Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
PyTorch – Use in AI is a practical, code-first path into the framework behind most modern deep-learning research and production. You will start with tensors and automatic differentiation, then build, train and debug your own neural networks — feed-forward, convolutional and recurrent — using PyTorch’s idiomatic patterns. The course covers the full real-world loop: datasets and dataloaders, training on GPUs, checkpointing, and transfer learning from pretrained models. By the end you can implement a paper-style model and train it on your own data with confidence. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course teaches deep learning with PyTorch end to end: tensors and autograd, building and training networks, and the workflow for real projects on GPUs.
📋 Course Objectives
1. Work fluently with PyTorch tensors and autograd.
2. Build feed-forward, convolutional and recurrent networks.
3. Write clean training loops with datasets and dataloaders.
4. Train on GPUs, checkpoint, and apply transfer learning.
5. Debug and profile models for correctness and speed.
👥 Who Should Enroll?
• Developers and ML practitioners adopting PyTorch
• Students moving from theory to hands-on deep learning
• Researchers implementing custom architectures
• Engineers preparing models for production
🚀 Key Learning Outcomes
• The ability to implement and train a model from a paper in PyTorch.
• A trained deep-learning model on your own dataset.
• A reusable PyTorch project scaffold.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and PyTorch Foundations
Develop a deep understanding of the mathematical prerequisites for PyTorch, including linear algebra and calculus • Analyze the fundamentals of AI, including machine learning and deep learning concepts, and their applications in real-world scenarios • Configure a PyTorch environment and implement basic PyTorch operations, including tensor manipulation and automatic differentiation
Data Engineering, Preprocessing, and Feature Pipelines
Design and implement data pipelines using PyTorch's DataLoader and Dataset classes, including data loading, preprocessing, and feature engineering • Evaluate the quality of datasets and implement data augmentation techniques to improve model performance and robustness • Optimize data processing workflows using PyTorch's distributed computing capabilities and parallel processing techniques
Model Architecture, Algorithm Design, and PyTorch Methods
Implement popular deep learning architectures, including convolutional neural networks, recurrent neural networks, and transformers, using PyTorch's nn.Module and nn.Sequential classes • Analyze and compare the performance of different model architectures and algorithms, including their strengths, weaknesses, and applications • Develop and train custom PyTorch models using PyTorch's autograd system and optimization algorithms, including stochastic gradient descent and Adam
Training, Hyperparameter Optimization, and Evaluation
Configure and train PyTorch models using various optimization algorithms and hyperparameter tuning techniques, including grid search, random search, and Bayesian optimization • Evaluate the performance of trained models using metrics such as accuracy, precision, recall, and F1-score, and implement techniques to improve model performance and robustness • Implement early stopping and learning rate scheduling techniques to prevent overfitting and improve model convergence
Deployment, MLOps, and Production Workflows
Deploy trained PyTorch models in production environments using PyTorch's JIT compiler and ONNX export, and implement model serving and inference pipelines • Design and implement MLOps workflows using tools such as PyTorch's TensorBoard and Weights & Biases, including model monitoring, logging, and versioning • Configure and manage production-ready PyTorch environments using containerization tools such as Docker and Kubernetes
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze and identify potential biases in AI systems and datasets, and implement techniques to mitigate bias and ensure fairness and transparency • Develop and implement responsible AI practices, including data privacy, security, and explainability, and ensure compliance with regulatory requirements • Evaluate the social and environmental impact of AI systems and implement strategies to promote AI for social good and sustainability
Industry Integration, Business Applications, and Case Studies
Implement PyTorch solutions for real-world industry applications, including computer vision, natural language processing, and recommender systems • Analyze and evaluate the business value and ROI of AI solutions, and develop strategies to integrate AI into existing business workflows and processes • Develop and present case studies of successful AI deployments, including their challenges, opportunities, and lessons learned
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Docker |
| Covered Tool / Platform | Kubernetes |
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