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.

What you will learn?

1. Introduction and Review of Neural Networks
   • Brief Review of Feedforward Neural Networks
   • Activation Functions and Optimization Techniques
2. Deep Learning Architectures and Regularization
   • Advanced Regularization Techniques (Dropout, Batch Normalization)
   • Advanced Network Architectures (Residual Networks, DenseNet)
3. Convolutional Neural Networks (CNNs) and Architectures
   • Deeper CNN Architectures (Inception, Xception)
   • Advanced Topics in CNNs (Dilated Convolutions, Group Convolutions)
4. Recurrent Neural Networks (RNNs) and Extensions
   • Limitations of Vanilla RNNs
   • Advanced RNN Models (LSTM, GRU)
   • Attention Mechanisms in RNNs
5. Transformers and Self-Attention
   • Transformer Architecture
   • Self-Attention Mechanisms
   • BERT, GPT, and Other Transformer Variants
6. Generative Models and Autoencoders
   • Variational Autoencoders (VAE)
   • Generative Adversarial Networks (GANs)
   • Advanced GAN Architectures (CycleGAN, StyleGAN)
7. Graph Neural Networks (GNNs)
   • Introduction to Graph Neural Networks
   • Graph Convolutional Networks (GCN)
   • Applications of GNNs in NLP and Computer Vision
8. Neural Architecture Search (NAS)
   • Automated Neural Architecture Search Techniques
   • Reinforcement Learning for NAS
   • Evolutionary Algorithms for NAS
9. Meta-Learning and Few-Shot Learning
   • Meta-Learning Concepts
   • Prototypical Networks, Matching Networks
   • Applications in Few-Shot and Zero-Shot Learning
10. Reinforcement Learning with Neural Networks
    • Policy Gradient Methods
    • Deep Q-Learning
    • Actor-Critic Algorithms and Applications
11. Ethics, Bias, and Interpretability in Neural Networks
    • Model Interpretability and Explainability
    • Bias in Deep Learning Models
    • Ethical Considerations in Advanced Neural Network Deployments