Deep Learning For Computer Vision: Ai-powered Image & Video Analysis

Introduction:

Deep learning has revolutionized computer vision, enabling unprecedented accuracy in image and video analysis. This course on Deep Learning for Computer Vision equips participants with the specialized knowledge and skills to build and deploy AI models for visual data processing. Participants will learn how to utilize convolutional neural networks (CNNs), object detection techniques, and video analysis methods. This course bridges the gap between theoretical deep learning concepts and practical computer vision applications, empowering professionals to unlock the power of visual AI.

Target Audience:

This course is designed for professionals seeking to apply deep learning to computer vision tasks, including:

• AI/ML Engineers
• Data Scientists
• Computer Vision Developers
• Image Processing Professionals
• Security Analysts
• Anyone interested in building AI-powered visual applications

Course Objectives:

Upon completion of this Deep Learning for Computer Vision course, participants will be able to:

• Understand the fundamentals of deep learning and computer vision.
• Build and train convolutional neural networks (CNNs) for image classification.
• Implement object detection and segmentation techniques.
• Analyze video data using deep learning models.
• Utilize transfer learning and pre-trained models.
• Understand the challenges and limitations of deep learning for computer vision.
• Apply deep learning for image recognition, object tracking, and video analytics.
• Develop strategies for deploying computer vision models in real-world applications.
• Understand the ethical considerations of using computer vision.
• Enhance their ability to build AI-powered visual applications.
• Improve their skills in analyzing and processing image and video data.
• Contribute to improved visual data analysis and automation within their organization.
• Stay up-to-date with the latest trends and best practices in deep learning for computer vision.
• Become a more knowledgeable and effective computer vision professional.
• Understand ethical considerations in computer vision applications.
• Learn how to use deep learning frameworks and tools effectively for computer vision tasks.

DURATION

10 Days

COURSE CONTENT

Module 1: Introduction to Deep Learning and Computer Vision

• Understanding the basics of computer vision and its applications.
• Introduction to deep learning and its relevance to computer vision.
• Overview of convolutional neural networks (CNNs) and their architecture.
• Setting up the development environment (TensorFlow, PyTorch, etc.).
• Understanding image representation and processing.

Module 2: Fundamentals of Convolutional Neural Networks (CNNs)

• Understanding convolution operations and feature maps.
• Pooling layers and their role in feature extraction.
• Activation functions and their impact on network performance.
• Understanding different CNN architectures (e.g., LeNet, AlexNet, VGG).
• Building and training basic CNN models for image classification.

Module 3: Image Classification and Recognition

• Training CNNs for image classification tasks.
• Utilizing pre-trained models and transfer learning.
• Data augmentation techniques for improving model performance.
• Evaluating and visualizing CNN performance.
• Implementing image recognition applications.

Module 4: Object Detection Techniques

• Understanding object detection concepts and challenges.
• Implementing region-based convolutional neural networks (R-CNNs).
• Understanding and utilizing Faster R-CNN and YOLO models.
• Implementing single shot detectors (SSDs).
• Evaluating object detection performance (mAP).

Module 5: Image Segmentation

• Understanding semantic and instance segmentation.
• Implementing fully convolutional networks (FCNs).
• Utilizing U-Net architecture for image segmentation.
• Implementing Mask R-CNN for instance segmentation.
• Applications of image segmentation (e.g., medical imaging, autonomous driving).

Module 6: Transfer Learning and Pre-trained Models

• Understanding the benefits of transfer learning.
• Utilizing pre-trained models from popular architectures (e.g., ResNet, Inception).
• Fine-tuning pre-trained models for specific tasks.
• Feature extraction using pre-trained models.
• Adapting pre-trained models to new datasets.

Module 7: Video Analysis with Deep Learning

• Understanding video data representation and processing.
• Implementing recurrent neural networks (RNNs) for video analysis.
• Utilizing long short-term memory (LSTM) and gated recurrent units (GRUs).
• Implementing 3D CNNs for video classification and action recognition.
• Object tracking and motion analysis in videos.

Module 8: Generative Adversarial Networks (GANs) for Image Generation

• Understanding the concepts of GANs and their architecture.
• Implementing basic GANs for image generation.
• Exploring different GAN architectures (e.g., DCGAN, StyleGAN).
• Applications of GANs in image synthesis and manipulation.
• Evaluating and improving GAN performance.

Module 9: Image and Video Enhancement

• Implementing super-resolution techniques using deep learning.
• Image denoising and restoration using CNNs.
• Video stabilization and frame interpolation.
• Utilizing deep learning for image colorization and style transfer.
• Enhancing image quality for specific applications.

Module 10: Deep Learning for Medical Image Analysis

• Understanding the challenges of medical image analysis.
• Implementing deep learning for medical image segmentation and classification.
• Utilizing deep learning for disease detection and diagnosis.
• Ethical considerations in medical image analysis.
• Data privacy and security in medical imaging.

Module 11: Real-time Computer Vision Applications

• Optimizing deep learning models for real-time performance.
• Deploying computer vision models on edge devices.
• Utilizing hardware acceleration (GPUs, TPUs).
• Implementing real-time object detection and tracking.
• Building real-time video analytics systems.

Module 12: 3D Computer Vision

• Understanding 3D data representation (point clouds, depth maps).
• Implementing 3D CNNs for point cloud processing.
• Utilizing deep learning for 3D object recognition and reconstruction.
• Applications of 3D computer vision (e.g., robotics, autonomous driving).
• Understanding structure from motion.

Module 13: Advanced CNN Architectures and Techniques

• Exploring recent advancements in CNN architectures (e.g., EfficientNet, Vision Transformers).
• Understanding attention mechanisms and their applications.
• Implementing graph convolutional networks (GCNs) for image analysis.
• Utilizing knowledge distillation and model compression techniques.
• Understanding neural architecture search.

Module 14: Ethical Considerations and Bias in Computer Vision

• Understanding the ethical implications of computer vision applications.
• Addressing bias in datasets and models.
• Ensuring fairness and transparency in computer vision systems.
• Understanding data privacy and security in computer vision.
• Developing responsible computer vision practices.

Module 15: Deploying Computer Vision Models and Building Applications

• Deploying computer vision models in cloud and edge environments.
• Utilizing containerization and orchestration for model deployment.
• Building end-to-end computer vision applications.
• Monitoring and maintaining deployed models.
• Continuous learning and professional development in deep learning for 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 5 working days before commencement of the training.