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LLMs Mastery: Complete Guide to Transformers & Generative AI

Name: LLMs Mastery: Complete Guide to Transformers & Generative AI
Rating: 4.4 (9071 reviews)

Generative AI, r1, LLMs, ChatGPT, GPT4, o1, Llama3, Decoders, T5, BERT, LoRA, FSDP, 4bit, Machine Learning, Data Science

Created byThe Fuzzy Scientist | LLM Expert

Last updated 1/2026

English

What you'll learn

Grasp NLP Fundamentals: Understand the evolution from rule-based systems to advanced LLMs like Llama3, Gemma2, Phi3, and Mistral.
Master Transformers & LLMs: Learn the architecture and application of Transformers in depth. Including tokenization, embeddings, pre-training & fine-tunning.
Understand Generative AI Principles: Develop skills in building and fine-tuning generative models for real-world applications using RLHF and Chat-Templates
Use Transformer Models: Overview LLMs and Encoder-Decoder models like BERT, GPT, T5, Llama and more in many different NLP tasks: Personal assistant, Reviews, QA
Specialised Techniques: Implement 8-bit and 4-bit training, and use tools like DeepSpeed and FSDP, along with PeFT, LoRA, FlashAttention and more.

Course content

12 sections • 52 lectures • 7h 30m total length

Introduction to Part 1 of the Course: Transformers Fundamentals9:51
Introduction to Part 2 of The Course: LLMs6:44

Building Blocks4:30
Tokenizers14:24
Explore how tokenizers convert text into tokens for BERT-like models, including subword tokenization, token IDs, vocabularies, attention masks, and padding.
Understanding Tokenizers for Transformers
Word Embeddings11:02
Masked Language Modeling (MLM)6:04
Masked Language Modeling Quizz
Semantic Search Index9:07
Build a semantic search engine with transformer-based embeddings to retrieve relevant news articles from a multi news dataset using sentence transformers, cosine similarity, and top-k retrieval.

Decoding Large Language Models: An Introduction8:31
Introduction to Large Language Models (LLMs)
RLHF: Teaching LLMs to Communicate Effectively8:33
Understanding Input/Output in Language Models5:43
Chat Templates: Hands On Overview7:10
Explore how chat templates and tokenizers structure multi-turn conversations for LLMs, detailing user and assistant roles, message turns, and generation prompts across models like Blenderbot, Mistral, Gemma, and Llama three.
Decision Frameworks for LLM Selection7:20
Generation: An Interactive Guide10:22
Explore how to use Tiny Llama, load a tokenizer, apply a chat template, and control generation with max tokens, temperature, and top p within the 2048-token context—while noting potential hallucinations.

Comprehensive Dive into Sequence Length9:51
Dissect how sequence length governs fine tuning of large language models, from hardware and memory during training and inference to the impact of truncation and context.
Token Counts: Practical Intuition & Impact7:30
Precision Matters: Numerical Precision in Training13:32
Sequences and Tokens
Navigating GPU Selection: A Guide to Hardware Platforms6:00
Practice Fundamentals: Most Basic Form of Training LLMs4:43
Practice Fundamentals: Most Basic Form of Training LLMs - Part 28:35
Practice Fundamentals: Most Basic Form of Training LLMs - Part 35:22

Understanding Practical Limitations5:15
Boosting Efficiency: PeFT and LoRa in Depth13:37
Managing Data Memory: Batch Size & Sequence Length8:00
Learn how batch size and sequence length drive data memory usage in training large language models, and how to balance memory constraints, epochs, and gradient updates for efficient learning.
Advanced Solutions: Gradient Accumulation & Checkpointing13:33
Master gradient accumulation and gradient checkpointing to train with larger effective batch sizes while saving memory, by micro-batching, saving selective activations, and balancing training speed with accuracy.
Fitting Giants: Practical Introduction to LoRA for Large Models10:05
Expanding LoRA: Adapter Merging and Effective Evaluations8:04
Advanced Techniques

Requirements

Basic understanding of Python programming, some basic familiarity with machine learning concepts, and a strong eagerness to delve into LLMs and Natural Language Processing.

Description

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!

Who this course is for:

Those who wish to understand the new world of LLMs, how chatGPT, GPT4 and Llama work, and how to build their own powerful language models.

LLMs Mastery: Complete Guide to Transformers & Generative AI

What you'll learn

Explore related topics

Course content

1.1 Introduction: Course Overview + What You'll Learn2 lectures • 17min

1.2 Getting Started: How to Make the Best Use of this Course2 lectures • 7min

1.3 Overview of Natural Language Processing: Bring Transformers into Perspective4 lectures • 22min

1.4 Transformers Introduction: Important Concepts and Use-cases3 lectures • 37min

1.5 Popular Transformers Models: Choose the Best Model for the Job3 lectures • 33min

1.6 Using Transformers: Building Blocks and Hidden Gems (Practical)5 lectures • 45min

1.7 Mastering Real-World Scenarios with Transformers and LLMs (Practical)3 lectures • 44min

2.1 Introduction to Large Language Models6 lectures • 48min

2.2 Preparing for LLM Training (Practical)7 lectures • 56min

2.3 Advanced LLM Training Techniques (Practical)6 lectures • 59min

Requirements

Description

Who this course is for: