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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
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.
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
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
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
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
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
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
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
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
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
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

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Build applications using Large Language Models Bootcamp

  • 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