Building Intelligence from First Principles
We teach neural networks the way they actually work—starting with raw mathematics, moving through activation functions and backpropagation, then building up to production architectures that power real systems.
Explore Our Teaching Approach
Architecture That Makes Sense
Most courses throw you into TensorFlow or PyTorch on day one. That's backwards. You end up memorizing API calls without understanding what's happening underneath.
We start with NumPy. You'll code a single neuron by hand, calculate gradients yourself, and see why activation functions matter before you ever touch a framework. By the time we introduce high-level libraries, you'll know exactly what they're abstracting away.
This matters because debugging real models requires knowing where things go wrong. When your loss function explodes or your network won't converge, framework documentation won't save you. Understanding the actual math will.
How We Actually Teach This
Mathematics Without Fear
Linear algebra and calculus come up naturally as you need them. We don't frontload six weeks of theory. Instead, you learn partial derivatives when you're coding backpropagation, and matrix operations when you're building layers.
Real Code, Real Problems
Every concept gets implemented. You write training loops, debug vanishing gradients, and optimize hyperparameters. We use real datasets—not toy examples—so you see where theory breaks down in practice.
Architecture Decisions
CNNs for images, RNNs for sequences, Transformers for attention—but more importantly, why each architecture exists and when to use which. We cover the trade-offs that textbooks skip.
From Neurons to Production
Our curriculum moves through three distinct phases, each building on what came before. You're not jumping between disconnected topics—you're following the actual evolution of neural network design.
Most students coming from traditional CS backgrounds find the first phase challenging but rewarding. Those from mathematics backgrounds usually struggle more with the implementation details in phase two.
Foundation (12 weeks)
Perceptrons, feedforward networks, gradient descent mechanics. You'll build everything from scratch in Python, calculating weight updates by hand before automating them. Expect to spend time debugging shape mismatches and learning why initialization matters.
Modern Architectures (16 weeks)
CNNs, residual connections, batch normalization, attention mechanisms. We introduce PyTorch here and you'll train actual models on GPU. Projects include image classification, sequence prediction, and a Transformer implementation.
Practical Deployment (8 weeks)
Model optimization, quantization, serving infrastructure. You'll take a trained model and make it production-ready—handling edge cases, monitoring performance, and dealing with data drift. This is where research meets reality.
What Past Students Say
Ingrid Thorvaldsen
Computer Vision Engineer
I'd used Keras for two years but couldn't explain what my models were doing. Going back to basics here was humbling—and necessary. Now when something breaks, I know where to look instead of just tweaking hyperparameters randomly.
Siobhan Kavanagh
Research Scientist
The phase on Transformers was worth the tuition alone. We implemented attention from scratch, then optimized it, then compared it to production implementations. That progression made everything click in a way reading papers never did.
Program Investment
All programs include lab access, GPU compute credits, and ongoing support through our technical community. Cohorts starting March 2026.
Foundation Track
- Core neural network fundamentals
- NumPy implementations
- Gradient descent mastery
- Weekly code reviews
- Office hours access
Complete Program
- All foundation material
- Modern architectures (CNNs, Transformers)
- Production deployment skills
- Personal project mentorship
- Unlimited GPU compute
- Career guidance sessions
Advanced Track
- Assumes foundation knowledge
- Architecture deep-dives
- Research paper implementations
- Optimization techniques
- Small group sessions
Industry Connections
We work with AI research labs and product teams across Taiwan to keep our curriculum grounded in what's actually being built. Our partners contribute to curriculum design, guest lecture on emerging architectures, and sometimes hire directly from our cohorts.
These relationships help us stay current. When techniques like LoRA or quantization-aware training become industry standard, we hear about it quickly and adjust what we teach.
View Our Partners