Building Real Computer Vision Skills Since 2018
We started StreamPulse because the industry needed people who could actually write vision algorithms, not just import libraries and hope for the best.
Why We Exist
Back in 2018, Taiwan's tech sector faced a weird problem. Companies needed computer vision engineers, but most candidates could barely explain how a Gaussian blur worked. They'd memorized TensorFlow commands without understanding what happened underneath.
Our founder, Aldric Sørensen, spent years interviewing candidates who looked great on paper but couldn't debug a simple edge detection failure. That frustration led to StreamPulse — a place where people learn the actual mathematics and algorithms before touching any frameworks.
And yeah, that approach takes longer. But our graduates actually know what they're doing.
How We're Different
Most programs rush you into deep learning on day three. We don't. You'll spend weeks understanding image processing fundamentals, writing convolution kernels by hand, debugging pixel-level operations.
Is it harder? Absolutely. But when you eventually work with neural networks, you'll understand why certain architectures work and others don't. You'll be able to troubleshoot production issues that would leave other developers confused.
We focus on the Taiwan market specifically because local industries — manufacturing automation, agricultural monitoring, medical imaging — need engineers who understand both theory and practical constraints. Not everything needs a GPU cluster and cloud services.
Seven Years of Building Expertise
These numbers represent real people who completed challenging programs and now work in positions where they solve actual problems.
12 years in computer vision research and industrial implementation. Former senior engineer at two Taiwan-based automation companies.
Teaching What Actually Matters
I've been writing vision algorithms since 2013, mostly for manufacturing inspection systems and agricultural monitoring. Spent years building custom solutions before pre-trained models became the default answer for everything.
What bothered me most was watching companies hire people who couldn't explain basic concepts. Someone would claim expertise in object detection but couldn't describe how non-maximum suppression worked. Or they'd confidently talk about convolutional layers while having no idea why kernel size mattered.
StreamPulse exists because I got tired of that gap. We teach the fundamentals that make everything else make sense — Fourier transforms, morphological operations, feature extraction methods, camera calibration math. The stuff that sounds boring but becomes essential when you're debugging why your detection system fails in certain lighting conditions.
Our next cohort starts in September 2025. We're keeping it small — 24 students maximum — because this material requires personalized attention. You can't learn computer vision by watching videos and doing multiple-choice quizzes.
How We Structure Learning
Computer vision isn't something you pick up in a weekend bootcamp. Our approach emphasizes depth over breadth, understanding over memorization.
Foundation Mathematics
Linear algebra, calculus, and probability theory as they apply to image processing. You'll understand why certain operations work before implementing them. We don't skip the math — it's where real comprehension comes from.
Classical Algorithms First
Months spent on traditional computer vision techniques. Edge detection, corner detection, feature matching, camera geometry. These fundamentals explain what deep learning models actually do internally — most people never learn this part.
Modern Methods Context
Once you understand classical approaches, neural networks make sense. You'll build CNNs from scratch, implement backpropagation manually, and understand why certain architectures work better for specific problems. Framework usage comes last, not first.
Want to Learn Computer Vision Properly?
Our autumn 2025 program opens for applications in June. It's a 14-month commitment that demands significant time and effort. But if you're serious about building real expertise in computer vision, it's worth considering.
Explore Learning Program Details