Data preparation, design, simulation, and deployment for deep neural networks with MATLAB and Simulink Training by Tonex
Data preparation, design, simulation, and deployment for deep neural networks with MATLAB and Simulink is a comprehensive training course that equips professionals with the essential knowledge and hands-on skills required to prepare, design, simulate, and deploy deep neural networks using MATLAB and Simulink. Deep learning has revolutionized numerous industries, and this course empowers participants to harness its potential by mastering the critical stages of deep neural network development.
By the end of this course, participants will have the skills and knowledge necessary to confidently prepare, design, simulate, and deploy deep neural networks using MATLAB and Simulink, making them valuable contributors to the rapidly evolving field of deep learning.
Learning Objectives: Upon completion of this course, participants will be able to:
- Learn the fundamentals of deep learning and its applications in various domains.
- Effectively prepare and preprocess data for deep neural network training.
- Design and configure deep neural network architectures for specific tasks.
- Simulate and fine-tune deep neural networks using MATLAB and Simulink.
- Deploy trained models in real-world applications and environments.
- Troubleshoot common issues and optimize deep neural network performance.
Audience: This course is designed for:
- Data Scientists and Analysts
- Machine Learning Engineers
- Software Developers
- Researchers in AI and Deep Learning
- Engineering Professionals
- Anyone seeking to leverage deep learning for data-driven solutions
Course Outline:
Introduction to Deep Learning
- Understanding Deep Neural Networks
- Deep Learning Applications
- MATLAB and Simulink for Deep Learning
Data Preparation for Deep Learning
- Data Collection and Acquisition
- Data Cleaning and Preprocessing
- Data Augmentation Techniques
- Handling Imbalanced Data
- Data Validation and Splitting
- Data Annotation and Labeling
Designing Deep Neural Networks
- Neural Network Architectures
- Hyperparameter Tuning
- Transfer Learning
- Custom Network Design
- Model Selection and Evaluation
- Optimization Techniques
Simulation and Training
- Setting Up the Deep Learning Environment
- Training Deep Neural Networks
- Monitoring Training Progress
- Handling Overfitting
- Model Interpretability and Visualization
- Advanced Training Techniques
Model Deployment
- Exporting Models from MATLAB/Simulink
- Deployment Platforms and Frameworks
- Real-time Inference
- Cloud-Based Deployment
- Edge Deployment for IoT Applications
- Model Versioning and Management
Troubleshooting and Optimization
- Debugging Deep Neural Networks
- Performance Profiling
- Fine-tuning for Efficiency
- Handling Large Datasets
- Adapting to Changing Data
- Security and Privacy Considerations