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Deep Learning MATLAB and Simulink Training Free Course with Certificate – Matlab Academy Mathworks

MATLAB and Simulink are powerful tools for deep learning, offering a comprehensive environment for developing and deploying machine learning models. Here’s a general outline for a training course on deep learning with MATLAB and Simulink:

Course Overview:

  1. Introduction to Deep Learning
    • Understanding deep learning concepts and its applications.
    • Overview of neural networks and their architectures.
  2. MATLAB Basics for Deep Learning
    • Introduction to MATLAB environment.
    • Basic operations, matrices, and arrays.
    • Plotting and visualization.
  3. Deep Learning Fundamentals in MATLAB
    • Introduction to Deep Learning Toolbox.
    • Building and training neural networks using MATLAB.
    • Preprocessing data for deep learning tasks.
  4. Advanced Neural Network Architectures
    • Convolutional Neural Networks (CNNs) for image classification.
    • Recurrent Neural Networks (RNNs) for sequence modeling.
    • Transfer learning and fine-tuning pre-trained models.
  5. Hands-on Projects
    • Image classification with CNNs.
    • Sentiment analysis with RNNs.
    • Object detection and tracking.
  6. Introduction to Simulink for Deep Learning
    • Overview of Simulink environment.
    • Building and simulating dynamic systems.
  7. Integration of MATLAB and Simulink
    • Importing MATLAB code into Simulink.
    • Designing control systems with deep learning components.
  8. Deployment and Optimization
    • Code generation for deploying models to embedded systems.
    • Optimizing deep learning models for performance.
    • Deployment considerations and best practices.
  9. Real-world Applications
    • Case studies from various industries (e.g., automotive, aerospace, healthcare).
  10. Project Work and Assessment
    • Participants work on real-world projects.
    • Assessment of project outcomes and presentations.

Prerequisites:

  • Basic knowledge of MATLAB programming.
  • Familiarity with machine learning concepts.
  • Basic understanding of linear algebra and calculus.

Additional Resources:

  • MATLAB and Simulink documentation and tutorials.
  • Online forums and communities for support and additional learning materials.
  • Research papers and case studies showcasing deep learning applications in MATLAB and Simulink.

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