Deep Learning Specialization
Deep Learning Specialization is a advanced-level, 8 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)
- LevelAdvanced
- Duration8 Weeks
| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Advanced |
| Duration | 8 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹10749 / $145 |
| Tools | Python PyTorch TensorFlow Keras CUDA Jupyter Notebook Weights & Biases |
About the Deep Learning Specialization Course
This self-paced specialization provides an in-depth exploration of deep learning, covering theoretical foundations and practical implementations.
Participants will gain expertise in neural networks, convolutional networks, sequence models, and other advanced topics, preparing them for cutting-edge AI research and applications.
Program Highlights
• Comprehensive coverage of Deep Learning Specialization 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 Deep Learning
- Overview of Deep Learning Definition and Scope
- History and Evolution of Deep Learning Milestones and Key Figures
- Key Applications of Deep Learning Real-World Use Cases
- Basic Concepts and Terminology Fundamental Terms and Definitions
Module 2: Neural Networks and Deep Learning
- Introduction to Neural Networks Basic Structure and Function
- Perceptrons and Multilayer Perceptrons Single-Layer vs. Multi-Layer Perceptrons
- Activation Functions Common Activation Functions and Their Roles
- Training Neural Networks Process and Techniques
Module 3: Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization
- Hyperparameter Tuning Methods and Strategies
- Regularization Techniques L1 and L2 Regularization
- Dropout
- Data Augmentation
Module 4: Structuring Machine Learning Projects
- Project Workflow and Best Practices End-to-End Process
- Data Preparation and Preprocessing Techniques and Tools
- Training, Validation, and Test Sets Splitting and Management
- Model Selection and Evaluation Metrics Criteria and Methods
Module 5: Convolutional Neural Networks (CNNs)
- Introduction to CNNs Basic Concepts and Architecture
- Convolutional Layers Function and Implementation
- Pooling Layers Types and Applications
- Fully Connected Layers Role in CNNs
Module 6: Sequence Models
- Introduction to Sequence Models Overview and Applications
- Recurrent Neural Networks (RNNs) Basic Concepts and Uses
- Long Short-Term Memory (LSTM) Networks Structure and Function
- Gated Recurrent Units (GRUs) Comparison with LSTMs
Module 7: Advanced Topics in Deep Learning
- Generative Adversarial Networks (GANs) Concepts and Applications
- Autoencoders and Variational Autoencoders (VAEs) Theory and Use Cases
- Reinforcement Learning Basics and Applications
- Deep Reinforcement Learning Advanced Techniques
