Practical Computer Vision with PyTorch

Course
  • Big Data and AI
  • Data Science

Practical Computer Vision with PyTorch

Practical Computer Vision in PyTorch is a comprehensive, hands-on course for developers and practitioners eager to explore computer vision with PyTorch. It spans image classification, object detection, segmentation, and generative modeling. Emphasizing implementation, participants work through coding demos and projects with industry-standard tools and libraries. By the end, they will be able to build and fine-tune computer-vision models for real-world applications.

Offered by Antonio Rueda-Toicen | Last updated on Nov 24, 2025 | 2843 enrollments

Enroll now
  • Intermediate
  • EN
  • Certificate
Course information

Computer-vision technologies are transforming industries, driving innovation in healthcare, automotive, retail, and media. Effectively applying these techniques demands deep practical knowledge. This course offers a comprehensive introduction to modern computer-vision methods with PyTorch, beginning with convolutional neural networks (CNNs), moving to advanced architectures such as Vision Transformers (ViT), and exploring cutting-edge vision-language models like CLIP and Grounding DINO.
Beyond technical implementation, participants will learn best practices for evaluating and fine-tuning models, ensuring proficiency in every stage of development.

Course Structure
Module 1: Foundations of Deep Learning for Vision
  • CNNs, optimization, metrics, and embeddings
  • Code demos: Zero-shot inference, visualization of CNN operations, data augmentation
Module 2: Advanced Techniques
  • Vision transformers, object detection, segmentation
  • Vision-language models and generative modeling techniques (CLIP, diffusion models)
  • Practical coding and advanced implementation demos
Scope

The Practical Computer Vision with PyTorch course runs for two weeks with a total workload of approximately 8-10 hours. It includes video lectures and interactive coding demonstrations, each accompanied by multiple-choice assessments.

All learning materials (videos, coding demonstrations, self-assessments) are available at the course start. The Module-1 Homework will be released along with the course material, and the Module-2 Homework will be released at the end of the first week, giving learners two weeks to complete the course and submit their solutions.

Prerequisites
  • Intermediate AI/ML understanding
  • Proficiency in Python (writing classes/functions)

Insert image description

Learning goals
  • Fundamental concepts of computer vision and applications
  • Building/training neural networks (CNNs, transformers)
  • Loss functions and optimization techniques
  • Performance evaluation metrics
  • Transfer learning and feature extraction techniques
  • Data augmentation and dataset curation methods
  • Specialized architectures: Vision Transformers, Mask R-CNN, etc.
  • Vision-language models and generative modeling techniques (CLIP, diffusion models)
  • Experiment tracking with Weights and Biases (wandb)
  • Image dataset curation with FiftyOne
Requirements for Proofs of Achievement
  • Confirmation of Participation: You will automatically receive a free Confirmation of Participation once you have completed at least 50% of the content.
  • Certificate: You will receive your official openHPI Certificate by earning at least 50% of the maximum number of points from all graded assignments.

Find out more on our page for Proofs of Achievement and Guidelines.

Our teachers

Antonio Rueda-Toicen

Antonio Rueda-Toicen helps companies and individuals use artificial intelligence. He has experience developing and deploying machine learning models both in industry and academia. Currently, he is a researcher in the Artificial Intelligence and Intelligent Systems group at the Hasso Plattner Institut. He also works as an AI Engineer at Voxel51, where he leads workshops on practical computer vision skills. Antonio is a certified instructor of deep learning and generative models at NVIDIA's Deep Learning Institute.

Since 2019, Antonio has organized the Berlin Computer Vision Group meetup. He has delivered workshops to over 1,000 participants both in person and online. He mentors students at Berlin's Data Science Retreat, helping them transition into industry roles. He enjoys teaching computer vision, MLOps, and neural networks. As an engineer at HPI's AI Service Center, he co-founded the AI Maker Community to support open collaboration.

Antonio is pursuing a PhD at HPI. His focus is on vision-language models and representation learning. He holds degrees in computer science and bioengineering from Universidad Central de Venezuela. Antonio is passionate about making complex technology accessible and useful for everyone.