When does class start/end?
Class hours may vary, please reach out to contact@ascendientlearning.com if you have any questions.
Advanced Computer Vision
Build computer vision systems that combine classical methods with deep learning. Visual data and classical CV techniques cover formats, structured versus unstructured representation, smoothing,...
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$1,665USD
Duration
2 days
Course Code
DS-3202
Available Formats
Classroom
Overview
Course Description
Build computer vision systems that combine classical methods with deep learning. Visual data and classical CV techniques cover formats, structured versus unstructured representation, smoothing, filtering, edge detection, feature detectors including Harris, FAST, SIFT, and ORB, and tracking algorithms including Mean-Shift, CAMShift, and Optical Flow. Deep learning covers CNN architectures including AlexNet, VGG, and ResNet, object detection across R-CNN, YOLO, and SSD, image segmentation, and Vision Transformers. CV pipelines cover data acquisition, feature extraction, model selection, post-processing, and edge versus cloud deployment. Advanced applications cover generative vision through GANs and Diffusion, multimodal AI, real-time edge CV, 3D reconstruction, and explainable AI, along with future directions including self-supervised, few-shot, and meta-learning approaches. Hands-on labs produce trained CV models on real image datasets, with classical and deep techniques compared side-by-side. The course is designed for data scientists, analysts, and developers with Python and DS-2204 experience.
Skills Gained
By the end of this course, participants will be able to:
- Apply classical and deep learning techniques to computer vision problems
- Configure end-to-end computer vision pipelines
- Address ethical and practical challenges in visual data processing
- Recognize emerging trends including multimodal approaches in vision
Who Can Benefit
This course is designed for:
- Data Scientists & Analysts
- Software Developers
Prerequisites
Participants should enter this course with:
- Practical Python experience
- DS-2204 or equivalent
Organizational Objectives
This course assists organizations to:
- Reduce vision-model deployment cost through deliberate edge-versus-cloud choices
- Lower bias risk through ethical data-handling and explainable-AI practices
- Build a working advanced-CV capability across data-science and engineering teams
- Establish multimodal vision-language patterns that compound across projects
Software
All attendees must have a modern web browser and an Internet connection.
Course Details
Course Details
Module 1 - Understanding Visual Data
By the end of this module, you will be able to articulate visual-data types and formats, distinguish structured from unstructured visual data, recognize mathematical representations, apply visual data across industries, and reason about ethical implications and data bias.
- Enhanced communication through visualization
- Types and common formats of visual data
- Structured vs. unstructured visual data
- Mathematical representation & processing challenges
- Applications in industry (e.g., healthcare, security, autonomous systems)
- Ethical implications and data bias
- Hands-on Lab: Run feature extraction and image classification using a pre-trained CNN.
Module 2 - Classical Computer Vision Techniques
By the end of this module, you will be able to apply smoothing, filtering, and edge detection; use feature detectors (Harris, FAST, SIFT, ORB); apply tracking algorithms (Mean-Shift, CAMShift, Optical Flow); and combine classical with AI approaches at the edge.
- Rationale for classical CV and pre–deep learning methods
- Smoothing, filtering, and edge detection
- Feature detection & description (e.g., Harris, FAST, SIFT, ORB)
- Tracking algorithms (Mean-Shift, CAMShift, Optical Flow)
- Image segmentation and epipolar geometry
- Hybrid AI-classical approaches and edge AI
- Hands-on Lab: Run feature detection and matching alongside object tracking with optical flow.
Module 3 - Deep Learning for Computer Vision
By the end of this module, you will be able to articulate deep learning’s CV advantages, apply CNN architectures (AlexNet, VGG, ResNet), use object-detection models (R-CNN, YOLO, SSD), perform image segmentation, and apply Vision Transformers.
- Defining deep learning for CV and its advantages
- CNN architectures and popular models (AlexNet, VGG, ResNet)
- Object detection models (R-CNN, YOLO, SSD)
- Image segmentation techniques (semantic and instance)
- Vision Transformers, self-attention, and emerging trends
- Data, computational, and ethical considerations in deep CV
- Hands-on Lab: Run image classification with a pre-trained CNN and object detection with YOLOv5.
Module 4 - Building Computer Vision Pipelines
By the end of this module, you will be able to build a CV pipeline end-to-end — data acquisition, feature extraction, model selection and training, post-processing, and deployment across edge and cloud — and tune the pipeline for performance.
