📰 Dev.to · Gary Jackson

Pull everything into a GptModel class, package Adam as a reusable optimiser, and run the real 10,000-step training loop end-to-end.

Run several attention heads in parallel on embedding slices, add a two-layer MLP for per-position computation, and assemble a transformer block.

Build causal self-attention with Q/K/V projections, scaled dot-product scoring, softmax weights, and a KV cache for sequential processing.

Add two stabilisation patterns deep networks need: RMSNorm to keep activations bounded, and residual connections to give gradients a highway.

Assemble a full training loop: forward, loss, backward, and Adam parameter updates with momentum, adaptive scaling, and learning rate decay.

Give tokens and positions learned vector identities, assemble a minimal forward pass to logits, and compute cross-entropy loss.

Introduce Linear (matrix-vector multiply) and Softmax (logits to probabilities) - the two workhorse helpers used throughout the model.

Implement a counting-based bigram model to pin down the next-token prediction task and establish a loss baseline before neural networks enter.

Build a character-level tokenizer with a BOS delimiter that converts between characters and integer IDs.

Implement Backward() on Value: topologically sort the graph, then walk it in reverse using the chain rule to fill every .Grad.

Build a Value wrapper around double that records every operation, so the backward pass in the next chapter can compute gradients automatically.

Create the .NET console project, download the training data, and scaffold the dispatcher and source files the rest of the course expects.

Why this course exists and what you'll build - a progressive, from-scratch GPT tutorial in C#.