Computer Vision With Deep Learning Training Course: Image Recognition & Detection

Introduction

Unlock the potential of visual data with our Computer Vision with Deep Learning Training Course. This program is designed to equip you with the essential skills to build image recognition and object detection systems, enabling you to create powerful applications that understand and interpret visual information. In today's AI-driven world, mastering computer vision is crucial for developing innovative solutions across various industries, from autonomous vehicles to medical imaging. Our computer vision training course offers hands-on experience and expert guidance, empowering you to implement state-of-the-art deep learning techniques.

This deep learning image recognition training delves into the core concepts of convolutional neural networks (CNNs), object detection algorithms, and image segmentation techniques. You'll gain expertise in using industry-standard libraries and tools to build image recognition and object detection systems, meeting the demands of modern computer vision projects. Whether you're a data scientist, AI developer, or computer vision engineer, this Computer Vision with Deep Learning course will empower you to build powerful visual AI systems.

Target Audience:

• Data Scientists
• AI Developers
• Computer Vision Engineers
• Machine Learning Engineers
• Researchers
• Software Developers
• Robotics Engineers
• Anyone needing deep learning computer vision skills

Course Objectives:

• Understand the fundamentals of computer vision with deep learning.
• Master convolutional neural networks (CNNs) for image classification.
• Utilize object detection algorithms (YOLO, SSD, Faster R-CNN).
• Implement image segmentation techniques (semantic, instance).
• Design and build computer vision systems for various applications.
• Optimize deep learning models for image processing tasks.
• Troubleshoot and address complex computer vision challenges.
• Implement model evaluation and validation techniques for CV.
• Integrate computer vision models into real-world systems.
• Understand how to preprocess and augment image data.
• Explore advanced computer vision techniques (e.g., generative models, 3D vision).
• Apply real world use cases for image recognition and object detection.
• Leverage deep learning libraries for efficient CV implementation.

Duration

10 Days

Course content

Module 1: Introduction to Computer Vision with Deep Learning

• Fundamentals of computer vision with deep learning.
• Overview of CNNs, object detection, and segmentation.
• Setting up a computer vision development environment.
• Introduction to deep learning libraries for CV.
• Best practices for computer vision.

Module 2: Convolutional Neural Networks (CNNs) for Image Classification

• Implementing CNN architectures for image classification.
• Utilizing transfer learning for image recognition.
• Designing and building image classification models.
• Optimizing CNNs for image processing tasks.
• Best practices for CNNs.

Module 3: Object Detection Algorithms

• Implementing YOLO for real-time object detection.
• Utilizing SSD and Faster R-CNN for accurate detection.
• Designing and building object detection systems.
• Optimizing detection models for various applications.
• Best practices for object detection.

Module 4: Image Segmentation Techniques

• Implementing semantic segmentation for pixel-level classification.
• Utilizing instance segmentation for object-level segmentation.
• Designing and building image segmentation models.
• Optimizing segmentation for specific applications.
• Best practices for image segmentation.

Module 5: Computer Vision System Design

• Designing computer vision systems for specific applications.
• Implementing model architectures for various CV tasks.
• Utilizing data augmentation for improved model performance.
• Optimizing model design for real-world scenarios.
• Best practices for system design.

Module 6: Model Optimization for Image Processing

• Optimizing deep learning models for image processing tasks.
• Utilizing hyperparameter tuning for CV models.
• Implementing model compression and acceleration.
• Designing scalable computer vision solutions.
• Best practices for model optimization.

Module 7: Troubleshooting Computer Vision Challenges

• Debugging complex computer vision issues.
• Analyzing model performance and errors.
• Utilizing troubleshooting techniques for model improvement.
• Resolving common computer vision challenges.
• Best practices for troubleshooting.

Module 8: Model Evaluation and Validation for CV

• Implementing evaluation metrics for computer vision tasks.
• Utilizing cross-validation techniques for CV models.
• Designing and building model validation pipelines.
• Optimizing model evaluation strategies.
• Best practices for model evaluation.

Module 9: Integration with Real-World Systems

• Integrating computer vision models into real-world applications.
• Utilizing APIs and deployment tools for CV.
• Implementing real-time computer vision systems.
• Optimizing models for deployment environments.
• Best practices for integration.

Module 10: Image Data Preprocessing and Augmentation

• Implementing image data preprocessing techniques.
• Utilizing data augmentation for model robustness.
• Designing and building data pipelines for CV.
• Optimizing data handling for model performance.
• Best practices for data handling.

Module 11: Advanced Computer Vision Techniques

• Implementing generative models for image synthesis.
• Utilizing 3D computer vision techniques.
• Designing and building advanced CV architectures.
• Optimizing advanced techniques for specific tasks.
• Best practices for advanced techniques.

Module 12: Real-World Use Cases

• Implementing computer vision for autonomous vehicles.
• Utilizing computer vision for medical image analysis.
• Implementing computer vision for industrial automation.
• Utilizing computer vision for surveillance and security.
• Best practices for real-world applications.

Module 13: Deep Learning Libraries Implementation

• Utilizing TensorFlow and PyTorch for computer vision tasks.
• Implementing computer vision models with Keras and OpenCV.
• Designing and building CV pipelines with libraries.
• Optimizing library usage for efficient implementation.
• Best practices for library implementation.

Module 14: Model Interpretability for Computer Vision

• Implementing model interpretability techniques for CV.
• Utilizing visualization tools for model understanding.
• Designing and building interpretable CV models.
• Optimizing model transparency.
• Best practices for model interpretability.

Module 15: Future Trends in Computer Vision

• Emerging trends in computer vision.
• Utilizing transformers for visual tasks.
• Implementing federated learning for distributed CV.
• Best practices for future computer vision.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.