This document provides information about a bootcamp to build applications using Large Language Models (LLMs). The bootcamp consists of 11 modules covering topics such as introduction to generative AI, text analytics techniques, neural network models for natural language processing, transformer models, embedding retrieval, semantic search, prompt engineering, fine-tuning LLMs, orchestration frameworks, the LangChain application platform, and a final project to build a custom LLM application. The bootcamp will be held in various locations between September 2023 and January 2024.

1. Build applications using Large Language
Models
Large Language Models
Bootcamp
Tools and Technologies
Location
Seattle
Location
Washington D.C.
Date
Sept 18th to 22nd '23
Date
Oct 16th to 20th '23
Location
Austin
Location
New York
Location
Singapore
Date
Nov 6th to 10th '23
Date
Dec 4th to 8th '23
Date
Jan '24

2. Module
01
Introduction to Generative AI
Quick overview of generative AI, LLMs, and
foundation models. Learn more about how
transformers and attention mechanism works
behind text and image based models.

3. Evolution of Text Analytics Techniques
Review of classical text analytics techniques: encoding
(one-hot, count-based, TF-IDF), N-grams with Word2Vec.
Hands-on exercises to solidify understanding.
Module
02

4. Machine Learning Models for NLP
Overview of discriminative and generative approaches.
Logistic Regression, Naive Bayes, and Markov Chains.
Hands-on exercise using Naive Bayes for text
classification.
Module
03

5. LLM Fundamentals: Attention
Mechanism and Transformers
Dive into the world of Large Language Models,
discovering the potent mix of text embeddings, attention
mechanisms, and the game-changing transformer model
architecture.
Module
04

6. Efficient Storage and Retrieval of Vector
Embeddings Using Vector Databases
Learn about efficient vector storage and retrieval with
vector databases, indexing techniques, retrieval methods,
and hands-on exercises.
Module
05

7. Leveraging Text Embeddings for
Semantic Search
Understand how semantic search overcomes the
fundamental limitation in lexical search i.e. lack of
semantic. Learn how to use embeddings and
similarity in order to build a semantic search model.
Module
06

8. Fundamentals of Prompt Engineering
Unleash your creativity and efficiency with prompt
engineering. Seamlessly prompt models, control
outputs, and generate captivating content across
various domains and tasks.
Module
07

9. Customizing Foundation LLMs
Discover the ins and outs of fine-tuning foundation
language models (LLMs) through theory discussions,
exploring rationale, limitations, and Parameter Efficient
Fine Tuning.
08
Module

10. Orchestration Frameworks to Build
Applications on Enterprise Data
Explore the necessity of orchestration frameworks,
tackling issues like foundation model retraining, token
limits, data source connectivity, and boilerplate code.
Discover popular frameworks, their creators, and open-
source availability.
09
Module

11. LangChain for LLM Application Development
Build LLM Apps using LangChain. Learn about
LangChain's key components such as Models, Prompts,
Parsers, Memory, Chains, and Question-Answering. Get
hands-on evaluation experience.
10
Module

12. Project: Build A Custom LLM Application
On Your Own Data
Apply the concepts and techniques learned during
the bootcamp to build an LLM application.
11
Module