Microsoft Learn
FreeLLMs Mastery: Complete Guide to Transformers & Generative AI
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The course sweeps from NLP history to modern LLM training. It moves through encoder and decoder designs and the BERT, GPT-2 and T5 model families, then into the efficiency methods that reduce memory and compute during training.
This is the section that gives the course its practical edge. Parameter-efficient fine-tuning with LoRA, 8-bit and 4-bit training, and scaling tools such as DeepSpeed and FSDP are covered together, so you see how they fit into one training workflow.
Expect a mix of concept and code, with the concepts doing more of the work at the start.
Welcome to "LLMs Mastery: Complete Guide to Generative AI & Transformers"!
This practical course is designed to equip you with the knowledge and skills to build efficient, production-ready Large Language Models using cutting-edge technologies.
Key Topics Covered:
Generative AI: Understand the principles and applications of Generative AI in creating new data instances.
ChatGPT & GPT4: Dive into the workings of advanced AI models like ChatGPT and GPT4.
LLMs: Start with the basics of LLMs, learning how they decode, process inputs and outputs, and how they are taught to communicate effectively.
Encoder-Decoders: Master the concept of encoder-decoder models in the context of Transformers.
T5, GPT2, BERT: Get hands-on experience with popular Transformer models such as T5, GPT2, and BERT.
Machine Learning & Data: Understand the role of machine learning and data in training robust AI models.
Advanced Techniques: Sophisticated training strategies like PeFT, LoRa, managing data memory and merging adapters.
Specialised Skills: Cutting-edge training techniques, including 8-bit, 4-bit training and Flash-Attention.
Scalable Solutions: Master the use of advanced tools like DeepSpeed and FSDP to efficiently scale model training.
Course Benefits:
• Career Enhancement: Position yourself as a valuable asset in tech teams, capable of tackling significant AI challenges and projects.
• Practical Application: Learn by doing—build projects that demonstrate your ability to apply advanced LLM techniques in real-world situations.
• Innovative Approach: Stay at the forefront of AI technology by mastering techniques that are shaping the future of machine learning.
What You Will Learn:
Natural Language Processing Basics
• Journey Through NLP Evolution: From rule-based systems to advanced embeddings.
• Foundation in NLP: Set the stage for advanced learning in natural language processing.
Introduction to Transformers
• Transformer Architecture: Learn about encoders, decoders, and attention mechanisms.
• Model Strategies: Understand pre-training, fine-tuning, tokenization, and embeddings.
Popular Transformer Models
• Explore Key Models: Dive into BERT, GPT, and T5 and their unique capabilities.
• Deepen Model Insights: Uncover the potential and versatility of Transformer technology.
Using Transformers (Practical)
• Hands-On Experience: Apply Transformers in real-world scenarios.
• Advanced Techniques: Master tokenization, embeddings, and MLMs.
• Project Implementation: Build a Semantic Search Index.
NLP Tasks and Applications (Practical)
• Real-World Applications: Use BERT for question answering, GPT for personal assistants, and T5 for writing reviews.
• Practical NLP Skills: Experience the direct application of NLP tasks.
Foundations of Large Language Models
• Introduction to LLMs: Understand basic architecture and functionalities.
• Communication Techniques: Enhance model responsiveness with RLHF.
• Input/Output Processes: Explore how LLMs handle data for AI interactions.
Advanced Configuration and Optimization
• Chat Template Design: Practical experience in structuring LLM interactions.
• Model Selection Frameworks: Strategic decision-making for choosing LLMs.
• Generation Techniques: Tailor LLM outputs through interactive learning.
Specialized Training Techniques
• Advanced Model Training: Focus on sequence length, token counts, and numerical precision.
• Efficiency Methods: Learn 8-bit and 4-bit training to adapt models to constraints.
• Scaling Tools: Implement DeepSpeed and FSDP for efficient model scaling.
Practical Applications of LLMs
• Application in Contexts: Apply LLM skills in simulated real-world projects.
• Task-Specific Training: Optimize models for specific tasks like memory management and efficiency.
Who This Course Is For:
Tech Professionals: Enhance your skills and knowledge in cutting-edge AI technologies.
Aspiring AI Practitioners: Get a comprehensive education in LLMs from basic principles to advanced applications.
Researchers and Students: Gain a deep understanding of the latest developments and how they can be applied to solve complex problems.
Ready to dive into the world of Generative AI and Transformers?
Enroll today and start your journey to mastery!
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