- Defining the CV pipeline and data acquisition strategies
- Feature extraction and model selection/training
- Post-processing and deployment strategies (edge vs. cloud)
- Challenges in optimizing vision pipelines
- Hands-on Lab: Build a preprocessing-and-feature-extraction pipeline and deploy a vision model to an edge device.
Module 5 - Advanced CV Applications
By the end of this module, you will be able to apply generative vision models (GANs, Diffusion), integrate multimodal AI, run real-time edge CV, visualize high-dimensional data, perform 3D reconstruction and depth estimation, and apply explainable AI (Grad-CAM, SHAP).
- Evolving use cases and generative models (GANs, Diffusion Models)
- Multimodal AI integrating vision and other modalities
- Real-time CV and edge AI applications
- Advanced visualization of high-dimensional data (e.g., t-SNE, UMAP)
- 3D reconstruction and depth estimation techniques
- Explainable AI methods (e.g., Grad-CAM, SHAP)
- Hands-on Lab: Visualize high-dimensional features in deep learning models and implement real-time object detection on an edge device.
Module 6 - Future Directions in Computer Vision
By the end of this module, you will be able to recognize the transition to AI-driven approaches in emerging sectors, apply self-supervised, few-shot, and meta-learning techniques, follow advances in vision transformers, and integrate vision-language and 3D-spatial understanding.
- Transition to AI?driven approaches in emerging sectors (automation, security, healthcare, creative AI)
- Self?supervised, few?shot, and meta?learning techniques
- Advances in vision transformers and efficient designs
- Cross?modal learning (vision-language integration)
- Innovations in 3D/spatial understanding (e.g., NeRF)
- Hands-on Lab: Explore explainability in vision models with Grad-CAM and implement real-time vision transformers for image classification.
Schedule
FAQ
Does the course schedule include a Lunchbreak?
Classes typically include a 1-hour lunch break around midday. However, the exact break times and duration can vary depending on the specific class. Your instructor will provide detailed information at the start of the course.
What languages are used to deliver training?
Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.
What does GTR stand for?
GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.
How do I find an Ascendient Learning training location?
We have training locations across the United States and Canada - View a complete list of classroom training locations.
Which delivery formats are available?
At Ascendient Learning, we offer training that is Instructor-Led, Online, Virtual, and Self-Paced.
Does Ascendient Learning deliver group training?
Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.
What does vendor-authorized training mean?
As a vendor-authorized training partner, we offer a curriculum that our partners have vetted. We use the same course materials and facilitate the same labs as our vendor-delivered training. These courses are considered the gold standard and, as such, are priced accordingly.
Is the training too basic, or will you go deep into technology?
It depends on your requirements, your role in your company, and your depth of knowledge. The good news about many of our learning paths, you can start from the fundamentals to highly specialized training.
How up-to-date are your courses and support materials?
We continuously work with our vendors to evaluate and refresh course material to reflect the latest training courses and best practices.
Are your instructors seasoned trainers who have deep knowledge of the training topic?
Ascendient Learning instructors have an average of 27 years of practical IT experience and have also served as consultants for an average of 15 years. To stay current, instructors spend at least 25 percent of their time learning new, emerging technologies and courses.
Do you provide hands-on training and exercises in an actual lab environment?
Lab access is dependent on the vendor and the type of training you sign up for. However, many of our top vendors will provide lab access to students to test and practice. The course description will specify lab access.
Will you customize the training for our company’s specific needs and goals?
We will work with you to identify training needs and areas of growth. We offer a variety of training methods, such as private group training, on-site of your choice, and virtually. We provide courses and certifications that are aligned with your business goals.
How do I get started with certification?
Getting started on a certification pathway depends on your goals and the vendor you choose to get certified in. Many vendors offer entry-level IT certification to advanced IT certification that can boost your career. To get access to certification vouchers and discounts, please contact info@ascendientlearning.com.
Will I get access to content after I complete a course?
You will get access to the PDF of course books and guides, but access to the recording and slides will depend on the vendor and type of training you receive.
How do I request a W9 for Ascendient Learning?
View our filing status and how to request a W9.
Reviews
ExitCertified gave me some good trainings and I got to learn through doing labs.
Great oppportunity for me to get training on the software during my work day.
Easy to use and exactly what I was looking for. Value for money was exceptional.
The labs and course material gave me valuable insights into cloud security architecture
Great training it covered the most importan topics if GitHub copilot with good explanation and good labs.
