How We Actually Teach Neural Networks
Most neural network courses throw theory at you and hope something sticks. We've been doing this since 2019, and we've learned that people understand architectures better when they build from scratch, break things on purpose, and fix their own mistakes.
Our approach isn't about memorizing formulas. It's about understanding why a convolutional layer works better than a fully connected one in certain scenarios. Why batch normalization stabilizes training. Why your gradient descent keeps overshooting the minimum.
We don't promise you'll build the next breakthrough model. But you'll know enough to read research papers, implement architectures from scratch, and most importantly—debug your own networks when they inevitably fail.
Build It Wrong First
Here's something we figured out after teaching hundreds of students: watching someone code a perfect LSTM doesn't teach you much. What really works is building a recurrent network that completely fails to learn anything meaningful.
So that's what we do. Week two, you'll implement a basic RNN for sequence prediction. It'll probably explode or vanish—that's the point. Then we walk through why it happened and how LSTM gates solve exactly that problem.
You retain way more when you've personally experienced the vanishing gradient problem than when someone just mentions it exists. Same goes for overfitting, mode collapse in GANs, or why your transformer needs positional encoding.
Real Problems, Real Solutions
Every architecture challenge in our curriculum comes from actual projects we've worked on. You'll see the same issues that trip up professional ML engineers—data leakage, training instability, poor generalization—and learn systematic ways to diagnose and fix them.
Three Core Approaches
Paper Implementation
Pick a recent architecture from ArXiv. Read the paper. Implement it from scratch using only the mathematical descriptions. No looking at reference code.
Sounds brutal, but it's how you really learn to read research. By month three, students can take a transformer variant paper and have a working implementation in a couple of days.
- Start with foundational papers like ResNet, attention mechanisms
- Progress to recent variants and modifications
- Learn to spot implementation details papers don't explicitly state
- Debug differences between your results and published benchmarks
Architecture Autopsy
Take a trained model that performs well. Systematically break parts of it. Remove layers. Change activation functions. Mess with the architecture.
Watch what degrades first. That tells you what's actually critical versus what's just tradition. You'd be surprised how often "standard practices" don't matter as much as people think.
- Remove normalization layers and observe training dynamics
- Replace skip connections and measure gradient flow
- Modify attention patterns in transformers
- Document which changes break everything versus minor impacts
Production Constraints
Academic papers rarely mention that your beautiful attention mechanism is too slow for production. Or that your model won't fit in available memory.
We give you real resource constraints—inference must run under 100ms, model size under 50MB—then you figure out how to maintain accuracy. Quantization, pruning, distillation. The stuff that actually matters when deploying.
- Profile your models to find computational bottlenecks
- Apply compression techniques without destroying performance
- Balance accuracy versus speed tradeoffs systematically
- Deploy models that actually work in production environments
Toivo Järvinen
I spent six years building computer vision systems for manufacturing defect detection before teaching. Dealt with everything from catastrophic overfitting to models that mysteriously stopped working in production.
That background shapes how I teach. I'm less interested in theoretical elegance and more focused on networks that actually work when you need them to. The math matters, but so does knowing when your validation set is leaking information or why your model performs differently on edge devices.
Started teaching at Glrintex in 2021 after realizing most courses skip the messy reality of making neural networks reliable. Now I mostly work with students who've already taken intro ML courses and want to understand architectures at a deeper level.
What I Actually Know About
Convolutional architectures for vision tasks, attention mechanisms and transformer variants, training stability and optimization, model compression and deployment. Also pretty good at explaining why your gradient descent isn't converging.
Learning Path Structures
| Component | Foundation Track | Architecture Track | Research Track |
|---|---|---|---|
| Duration | 4 months | 6 months | 9 months |
| Prerequisites | Python, basic calculus, linear algebra fundamentals | Completed foundation track or equivalent ML experience | Strong architecture understanding, prior implementation experience |
| Implementation Focus | Basic feedforward networks, simple CNNs, standard RNNs | ResNets, attention mechanisms, LSTMs, basic transformers | Recent architecture variants, custom designs, novel approaches |
| Paper Reading | Classic papers with detailed explanations and guided reading | Recent papers with implementation exercises | Bleeding-edge research with independent implementation |
| Debug Training | Identifying common training failures, basic troubleshooting | Advanced debugging, systematic diagnosis, optimization tuning | Experimental debugging, novel problem solving, research methods |
| Project Work | Guided projects with clear objectives and provided datasets | Semi-independent projects requiring architecture decisions | Original research questions with minimal guidance |
| Production Skills | Basic model saving, simple deployment scenarios | Quantization, pruning, optimization for inference | Advanced deployment, edge optimization, performance tuning |
| Weekly Time | 12-15 hours including lectures and implementation | 15-20 hours with significant coding projects | 20-25 hours including research reading and experiments |
Next Cohort Starts March 2026
We run small groups—usually around 15 students per cohort—because architecture work needs individual attention. You'll get stuck implementing attention mechanisms. Your gradients will explode. We need enough time to actually help you debug.
What Happens After You Apply
We'll send you a short technical assessment—nothing crazy, just making sure you're comfortable with Python and basic math. Then a quick conversation about what you want to learn and whether our approach fits. Whole process usually takes a week.
Classes meet twice weekly for live sessions, plus you'll have ongoing project work. Most students spend around 15-20 hours per week total. Foundation track is four months, architecture track is six, research track is nine.
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