Program Structure

Module 1: Introduction to Deep Learning

• Overview of Deep Learning
• Historical Context and Current Trends
• Applications of Deep Learning (e.g., NLP, CV, Autonomous Systems)

Module 2: Neural Networks Basics

• Neurons, Activation Functions
• Feedforward Networks
• Backpropagation and Gradient Descent

Module 3: Training Deep Neural Networks

• Loss Functions
• Optimizers (SGD, Adam, etc.)
• Overfitting and Regularization (Dropout, Batch Normalization)

Module 4: Convolutional Neural Networks (CNNs)

• Introduction to CNNs
• Convolution, Pooling Layers
• Architectures like AlexNet, VGG, ResNet

Module 5: Recurrent Neural Networks (RNNs)

• Sequence Modeling
• LSTM, GRU, and Attention Mechanisms
• Applications in NLP and Time Series

Module 6: Deep Learning Frameworks

• Introduction to TensorFlow and PyTorch
• Building Models in TensorFlow/PyTorch
• Customizing Layers and Loss Functions

Module 7: Autoencoders and Generative Models

• Introduction to Autoencoders
• Variational Autoencoders (VAE)
• Generative Adversarial Networks (GANs)

Module 8: Advanced Deep Learning Concepts

• Transfer Learning
• Reinforcement Learning Basics
• Transformers and BERT

Module 9: Model Deployment and Production

• Model Serving
• Model Optimization (Quantization, Pruning)
• Using Models in Real-World Applications (APIs, Cloud, Edge)

Module 10: Deep Learning Ethics and Fairness

• Bias in AI Models
• Ethical Considerations in AI
• AI for Social Good

Final Project

• Students build and deploy a deep learning model in a chosen domain (e.g., image classification, NLP task, etc.)

Participant’s Eligibility

• AI and data science researchers, machine learning engineers, and academicians looking to gain deep learning expertise.

Program Outcomes

• Mastery of fundamental deep learning concepts and architectures.
• Ability to build, train, and optimize deep learning models.
• Practical skills for real-world applications like image recognition and NLP.
• Proficiency in using deep learning frameworks like TensorFlow and PyTorch.

Program Deliverables

• Access to e-LMS
• Real-Time Project for Dissertation
• Project Guidance
• Paper Publication Opportunity
• Self Assessment
• Final Examination
• e-Certification
• e-Marksheet

Future Career Prospects

• Deep Learning Engineer
• AI Research Scientist
• Machine Learning Engineer
• Data Scientist
• Computer Vision Specialist
• NLP Engineer

Job Opportunities

• AI labs and research centers
• Tech companies using deep learning for product development
• Startups in AI-driven industries
• Data science departments in healthcare, finance, and e-commerce