as well as advanced topics commonly included in deep learning courses. The lab includes experiments on perceptrons, feedforward neural networks, activation functions and optimization, convolutional neural networks, transfer learning using pretrained deep CNNs, recurrent neural networks, LSTM-based sentiment analysis, autoencoders, generative adversarial networks, and vision transformers. This comprehensive coverage ensures that learners are gradually introduced to the building blocks of deep learning and then guided toward modern architectures used in real-world applications.
The platform is developed as a web-based, interactive environment that enables learners to explore deep neural network architectures, work with benchmark datasets, tune hyperparameters, and visualize training behavior through guided simulations. Each experiment follows a systematic workflow involving dataset preparation, model construction, training, parameter tuning, visualization, and performance evaluation. Visual outputs such as loss and accuracy curves, confusion matrices, feature maps, activation maps, reconstruction grids, and attention maps help students connect abstract theory with observable model behavior.
Overall, the Deep Learning Virtual Laboratory aims to provide a unified, scalable, and pedagogically sound framework for deep learning education. By offering browser-based access and reducing dependence on costly GPU infrastructure, the lab supports wider academic access, promotes standardized practical training, and strengthens the delivery of deep learning education across institutions.
- Objective: Primary Objectives of the Deep Learning Virtual Laboratory
- To provide a web-based interactive environment for learning foundational and advanced deep learning concepts.
- To enable hands-on experimentation with neural network models using benchmark and real-world datasets.
- To enhance conceptual understanding through visualization of model architecture, training dynamics, and intermediate outputs.
- To support guided learning through structured experiment workflows, theory capsules, quizzes, and instructional content.
- To develop analytical and problem-solving skills through practical implementation and interpretation of deep learning models.
- To facilitate understanding of complete deep learning workflows, including data preparation, model design, training, tuning, evaluation, and visualization.
- To make deep learning experimentation accessible to students without requiring specialized local GPU infrastructure or complex software installation.
- To expose learners to modern deep learning architectures such as CNNs, RNNs, LSTMs, Autoencoders, GANs, and Transformers in a progressive manner.
- Course Alignment: The Deep Learning Virtual Laboratory is aligned with AICTE and UGC model curricula for undergraduate and postgraduate programs in Computer Science, Electrical Engineering, Artificial Intelligence, Data Science, Information Technology, and allied engineering disciplines. The proposed experiments correspond directly to the deep learning laboratory components prescribed in standard Indian university syllabi and support practical learning outcomes related to neural networks, CNNs, sequence models, generative models, and transformer-based architectures. Representative institutions and academic frameworks offering similar laboratory components include (but are not limited to):
- GVP College of Engineering, Visakhapatnam
- Indian Institute of Technology Kharagpur, Kharagpur
- Indian Institute of Technology (Indian School of Mines), Dhanbad
- National Institute of Technology Tiruchirappalli, Tiruchirappalli
- National Institute of Technology Warangal, Warangal
- Jawaharlal Nehru Technological University Hyderabad, Hyderabad
- KG Reddy College of Engineering and Technology, Hyderabad
- MLR Institute of Technology, Hyderabad
- Malla Reddy College of Engineering and Technology, Hyderabad
- Indian Institutes of Information Technology and other AI/Data Science programs offering DL-oriented laboratory components
- AICTE Model Curriculum references for AI, Data Science, Machine Learning, Robotics, and Deep Learning courses
- Target Audience: The Deep Learning Virtual Laboratory is intended for:
- Undergraduate students pursuing engineering, science, AI, Data Science, and allied programs with deep learning components.
- Postgraduate students specializing in Artificial Intelligence, Machine Learning, Deep Learning, Data Science, and Computer Vision.
- Faculty members teaching deep learning, neural networks, computer vision, NLP, and AI-related laboratory courses.
- Beginner and intermediate learners seeking a structured and guided introduction to deep learning concepts and workflows.
- Students from institutions with limited access to GPU-enabled laboratories or advanced computing infrastructure.
