Transformer for LLM Mastery: A Practical Guide for ML Engineers
A comprehensive course for ML engineers to master Transformer architecture, implementation, optimization, and deployment for building cutting-edge LLMs.
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Understanding the Transformer Architecture
Unit 1: Introduction to Transformers
Unit 2: Deep Dive into Attention Mechanisms
Unit 3: Encoder and Decoder Structures
Unit 4: Positional Encoding and Embeddings
Implementing Attention Mechanisms and Transformer Blocks
Unit 1: Self-Attention Implementation
Unit 2: Multi-Head Attention
Unit 3: Transformer Blocks
Unit 4: Putting it all Together
Fine-Tuning and Applying Pre-trained Transformer Models
Unit 1: Introduction to Fine-Tuning Transformers
Unit 2: Fine-Tuning for Text Classification
Unit 3: Fine-Tuning for Sequence-to-Sequence Tasks
