Advanced Neural Networks

Master State-of-the-Art Neural Network Architectures for Complex AI Solutions

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MODE
Online/ e-LMS

TYPE
Self Paced

LEVEL
Moderate

DURATION
4 Weeks

About

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

Participant’s Eligibility

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

Standard Fee: INR ~~5,998~~ USD 90

Discounted Fee: INR 2,999 USD 45

We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!

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Key Takeaways

Program Assessment

Certification to this program will be based on the evaluation of following assignment (s)/ examinations:

Exam	Weightage
Mid Term Assignments	50 %
Project Report Submission (Includes Mandatory Paper Publication)	50 %

To study the printed/online course material, submit and clear, the mid term assignments, project work/research study (in completion of project work/research study, a final report must be submitted) and the online examination, you are allotted a 1-month period. You will be awarded a certificate, only after successful completion/ and clearance of all the aforesaid assignment(s) and examinations.