Introduction

Understanding Large Language Models

Demystifying AI Terminology

Introduction to AI and LLMs

Overview of the AI Ecosystem

The Market Landscape

Key AI-Centric Startups

Platform Integrations

App Integrations

AI Ecosystem and Market Overview

Introduction to Developer Libraries and Frameworks

Understanding Datasets and Checkpoints

Overview of APIs and Vector Databases

Developer Tools and Frameworks

Autoregressive Decoding Explained

The Role of Vectors in LLMs

The Architecture of a Large Language Model

How LLMs Predict

Converting Words into Vectors

Transformer Architecture

Interacting with Word Embeddings

Embeddings and Transformations

What is Self-Attention?

Queries, Keys, and Values

Multi-Head Attention and Its Variants

Self-Attention Mechanism

Importance of Positional Encoding

Skip Connections and Their Benefits

Normalization Techniques in Neural Networks

Positional Encoding and Context

Multi-Query and Grouped-Query Attention

Transformer Diagrams and Flash Attention

Advanced Attention Mechanisms

Memory and Compute Bound Issues

Techniques for Making LLM Inference Faster

Quantization and Speculative Decoding

Optimizing LLM Inference

Creating Chatbots and Code Editors

Integrating LLMs with Existing Platforms

Future Trends in AI and LLM Development

Preparing for Machine Learning Interviews

Building Real-World Applications and Interview Prep

Practical Applications of LLMs and Interview Prep

In this bootcamp, we dive deep into Large Language Models (LLMs) to help you understand, build, and optimize their architecture for real-world applications. LLMs are transforming industries—from customer support to content creation—but understanding how these models function, and optimizing them for specific tasks, presents complex challenges.

Over an intensive, multi-week curriculum, we cover:

The technical foundations of LLMs, including autoregressive decoding, positional encoding, and multi-head attention
The LLM lifecycle—from large-scale pretraining to fine-tuning and instruction tuning for niche applications
Industry best practices for model evaluation, pinpointing performance bottlenecks, and employing cutting-edge architectures to balance efficiency and scalability.

This bootcamp includes hours of in-depth instruction, hands-on coding sessions, and access to a dedicated community for ongoing support and discussions. Additionally, you’ll have exclusive access to code templates, an expansive reference library, and downloadable resources for continuous learning.

Your guide through this bootcamp is [Alvin Wan](https://www.linkedin.com/in/alvinwan/), a Senior Research Scientist at Apple and a PhD student at UC Berkeley with global recognition for his work in efficient AI and model design. With his unique blend of industry experience and research expertise, Alvin will take you from foundational concepts to advanced applications, providing a solid grounding in the practical skills required to build, optimize, and evaluate LLMs.

Everyone’s heard of ChatGPT, but what truly powers these modern large language models? It all starts with the transformer architecture. This bootcamp demystifies LLMs, taking you from concept to code and giving you a full, hands-on understanding of how transformers work. You’ll gain intuitive insights into the core components—autoregressive decoding, multi-head attention, and more—while bridging theory, math, and code. By the end, you’ll be ready to understand, build, and optimize LLMs, with the skills to read research papers, evaluate models, and confidently tackle ML interviews.

AI Bootcamp

Understand and evaluate large language models

Importance of pretraining and fine-tuning in model performance

How to integrate AI models into real-world applications

Differences between training, fine-tuning, and evaluating models

What problems large language models can solve across industries

How to use developer tools like TensorFlow and PyTorch effectively

Strategies for reading and evaluating AI research papers

How to ace machine learning interviews with confidence

How to implement practical applications of AI, from chatbots to content generation

Differences between common AI ecosystems and platforms

Understanding the self-attention mechanism in transformers

Challenges in training large models and how to address them

What ethical considerations are crucial in AI development

How to build a portfolio of AI projects to showcase your skills

Emerging trends and future directions in AI research

---
title: Autoregressive Decoding Explained
---
