LLM Development from Scratch (Part 3: Lessons 21–30)
From multi-head attention to the training loop — Part 3, Lessons 21–30.
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Curriculum
Sections, lesson durations, and free previews
LLM Development from Scratch (Part 3: Lessons 21–30)
- 1Lesson 21: Masking, Truncation and Preventing Structural Errors20 minAvailable after enrollment
- 2Lesson 22: Coding Multi-Head Attention with PyTorch17 minAvailable after enrollment
- 3Lesson 23: Layer Normalization Logic and PyTorch Implementation22 minAvailable after enrollment
- 4Lesson 24: MLP and GeLU Activation in the Transformer37 minAvailable after enrollment
- 5Lesson 25: Building a Decoder Block with MLP and Residual Connections30 minAvailable after enrollment
- 6Lesson 26: Word Prediction with the LM Head24 minAvailable after enrollment
- 7Lesson 27: Understanding Logits and the Loss Function22 minAvailable after enrollment
- 8Lesson 28: Understanding Loss, Optimizer and BackpropagationAvailable after enrollment
- 9Lesson 29: Setting Up Dataset, Tokenizer and Training Loop37 minAvailable after enrollment
- 10Lesson 30: Saving and Loading Models with PyTorch (torch.save & load_state_dict)8 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