LLM Development from Scratch (Part 2: Lessons 11–20)
From positional encoding to self-attention — Part 2 of LLM development, Lessons 11–20.
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Curriculum
Sections, lesson durations, and free previews
LLM Development from Scratch (Part 2: Lessons 11–20)
- 1Lesson 11: Simple Positional Embedding Implementation27 minAvailable after enrollment
- 2Lesson 12: Sinusoidal Positional Encoding Implementation24 minAvailable after enrollment
- 3Lesson 13: Rotary Position Encoding (RoPE) Implementation26 minAvailable after enrollment
- 4Lesson 14: Building the Foundation of the Model14 minAvailable after enrollment
- 5Lesson 15: Understanding Context with Simple Self-Attention25 minAvailable after enrollment
- 6Lesson 16: Computing Semantic Similarity with Manhattan Distance16 minAvailable after enrollment
- 7Lesson 17: Computing Semantic Similarity with Cosine Similarity30 minAvailable after enrollment
- 8Lesson 18: Computing Attention Scores: QKV and Softmax21 minAvailable after enrollment
- 9Lesson 19: Coding a Self-Attention Layer from Scratch33 minAvailable after enrollment
- 10Lesson 20: Causal Self-Attention and Dropout Implementation23 minAvailable after enrollment
🔍 What you will find in this course:
✅ Core concepts such as tokenisation, embeddings and attention
✅ Line-by-line code explanations
✅ Jupyter Notebooks runnable in Google Colab
✅ 3D visualisations and animated explanations
✅ Training a small GPT model and building its interface
✅ Quizzes, assignments and interactive learning
✅ Open-source files and community support
🧠 Who is this for?
• Students developing skills in artificial intelligence
• Engineers, statisticians and researchers
• Anyone who wants to understand LLMs in theory and practice
Interested in this course?
Click the button below to enroll and start learning.
Secure checkout · PayTR · Lifetime access