Are there any good video lectures on computer vision?

Yes, there are several high-quality video lectures on computer vision tailored for developers and technical professionals. These resources cover foundational concepts, practical implementations, and advanced topics, making them valuable for both learning and reference. Many are freely available online or through educational platforms, and they often include hands-on examples using popular frameworks like PyTorch, TensorFlow, or OpenCV. Below are specific recommendations organized by type, along with details to help you choose based on your needs.

One of the most widely recommended video lecture series is Stanford University’s CS231n: Convolutional Neural Networks for Visual Recognition. This course, taught by Fei-Fei Li, Andrej Karpathy, and Justin Johnson, provides a thorough introduction to computer vision fundamentals. The lectures cover topics like image classification, object detection, segmentation, and neural network architectures such as CNNs, ResNets, and Transformers. The course materials are freely available on YouTube, and the accompanying assignments—which involve coding in Python with frameworks like PyTorch—are excellent for hands-on practice. For example, one assignment walks through implementing backpropagation for a CNN from scratch, which helps solidify understanding of how these models work under the hood. The lectures are well-structured, with clear explanations of mathematical concepts alongside practical implementation tips.

For developers looking for a more applied approach, Coursera’s Deep Learning Specialization by Andrew Ng includes a module dedicated to convolutional networks. While the entire specialization is broader than just computer vision, the CNN section focuses on real-world applications like medical image analysis and autonomous driving. Another platform, Udacity, offers a Computer Vision Nanodegree that combines video lectures with project-based learning. For instance, one project involves building an algorithm to track objects in a video stream using OpenCV. These courses are particularly useful for developers who want to quickly apply computer vision techniques to problems like facial recognition or augmented reality. Additionally, YouTube channels like “Two Minute Papers” or “Arxiv Insights” provide shorter, digestible summaries of recent research papers, which can help developers stay updated on advancements like vision transformers or self-supervised learning.

For those interested in advanced topics, conference tutorials and workshop recordings are invaluable. The CVPR (Conference on Computer Vision and Pattern Recognition) and ICCV (International Conference on Computer Vision) websites host free video recordings of tutorials and keynote talks. For example, a 2022 CVPR tutorial on “Efficient Deep Learning for Computer Vision” covered model compression and quantization techniques to optimize vision models for edge devices. Similarly, the PyImageSearch blog by Adrian Rosebrock offers free video tutorials paired with code examples, focusing on practical tasks like OCR (optical character recognition) or video processing with OpenCV. Finally, frameworks like TensorFlow and PyTorch have official YouTube channels with tutorials—such as PyTorch’s “Getting Started with TorchVision”—that demonstrate how to implement vision models step-by-step. These resources are ideal for developers who need actionable guidance for specific tools or tasks.