Building Neural Network Expertise in Taiwan
We started in a cramped workspace back in 2018 with three researchers who couldn't stop arguing about activation functions. That constant debate became our strength. These days, we help professionals understand neural architectures without drowning in academic jargon.
How We Actually Got Here
The whole thing began when Linnea Östberg was working on computer vision projects and realized most available training focused on theory but skipped the messy implementation details. You know, the stuff that actually breaks when you deploy a model.
She teamed up with Damjan Vukovic, who'd been consulting for companies struggling to understand why their neural networks weren't performing. They kept seeing the same gaps in knowledge. People would build models following tutorials but couldn't diagnose problems or adapt architectures to their specific needs.
Instead of creating another "intro to deep learning" course, they mapped out what working professionals actually needed to know. The curriculum evolved from real consulting cases and debugging sessions. Every module came from questions they'd answered dozens of times in actual projects.
What Drives Our Teaching
Practical Over Perfect
We teach architectures that work in production environments with real constraints. Theory matters, but only when it helps you make better decisions about model design and optimization strategies.
Debug First, Optimize Later
Most courses skip the debugging phase entirely. We spend significant time on identifying why networks fail, reading loss curves, and understanding what different training behaviors actually mean for your architecture.
Context-Specific Solutions
There's no universal neural network architecture. We focus on helping you understand trade-offs so you can adapt designs to your specific data, computational budget, and deployment environment.
Who You'll Learn From
Linnea Östberg
Architecture Design LeadLinnea spent eight years implementing computer vision systems for manufacturing quality control. She's debugged more convolutional architectures than most people have trained. Her teaching focuses on why certain architectural choices work better for specific problem types.
Damjan Vukovic
Optimization SpecialistBefore teaching, Damjan consulted for companies trying to deploy models on resource-constrained hardware. He's obsessive about understanding performance bottlenecks and knows practical optimization techniques that actually make a difference in real applications.
Our Teaching Approach
Architecture Walkthroughs
We dissect successful neural network designs, explaining each component's purpose and the reasoning behind structural choices. You'll understand not just what layers to use, but why certain configurations work better for different tasks.
Implementation Practice
Each concept includes hands-on coding where you build architectures from scratch. We work through common mistakes and debugging processes so you develop practical troubleshooting skills alongside theoretical knowledge.
Real Case Analysis
We analyze actual projects where neural networks succeeded or failed, examining architectural decisions and their consequences. You'll learn to recognize patterns and make informed design choices for your own work.
Find Us in Hsinchu
Location
No. 347號, Section 2, Dongda Rd
North District, Hsinchu City
Taiwan 300
Next Program
Applications for March 2026 cohort open in January. We typically work with small groups to maintain quality and allow for individual attention during architecture design sessions.