datatec.studio — AI fundamentals, concepts, and practical guidance
Build intuition for how modern AI systems work: core ideas and vocabulary, how they connect to real usage, and where to go deeper. The Research section walks the flagship Transformer paper in twelve grounded topics; Foundations defines terms with hover glosses; Udemy hosts optional video courses when you want a guided path.
What to open first
- Research / Transformer Discover — twelve topic guides on Attention Is All You Need, with optional arXiv PDF
- Foundations — one page per technical term with hover tooltips from the essays
- Udemy — optional structured video courses on AI and full-stack development
AI fundamentals, concepts, and real-world use—explained clearly
Fundamentals · Concepts · Usage
Transformer roadmap
Twelve Transformer topics
Below: twelve grounded notes on one flagship paper—vocabulary and mechanics you reuse across NLP and generative AI. Open any card for the full in-site essay (same order as the Research hub). The hub adds longer landing copy, sidebar topics, the arXiv PDF rail, and share controls.
Transformer Discover hub →Topic 1
Language as tensors & order
How did sequence-to-sequence MT set up the Transformer problem?
Encoder–decoder frames map source sentences to latent memory decoders consume while generating targets.
Open deep dive →Topic 2
Language as tensors & order
Why do models still begin with token + position vectors?
Unicode normalisation, byte-pair encoding, and sentence-piece models determine which atomic units get ids.
Open deep dive →Topic 3
Language as tensors & order
Why was word order historically hard?
Bag-of-words destroys syntax: permutations become identical inputs unless you augment features.
Open deep dive →Topic 4
Recurrence, depth, and convolutions
Why did gated RNNs precede Transformers?
Backprop through time unfolds the graph T steps; Jacobian spectra multiply.
Open deep dive →Topic 5
Recurrence, depth, and convolutions
Convolutions stacked depth for context—what was missing?
Convolutional filters see k nearby tokens unless you deepen the network or dilate kernels.
Open deep dive →Topic 6
Attention machinery
Attention as differentiable, sparse-ish information retrieval
Alignment scores decide how strongly each encoder position participates in updating the decoder context.
Open deep dive →Topic 7
Attention machinery
Q, K, V: organising matmul-friendly attention
Queries index; keys advertise content addresses; values carry payloads mixed by weights.
Open deep dive →Topic 8
Attention machinery
Why replicate attention in parallel instead of widening one head?
Attention heads specialise on syntax, lexical repeat, positional bias, pronoun linkage—empirically not guaranteed but frequently observed.
Open deep dive →Topic 9
Full encoder–decoder shell
Adding order without resurrecting recurrence
Additive encodings hinge on broadcasting across sequence positions with distinct frequency bands.
Open deep dive →Topic 10
Full encoder–decoder shell
Three attention flavours in one stack diagram
Encoder self-attention attends left-right freely over source tokens (subject to padding masks).
Open deep dive →Topic 11
From the paper forward
BERT, GPT, T5… same atoms, swapped training recipes
Encoder-only Transformer stacks discard decoder but keep bidirectional self-attention for Cloze-like denoising (BERT lineage).
Open deep dive →Topic 12
From the paper forward
Costs, hybrids, multimodal workloads
Attention matrix materialisation dominates memory—not just flops—motivating block-sparse kernels and FlashAttention tiling.
Open deep dive →