Advanced Neural Networks
Advanced Neural Networks is a intermediate-level, 4 Weeks online course by NSTC. Master key concepts and practical skills in Deep Learning through hands-on projects, real-world case studies, and expert mentorship.
- ModeOnline (e-LMS)
- LevelIntermediate
- Duration4 Weeks
- Fee₹5,499
| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Intermediate |
| Duration | 4 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹5499 / $82 |
| Tools | Python PyTorch TensorFlow Keras CUDA Jupyter Notebook Weights & Biases |
About the Advanced Neural Networks Course
This program covers advanced neural network architectures such as Residual Networks (ResNets), DenseNets, and Transformers.
Participants will gain hands-on experience in building, optimizing, and fine-tuning these architectures for various AI applications, focusing on improving model accuracy, generalization, and performance.
Program Highlights
• Comprehensive coverage of Advanced Neural Networks from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Deep Learning
• 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 Deep Learning
• Career-oriented training for academic and professional growth in Deep Learning
Course Curriculum
Module 1: Introduction to Advanced Neural Networks
- Overview and historical evolution of Advanced Neural Networks
- Key terminology, definitions, and core concepts in Deep Learning
- 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 Advanced Neural Networks
- Mathematical and analytical frameworks relevant to Deep Learning
- Comparative analysis of major approaches and methodologies
- Understanding key standards, guidelines, and best practices
Module 3: CNNs
- Introduction to CNNs concepts and methodologies
- Step-by-step practical implementation of CNNs techniques
- Tools and platforms commonly used for CNNs
- Troubleshooting, optimization, and best practices
Module 4: RNNs
- Introduction to RNNs concepts and methodologies
- Step-by-step practical implementation of RNNs techniques
- Tools and platforms commonly used for RNNs
- Troubleshooting, optimization, and best practices
Module 5: Transformers
- Introduction to Transformers concepts and methodologies
- Step-by-step practical implementation of Transformers techniques
- Tools and platforms commonly used for Transformers
- Troubleshooting, optimization, and best practices
Module 6: Advanced Topics and Emerging Trends in Deep Learning
- Cutting-edge research and innovations in Advanced Neural Networks
- Integration with AI, automation, and modern technologies
- Industry case studies and real-world problem solving
- Future directions and career pathways in Deep Learning
Module 7: Capstone Project and Assessment
- End-to-end project implementation using Advanced Neural Networks skills
- Peer review, collaborative exercises, and expert feedback
- Portfolio-ready project documentation and presentation
- Final assessment and course completion evaluation
