Adex Krost

Send a question before you decide

There is no intake process, no callback queue. Write directly — a team member reads every message and responds within one business day. If you are thinking about enrolling in a neural networks course, want clarification on programme structure, or need to check technical requirements, this is the right place.

Write to us

All fields are required. We use your contact details only to respond to this enquiry.

Course budget range
I have read and accept the Privacy Policy. My data will be used solely to respond to this message and will not be shared with third parties.
6–8 hrs average weekly study load per module

Architecture shapes how you think, not just what you build

Neural network architecture is one of those topics where surface-level familiarity creates false confidence. A week spent studying transformer attention mechanisms versus convolutional pipelines produces fundamentally different intuitions. Adex Krost structures lectures around deliberate comparison — you see both paths, with the same dataset, before choosing which direction to specialise in.

Convolutional

Spatial hierarchy built through filters. Strong for image-structured data where locality matters. Computationally predictable.

Transformer

Attention across full sequence. No inductive spatial bias. Scales with data volume. Dominates language and multimodal tasks.

Recurrent

Sequential dependency preserved step by step. Memory bottlenecks constrain length. Mostly succeeded by attention-based alternatives.

Graph Neural

Operates on node-edge relationships. Suited for molecular, social and knowledge-graph structures. Growing research area.

All four architectures covered across the programme — no single paradigm presented as default