Deep Learning Fundamentals Course
Deep Learning Fundamentals Course is a Beginner-level, 3 Weeks online program by NSTC. Master Activation Functions, Backpropagation, Convolutional Neural Networks (CNNs) through hands-on projects, real datasets, and expert mentorship.
- ModeOnline (e-LMS)
- LevelIntermediate
- Duration6 Months
- Fee₹2,499
| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Intermediate |
| Duration | 6 Months |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹2499 / $59 |
| Tools | Python TensorFlow Keras PyTorch |
About the Deep Learning Fundamentals Course
Deep Learning Fundamentals Course dives deep into Deep Learning.
Gain comprehensive expertise through our structured curriculum and hands-on approach.
Program Highlights
• Comprehensive coverage of Deep Learning Fundamentals Course from fundamentals to advanced applications
• Hands-on projects and real-world case studies in AI
• 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
• Practical experience with tools: Python, TensorFlow, Keras, PyTorch
• Career-oriented training for academic and professional growth in AI
Course Curriculum
Module 1: AI Fundamentals, Mathematics, and Deep Learning Foundations
- Apply linear algebra concepts to optimize neural network performance
- Analyze probability distributions to understand deep learning model uncertainties
- Develop mathematical models to represent complex systems using differential equations
Module 2: Data Engineering, Preprocessing, and Feature Pipelines
- Design data pipelines to handle large-scale datasets using Apache Beam
- Configure data preprocessing workflows to handle missing values and outliers
- Implement feature engineering techniques to extract relevant information from raw data
Module 3: Model Architecture, Algorithm Design, and Deep Learning Methods
- Evaluate the performance of different deep learning architectures for image classification tasks
- Develop convolutional neural networks to solve computer vision problems
- Implement recurrent neural networks to model sequential data
Module 4: Training, Hyperparameter Optimization, and Evaluation
- Optimize hyperparameters using grid search and random search techniques
- Analyze model performance using metrics such as accuracy, precision, and recall
- Configure training workflows to handle overfitting and underfitting using regularization techniques
Module 5: Deployment, MLOps, and Production Workflows
- Deploy deep learning models using Docker and Kubernetes
- Design MLOps workflows to handle model updates and rollbacks
- Implement monitoring and logging tools to track model performance in production
Module 6: Ethics, Bias Mitigation, and Responsible AI Practices
- Analyze datasets for bias and develop strategies to mitigate it
- Develop fair and transparent AI systems using techniques such as data augmentation
- Evaluate the ethical implications of AI systems on society and individuals
Module 7: Industry Integration, Business Applications, and Case Studies
- Apply deep learning techniques to solve real-world business problems
- Develop business cases for AI adoption in various industries
- Evaluate the return on investment (ROI) of AI projects using cost-benefit analysis
