| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Beginner |
| Duration | 3 weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹4249 / $95 |
| Tools | PyTorch |
About the PyTorch Basics Course
PyTorch - Use in AI is an intensive course tailored for M.Tech, M.Sc, and MCA students, as well as E0 & E1 level professionals interested in mastering this powerful deep learning framework.
The course covers PyTorch fundamentals, neural network construction, model training, and real-world applications, preparing participants to tackle complex AI challenges in various industries.
Program Highlights
• Comprehensive coverage of PyTorch Basics from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Science & Technology
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Exposure to industry-standard tools and platforms used in Science & Technology
• Career-oriented training for academic and professional growth in Science & Technology
Course Curriculum
Module 1: Introduction to PyTorch Basics
- Overview and historical evolution of PyTorch Basics
- Key terminology, definitions, and core concepts in Science & Technology
- Current industry landscape, trends, and career opportunities
- Setting up the learning environment and essential tools
Module 2: Fundamentals and Theoretical Foundations
- Core principles and scientific/theoretical underpinnings of PyTorch Basics
- Mathematical and analytical frameworks relevant to Science & Technology
- Comparative analysis of major approaches and methodologies
- Understanding key standards, guidelines, and best practices
Module 3: Advanced Topics and Emerging Trends in Science & Technology
- Cutting-edge research and innovations in PyTorch Basics
- Integration with AI, automation, and modern technologies
- Industry case studies and real-world problem solving
- Future directions and career pathways in Science & Technology
Module 4: Capstone Project and Assessment
- End-to-end project implementation using PyTorch Basics skills
- Peer review, collaborative exercises, and expert feedback
- Portfolio-ready project documentation and presentation
- Final assessment and course completion evaluation
Tools, Techniques, or Platforms Covered
PyTorch
Real-World Applications
- Apply PyTorch Basics skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical Science & Technology competencies
- Solve industry-relevant problems using PyTorch Basics methodologies and tools
- Contribute to open-source projects and collaborative research in Science & Technology
- Prepare for competitive examinations, interviews, and professional certifications in Science & Technology
Who Should Attend & Prerequisites
- Students pursuing degrees in Science & Technology, science, engineering, or related disciplines
- Working professionals seeking to upskill or transition into Science & Technology roles
- Researchers and academicians looking to adopt modern techniques in Science & Technology
- Entrepreneurs, freelancers, and self-learners interested in practical Science & Technology knowledge
Certification
