Program Objectives

• Master advanced neural network architectures like ResNets, DenseNets, and Transformers.
• Implement state-of-the-art neural networks for complex tasks.
• Optimize model performance using cutting-edge techniques.
• Explore real-world applications of advanced neural networks in AI-driven industries.
• Gain hands-on experience in building custom architectures for high-impact AI projects.

Program Structure

Module 1: Introduction and Review of Neural Networks

• Brief Review of Feedforward Neural Networks
• Activation Functions and Optimization Techniques

Module 2: Deep Learning Architectures and Regularization

• Advanced Regularization Techniques (Dropout, Batch Normalization)
• Advanced Network Architectures (Residual Networks, DenseNet)

Module 3: Convolutional Neural Networks (CNNs) and Architectures

• Deeper CNN Architectures (Inception, Xception)
• Advanced Topics in CNNs (Dilated Convolutions, Group Convolutions)

Module 4: Recurrent Neural Networks (RNNs) and Extensions

• Limitations of Vanilla RNNs
• Advanced RNN Models (LSTM, GRU)
• Attention Mechanisms in RNNs

Module 5: Transformers and Self-Attention

• Transformer Architecture
• Self-Attention Mechanisms
• BERT, GPT, and Other Transformer Variants

Module 6: Generative Models and Autoencoders

• Variational Autoencoders (VAE)
• Generative Adversarial Networks (GANs)
• Advanced GAN Architectures (CycleGAN, StyleGAN)

Module 7: Graph Neural Networks (GNNs)

• Introduction to Graph Neural Networks
• Graph Convolutional Networks (GCN)
• Applications of GNNs in NLP and Computer Vision

Module 8: Neural Architecture Search (NAS)

• Automated Neural Architecture Search Techniques
• Reinforcement Learning for NAS
• Evolutionary Algorithms for NAS

Module 9: Meta-Learning and Few-Shot Learning

• Meta-Learning Concepts
• Prototypical Networks, Matching Networks
• Applications in Few-Shot and Zero-Shot Learning

Module 10: Reinforcement Learning with Neural Networks

• Policy Gradient Methods
• Deep Q-Learning
• Actor-Critic Algorithms and Applications

Module 11: Ethics, Bias, and Interpretability in Neural Networks

• Model Interpretability and Explainability
• Bias in Deep Learning Models
• Ethical Considerations in Advanced Neural Network Deployments

Module 12: Final Project

• Students build an advanced neural network for a real-world problem (e.g., advanced GAN, Transformer for NLP)