| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Advanced |
| Duration | 3 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹59 |
| Tools | Autoencoders Backpropagation Convolutional Neural Networks Deep Learning Dropout Regularization Activation Functions Attention Mechanisms Transformers GANs Graph Neural Networks TensorFlow PyTorch |
About the Advanced Neural Networks Course
Advanced Neural Networks Course dives deep into Neural Networks. Gain comprehensive expertise through our structured curriculum and hands-on approach.
Program Highlights
• Mentorship by industry experts and NSTC faculty.
• Hands-on projects using Autoencoders, Backpropagation, Convolutional Neural Networks.
• Case studies on emerging artificial intelligence innovations and trends.
• e-Certification + e-Marksheet upon successful completion.
Course Curriculum
AI Fundamentals, Mathematics, and Neural Networks Foundations
- Implement Activation Functions with Attention Mechanisms for practical ai fundamentals, mathematics, and neural networks foundations applications and outcomes.
- Design Autoencoders with Backpropagation for practical ai fundamentals, mathematics, and neural networks foundations applications and outcomes.
- Analyze Convolutional Neural Networks (CNNs) with Deep Learning for practical ai fundamentals, mathematics, and neural networks foundations applications and outcomes.
Data Engineering, Preprocessing, and Feature Pipelines
- Implement Activation Functions with Attention Mechanisms for practical data engineering, preprocessing, and feature pipelines applications and outcomes.
- Design Autoencoders with Backpropagation for practical data engineering, preprocessing, and feature pipelines applications and outcomes.
- Analyze Convolutional Neural Networks (CNNs) with Deep Learning for practical data engineering, preprocessing, and feature pipelines applications and outcomes.
Model Architecture, Algorithm Design, and Neural Networks Methods
- Implement Activation Functions with Attention Mechanisms for practical model architecture, algorithm design, and neural networks methods applications and outcomes.
- Design Autoencoders with Backpropagation for practical model architecture, algorithm design, and neural networks methods applications and outcomes.
- Analyze Convolutional Neural Networks (CNNs) with Deep Learning for practical model architecture, algorithm design, and neural networks methods applications and outcomes.
Training, Hyperparameter Optimization, and Evaluation
- Implement Activation Functions with Attention Mechanisms for practical training, hyperparameter optimization, and evaluation applications and outcomes.
- Design Autoencoders with Backpropagation for practical training, hyperparameter optimization, and evaluation applications and outcomes. Gain hands-on experience and produce real-world projects.
- Analyze Convolutional Neural Networks (CNNs) with Deep Learning for practical training, hyperparameter optimization, and evaluation applications and outcomes.
Deployment, MLOps, and Production Workflows
- Implement Activation Functions with Attention Mechanisms for practical deployment, mlops, and production workflows applications and outcomes.
- Design Autoencoders with Backpropagation for practical deployment, mlops, and production workflows applications and outcomes. Gain hands-on experience and produce real-world projects.
- Analyze Convolutional Neural Networks (CNNs) with Deep Learning for practical deployment, mlops, and production workflows applications and outcomes.
Ethics, Bias Mitigation, and Responsible AI Practices
- Implement Activation Functions with Attention Mechanisms for practical ethics, bias mitigation, and responsible ai practices applications and outcomes.
- Design Autoencoders with Backpropagation for practical ethics, bias mitigation, and responsible ai practices applications and outcomes.
- Analyze Convolutional Neural Networks (CNNs) with Deep Learning for practical ethics, bias mitigation, and responsible ai practices applications and outcomes.
Industry Integration, Business Applications, and Case Studies
- Implement Activation Functions with Attention Mechanisms for practical industry integration, business applications, and case studies applications and outcomes.
- Design Autoencoders with Backpropagation for practical industry integration, business applications, and case studies applications and outcomes.
- Analyze Convolutional Neural Networks (CNNs) with Deep Learning for practical industry integration, business applications, and case studies applications and outcomes.
Advanced Research, Emerging Trends, and Neural Networks Innovations
- Implement Activation Functions with Attention Mechanisms for practical advanced research, emerging trends, and neural networks innovations applications and outcomes.
- Design Autoencoders with Backpropagation for practical advanced research, emerging trends, and neural networks innovations applications and outcomes.
- Analyze Convolutional Neural Networks (CNNs) with Deep Learning for practical advanced research, emerging trends, and neural networks innovations applications and outcomes.
Capstone: End-to-End Neural Networks AI Solution
- Implement Activation Functions with Attention Mechanisms for practical capstone: end-to-end neural networks ai solution applications and outcomes.
- Design Autoencoders with Backpropagation for practical capstone: end-to-end neural networks ai solution applications and outcomes.
- Analyze Convolutional Neural Networks (CNNs) with Deep Learning for practical capstone: end-to-end neural networks ai solution applications and outcomes.
Tools, Techniques, or Platforms Covered
Autoencoders Backpropagation Convolutional Neural Networks Deep Learning Dropout Regularization Activation Functions Attention Mechanisms Transformers GANs Graph Neural Networks TensorFlow PyTorch
Who Should Attend & Prerequisites
- Designed for Professionals.
- Designed for Students.
- Working experience with artificial intelligence tools and prior coursework in related topics expected.
Certification
