Neural Network Architecture Program
A comprehensive deep learning program designed for those who want to understand how neural networks actually work. We focus on architecture design, training optimization, and real implementation challenges that you'll face when building production systems.
Building Knowledge Layer by Layer
Our program runs for eight months starting March 2026. You'll work through four distinct phases that progressively build your understanding from mathematical foundations to complex architectural patterns.
Mathematical Foundations
Linear algebra, calculus, and probability from a practical perspective. We skip the theory you won't use and focus on what actually matters when you're debugging gradient descent at 2 AM.
Core Architecture Patterns
CNNs, RNNs, transformers, and attention mechanisms. You'll implement each from scratch before using frameworks, so you understand what's happening under the hood when things inevitably break.
Training and Optimization
Regularization techniques, hyperparameter tuning, and debugging strategies. This is where we deal with overfitting, vanishing gradients, and all the fun problems that textbooks gloss over.
Production Deployment
Model optimization, serving infrastructure, and monitoring. Because a model that works on your laptop but can't handle real traffic isn't particularly useful to anyone.
Learn From Practitioners
Our instructors have built and deployed neural networks in production environments. They've made the mistakes so you don't have to.
Soren Lindqvist
Deep Learning ArchitectSpent five years building recommendation systems at a major streaming platform. Soren specializes in attention mechanisms and has strong opinions about batch normalization that he'll share whether you ask or not.
Katya Volkov
Research EngineerPreviously at a computer vision startup that got acquired. Katya focuses on CNN architectures and has a talent for explaining why your training loss suddenly exploded at epoch 47.
Dimitri Vasileios
Guest LecturerWorks on NLP systems for financial services. Dimitri joins us quarterly to talk about transformer architectures and the challenges of deploying models that need to be explainable to regulators.
Niamh Byrne
Teaching AssistantRecent graduate who went through this program two years ago. Niamh now works on autonomous vehicle perception systems and helps with code reviews and project guidance.
Hands-On Project Work
Theory only gets you so far. Most of your time will be spent writing code, training models, and figuring out why your validation accuracy dropped after you changed one line.
