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attention.py

class MultiHeadAttention(nn.Module):

def __init__(self, d_model, n_heads):

super().__init__()

self.n_heads = n_heads

self.head_dim = d_model // n_heads

self.W_q = nn.Linear(d_model, d_model)

self.W_k = nn.Linear(d_model, d_model)

self.W_v = nn.Linear(d_model, d_model)

def forward(self, x):

Q = self.W_q(x).view(B, T, H, D)

K = self.W_k(x).view(B, T, H, D)

attn = (Q @ K.T) / math.sqrt(D)

return self.out_proj(attn @ V)

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Curriculum

What You'll Learn

LLM Theory

Understand transformers, attention mechanisms, embeddings and the math behind modern language models.

TransformersAttentionEmbeddings

Hands-On Code

Build every component from scratch in Python — tokenizers, attention layers, transformer blocks and the full GPT-2 architecture.

PythonGPT-2Tokenizers

Training & Deployment

Pretrain on real datasets, implement temperature scaling, top-k sampling and load the full GPT-2 architecture.

PretrainingTop-kDeployment

Your journey

From Theory to Production

Foundations

Understand how LLMs work

Build

Code GPT-2 from scratch

Train

Pretrain on real data

Ship

Production-ready SaaS products

Download deployment-ready products

Complete at least 80% of a course to unlock the NeuralForge LLM or GenAI SaaS products as deployment-ready ZIP downloads.

NeuralForge LLM5 GenAI SaaSCertificates

Course Map

All course stages

Getting Started

Stage 0 · 1 Lectures

LLM Fundamentals

Stage 1 · 6 Lectures

Tokenization & Data

Stage 2 · 7 Lectures

Attention Mechanism

Stage 3 · 7 Lectures

LLM Architecture

Stage 4 · 8 Lectures

Pretraining

Stage 5 · 9 Lectures

Fine-tuning

Stage 6 · 11 Lectures

GenAI SaaS Products

Stage 7 · 6 Lectures

NeuralForge Platform

Stage 8 · 8 Lectures

Advanced Topics

Stage 9 · 4 Lectures

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Stages 0 & 1 are free forever. No account required.

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