- Researchers and project students who require a conceptual platform for experimenting with deep neural architectures before advanced implementation.
The platform is designed to accommodate learners with varying levels of prior knowledge. It begins with simple neural models and gradually progresses toward advanced architectures, thereby providing a continuous and learner-friendly experience.
- Discipline: The proposed virtual lab belongs to the discipline of Deep Learning, a specialized and rapidly advancing area within Artificial Intelligence and Machine Learning. It is inherently interdisciplinary because deep learning models are widely used for image analysis, sequence modeling, language understanding, biomedical diagnosis, engineering automation, prediction, generation, and intelligent decision support.
Given the broad applicability of deep learning, the virtual lab can be effectively utilized across multiple disciplines, including:
- Computer Science and Engineering
- Electrical and Electronics Engineering
- Artificial Intelligence and Data Science
- Information Technology
- Electronics and Communication Engineering
- Mathematics, Statistics, and Computational Sciences
- Mechanical, Industrial, and Robotics Engineering
- Biomedical Engineering and Healthcare Informatics
- Natural Language Processing and Computational Linguistics
- Agriculture, Environmental, and Remote Sensing Applications
- Economics, Finance, Commerce, and Management Studies
- Social Sciences and interdisciplinary AI-enabled research areas
- Name of the Lab: Deep Learning Virtual Laboratory [CSE-P44], DEI
Lab/Experiment(s) Hosting Request
Use this issue to get your lab and experiment repositories hosted on Virtual Labs. This issue also is used to host labs and their experiments that have been migrated to Phase 3 lab/exp format from Phase 2.
Please provide item 1 for all the experiments of the lab
GitHub Handle: @ksrinivas-DEI
Virtual Lab Phase: Ext 3
Existing Hosted URL: NA
Approved Proposal & Hosting Approval Mail: Dayalbagh Educational Institute Mail - Request for Hosting of the Deep Learning Virtual Lab,Approval mail.pdf
Introduction: Deep Learning (DL) has emerged as one of the most influential areas of modern Artificial Intelligence, enabling major advances in computer vision, natural language processing, speech recognition, biomedical image analysis, autonomous systems, and intelligent decision-making. As deep learning is now widely included in undergraduate, postgraduate, and specialized AI curricula, there is a growing need for a structured laboratory platform that can support both conceptual understanding and practical experimentation.
The proposed Deep Learning Virtual Laboratory is designed to cover foundational as well as advanced topics commonly included in deep learning courses. The lab includes experiments on perceptrons, feedforward neural networks, activation functions and optimization, convolutional neural networks, transfer learning using pretrained deep CNNs, recurrent neural networks, LSTM-based sentiment analysis, autoencoders, generative adversarial networks, and vision transformers. This comprehensive coverage ensures that learners are gradually introduced to the building blocks of deep learning and then guided toward modern architectures used in real-world applications.
The platform is developed as a web-based, interactive environment that enables learners to explore deep neural network architectures, work with benchmark datasets, tune hyperparameters, and visualize training behavior through guided simulations. Each experiment follows a systematic workflow involving dataset preparation, model construction, training, parameter tuning, visualization, and performance evaluation. Visual outputs such as loss and accuracy curves, confusion matrices, feature maps, activation maps, reconstruction grids, and attention maps help students connect abstract theory with observable model behavior.
Overall, the Deep Learning Virtual Laboratory aims to provide a unified, scalable, and pedagogically sound framework for deep learning education. By offering browser-based access and reducing dependence on costly GPU infrastructure, the lab supports wider academic access, promotes standardized practical training, and strengthens the delivery of deep learning education across institutions.
The platform is designed to accommodate learners with varying levels of prior knowledge. It begins with simple neural models and gradually progresses toward advanced architectures, thereby providing a continuous and learner-friendly experience.
Given the broad applicability of deep learning, the virtual lab can be effectively utilized across multiple disciplines, including: