Advanced Neural Networks

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Mentor Based

Program Syllabus

Module 1

About This Course

This program covers advanced neural network architectures such as Residual Networks (ResNets), DenseNets, and Transformers. Participants will gain hands-on experience in building, optimizing, and fine-tuning these architectures for various AI applications, focusing on improving model accuracy, generalization, and performance.

Module 2

Aim

To equip PhD scholars, researchers, and AI professionals with advanced knowledge of neural network architectures, their applications, and optimization techniques. This course dives into state-of-the-art networks, enabling participants to apply deep learning models in complex real-world scenarios such as image recognition, NLP, and data analysis.

Module 3

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.

Module 4

Program Structure

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

Lead Instructor

Dr. Sarah Chen

PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.