Advanced Neural Networks
Master State-of-the-Art Neural Network Architectures for Complex AI Solutions
Early access to e-LMS included
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.
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.
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
- 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
Who Should Enrol?
AI researchers, data scientists, and machine learning engineers focusing on advanced deep learning.
Program Outcomes
- Expertise in advanced neural network architectures and their applications.
- Proficiency in building, optimizing, and fine-tuning deep learning models.
- Ability to tackle complex real-world problems using cutting-edge AI techniques.
- Hands-on experience with TensorFlow or PyTorch for advanced model development.
Fee Structure
Discounted: ₹10,999 | $164
We accept 20+ global currencies. View list →
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- 1:1 project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
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